diff --git a/doc/content/bib/tmap8.bib b/doc/content/bib/tmap8.bib index 60cbe210b..4bf7fc296 100644 --- a/doc/content/bib/tmap8.bib +++ b/doc/content/bib/tmap8.bib @@ -343,6 +343,46 @@ @article{zhao2020deuterium publisher={Elsevier} } +@article{ELMORSHEDY2024101616, +title = {Thermal-hydraulic simulation of ITER tungsten divertor monoblock for loss of flow transient}, +journal = {Nuclear Materials and Energy}, +volume = {38}, +pages = {101616}, +year = {2024}, +issn = {2352-1791}, +doi = {https://doi.org/10.1016/j.nme.2024.101616}, +url = {https://www.sciencedirect.com/science/article/pii/S2352179124000383}, +author = {Salah El-Din El-Morshedy} +} + +@article{Kessel01092013, +author = {C. E. Kessel and M. S. Tillack and J. P. Blanchard}, +title = {The Evaluation of the Heat Loading from Steady, Transient and Off-Normal Conditions in ARIES Power Plants}, +journal = {Fusion Science and Technology}, +volume = {64}, +number = {3}, +pages = {440--448}, +year = {2013}, +publisher = {American Nuclear Society}, +doi = {10.13182/FST12-538}, +URL = {https://doi.org/10.13182/FST12-538}, +eprint = {https://doi.org/10.13182/FST12-538} +} + +@article{SALTELLI2002280, +title = {Making best use of model evaluations to compute sensitivity indices}, +journal = {Computer Physics Communications}, +volume = {145}, +number = {2}, +pages = {280-297}, +year = {2002}, +issn = {0010-4655}, +doi = {https://doi.org/10.1016/S0010-4655(02)00280-1}, +url = {https://www.sciencedirect.com/science/article/pii/S0010465502002801}, +author = {Andrea Saltelli}, +keywords = {Sensitivity analysis, Sensitivity measures, Sensitivity indices, Importance measures} +} + @techreport{Matthews2021SWIFT, title={{Metal Hydride Simulations Using SWIFT}}, author={Matthews, C. and Shivprasad A. P. and Cooper M. W. D.}, @@ -390,3 +430,4 @@ @article{MAJER2002438 url = {https://www.sciencedirect.com/science/article/pii/S0925838801014529}, author = {G. Majer and J. Gottwald and D. T. Peterson and R. G. Barnes} } + diff --git a/doc/content/examples/divertor_monoblock/index.md b/doc/content/examples/divertor_monoblock/index.md index 9ea204a82..12f0ed648 100644 --- a/doc/content/examples/divertor_monoblock/index.md +++ b/doc/content/examples/divertor_monoblock/index.md @@ -290,6 +290,7 @@ their significance, the reader is referred to [!cite](Shimada2024114438). !alert warning title=The exodus file in `gold` is a smaller version of the output The input file [/divertor_monoblock.i] returns the outputs that were used in [!cite](Shimada2024114438). However, a slightly modified version of this input is run in [/divertor_monoblock/tests] as part of TMAP8's [Software Quality Assurance](sqa/index.md exact=True) process: It simulates only one pulse cycle, has a coarser mesh, and outputs the results less regularly to limit the file size. As a result, the exodus file in the test `gold` directory is a smaller version of the output generated when running the full input file. +A sensitivity study on material properties has [also been demonstrated](examples/divertor_monoblock/sensitivity.md exact=True). ## Complete input file diff --git a/doc/content/examples/divertor_monoblock/sensitivity.md b/doc/content/examples/divertor_monoblock/sensitivity.md new file mode 100644 index 000000000..bf163e8e3 --- /dev/null +++ b/doc/content/examples/divertor_monoblock/sensitivity.md @@ -0,0 +1,123 @@ +# Divertor Monoblock Sensitivities + +This work consists of sensitivity studies of different operating conditions performed on the [Divertor Monoblock](https://mooseframework.inl.gov/TMAP8/examples/divertor_monoblock/index.html). The original pulsed operation of the divertor monoblock model was modified according to [!cite](Hodille2021126003) towards a single-long tritium and heat flux pulse of an approximate equivalent total fluence. An initial sensitivity study on was performed on the divertor monoblock's boundary conditions under the steady pulse condition using the [Sobol Method](https://mooseframework.inl.gov/modules/stochastic_tools/examples/sobol.html) and other tools within the [Stochastic Tools Module](https://mooseframework.inl.gov/modules/stochastic_tools/). Two additional sensitivity studies were performed on select transients obtained from [!cite](ELMORSHEDY2024101616) and [!cite](Kessel01092013). + +## General description of the sensitivity studies and the modified cases + +### Introduction + +!style halign=left +Many fusion components, especially those exposed to especially unique or extreme conditions, often lack sufficient operation data to evaluate their reliability, availability, maintainability, and inspectability (RAMI). As a proof-of-concept, we seek to construct a Probabilistic Physics of Failure (PPoF) model of a tokomak divertor system to obtain failure rate metrics and lifetime metric relevant to the RAMI of a nuclear fusion reactor. + +To accomplish this, sensitivity studies on the [Divertor Monoblock](https://mooseframework.inl.gov/TMAP8/examples/divertor_monoblock/index.html) model described in [!cite](Shimada2024114438) will be performed for (1) steady state conditions based on ITER data [!cite](Hodille2021126003), (2) an inadvertant shutdown transient [!cite](ELMORSHEDY2024101616), and (3) an edge-localized mode transient [!cite](Kessel01092013). The sensitivity studies will be performed using the [MOOSE](https://mooseframework.inl.gov/) Stochastic Module and its Sobol Method according to [!cite](SALTELLI2002280). The results of the sensitivity studies will be evaluated for state limits associated with heat transfer and tritium migration physics included in the [Divertor Monoblock](https://mooseframework.inl.gov/TMAP8/examples/divertor_monoblock/index.html). + +First, the modifications made to the [Divertor Monoblock](https://mooseframework.inl.gov/TMAP8/examples/divertor_monoblock/index.html) model will be described. Second, the conditions the divertor monoblock was subjected to in the steady and transient scenarios will be described. Finally, the results of the sensitivities, namely the first and second order sensitivity indicies and example correlations and state frequencies will be presented. + +### Divertor Monoblock modifications + +The pulsed operation of the divertor is computationally expensive, so as a first approximation, we replace the pulsed operation with a single steady pulse. We also add several postprocessors. + +%Show added postprocessors + +### Steady operation + + +% Varied parameters: boundary conditions, + +!table id=tab:steady_case +caption=Steady case varied boundary conditions + +| Parameter | Samples | Nominal Value | Distribution | Deviation | +| --- | --- | --- | --- | --- | +| Incident Heat Flux | 100 | 10$^{7}$ MW/M$^2$ | Uniform | $\pm$5% | +| Incident Tritium Flux | 100 | 7.90$\times$10$^{-13}$ (normalized) | Uniform | $\pm$5% | +| Coolant Temperature | 100 | 552 K | Uniform | $\pm$5% | +| Coolant Tritium Concentration | 100 | 1.0$\times$10$^{-18}$ (normalized) | Uniform | $\pm$1% | + +% parameter space figure + +### Transient case: inadvertant shutdown + +!style halign=left + +% Transient characteristics (modification to BCs), varied parameters and their ranges +% Transient figure + +!table id=tab:inadvertant_shutdown_case +caption=Inadvertant shutdown varied parameter space. + +| Parameter | Samples | Nominal Value | Distribution | Deviation | +| --- | --- | --- | --- | --- | +| Peak Duration | 1000 | 1s | Normal | $\sigma$=$\pm$5% | +| Peak Heat Flux | 1000 | 2$\times$10$^{7}$ MW/M$^2$ | Normal | $\sigma$=$\pm$5% | +| Coolant Temperature | 1000 | 552 K | Normal | $\sigma$=$\pm$5% | +| Tungsten Conductivity Factor | 1000 | 1.0 | Uniform | $\sigma$=$\pm$5% | + +% Parameter space figure + +### Transient case: edge-localized mode disruption + +!style halign=left +% Transient characteristics (modification to BCs), varied parameters and their ranges +% transient figure +!table id=tab:elm_transient_case +caption=Edge-localized mode transient case parameter space. + +| Parameter | Samples | Nominal Value | Distribution | Deviation | +| --- | --- | --- | --- | --- | +| Peak Duration | 1000 | 1.32ms | Normal | $\sigma$=$\pm$5% | +| Peak Heat Flux | 1000 | 1147$\times$10$^{7}$ MW/M$^2$ | Normal | $\sigma$=$\pm$5% | +| Coolant Temperature | 1000 | 552 K | Normal | $\sigma$=$\pm$5% | +| Tungsten Conductivity Factor | 1000 | 1.0 | Uniform | $\sigma$=$\pm$5% | +% Paramete space figure +## Results + +### Steady operation + +% Sensitivity to inputs, pick two or three examples +% Correlation between incident heat flux and: W max temp, F_permeation + +### Inadvertant shutdown + +% Sensitivity to inputs, pick two or three examples +% Correlation between incident heat flux and: W max temp, F_permeation + +### Edge-localized mode disruption + +% Sensitivity to inputs, pick two or three examples +% Correlation between incident heat flux and: W max temp, F_permeation + +## Complete input files + +Below are the complete input files for the various sensitivity studies. Note that none of the inputs have been optimized for computational costs. + +### Steady operation + +#### Subapp input + +!listing test/tests/divertor_monoblock/steady_state_runner.i link=false + +#### Controller input + +!listing test/tests/divertor_monoblock/steady_state_sobol.i link=false + +### Inadvertent shutdown + +#### Subapp input + +!listing test/tests/divertor_monoblock/shutdown_transient_runner.i link=false + +#### Controller input + +!listing test/tests/divertor_monoblock/shutdown_transient_sobol.i link=false + +### Edge-localized mode disruption + +#### Subapp input + +!listing test/tests/divertor_monoblock/elm_transient_runner.i link=false + +#### Controller input + +!listing test/tests/divertor_monoblock/elm_transient_sobol.i link=false + diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/elm_transient_CCZ_state_frequency.png b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/elm_transient_CCZ_state_frequency.png new file mode 100644 index 000000000..d817072b7 Binary files /dev/null and b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/elm_transient_CCZ_state_frequency.png differ diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/elm_transient_W_sensitivity.png b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/elm_transient_W_sensitivity.png new file mode 100644 index 000000000..91df13ee1 Binary files /dev/null and b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/elm_transient_W_sensitivity.png differ diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/elm_transient_outputs.png b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/elm_transient_outputs.png new file mode 100644 index 000000000..8f52a8e6b Binary files /dev/null and b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/elm_transient_outputs.png differ diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/elm_transient_retention_sensitivity.png b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/elm_transient_retention_sensitivity.png new file mode 100644 index 000000000..752a7d42a Binary files /dev/null and b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/elm_transient_retention_sensitivity.png differ diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/shutdown_transient_CCZ_state_frequency.png b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/shutdown_transient_CCZ_state_frequency.png new file mode 100644 index 000000000..8e417e000 Binary files /dev/null and b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/shutdown_transient_CCZ_state_frequency.png differ diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/shutdown_transient_W_sensitivity.png b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/shutdown_transient_W_sensitivity.png new file mode 100644 index 000000000..15d0367f8 Binary files /dev/null and b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/shutdown_transient_W_sensitivity.png differ diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/shutdown_transient_outputs.png b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/shutdown_transient_outputs.png new file mode 100644 index 000000000..ad188d56a Binary files /dev/null and b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/shutdown_transient_outputs.png differ diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/shutdown_transient_retention_sensitivity.png b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/shutdown_transient_retention_sensitivity.png new file mode 100644 index 000000000..e0c670a25 Binary files /dev/null and b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/shutdown_transient_retention_sensitivity.png differ diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/sobol_processing.py b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/sobol_processing.py new file mode 100644 index 000000000..9da705eb3 --- /dev/null +++ b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/sobol_processing.py @@ -0,0 +1,560 @@ +# DETAILS: N. H. Snow Rev1 +# Good luck to anyone who will attempt to edit or use this file. + +from matplotlib import pyplot as plt; from matplotlib.colors import TABLEAU_COLORS as plt_colors +import pandas as pd; from pandas import DataFrame +import numpy as np; from numpy import ndarray +import json +from scipy import optimize; import scipy.stats as scipy +import seaborn + +plt.rcParams['font.family'] = 'Cambria' +plt.rcParams['font.size'] = 8 +colors = list(plt_colors.values()) + +def json_serial_killer(an_obj, **kwargs): + return_dict = {} + try: + if isinstance(an_obj, DataFrame): + raise(AttributeError) + else: + obj_attributes = an_obj.__dict__ + for k, v in obj_attributes.items(): + return_dict[k] = json_serial_killer(v) + return(return_dict) + except(AttributeError): + if isinstance(an_obj, dict): + obj_attributes = an_obj + for k, v in obj_attributes.items(): + return_dict[k] = json_serial_killer(v) + return(return_dict) + elif isinstance(an_obj, list): + return_list = [] + for item in an_obj: + return_list.append(json_serial_killer(item)) + return(return_list) + elif isinstance(an_obj, ndarray): + an_obj = an_obj.tolist() + return_list = [] + for item in an_obj: + return_list.append(json_serial_killer(item)) + return(return_list) + elif isinstance(an_obj, (int, float, str)): + return(an_obj) + elif isinstance(an_obj, DataFrame): + return(an_obj.to_dict()) + +def read_json(path, **kwargs): + with open(path, 'r') as ftw: + my_json = json.load(ftw) + return(my_json) + +class SOBOL: + def __init__(self, path, inputs = [], time_step = 0, matrix_reporter_name = 'matrix', sobol_reporter_name = 'sobol', statistics_reporter_name = 'stats', results_transfer_name = 'results'): + self.time_step = time_step + self.reference_dict = read_json(path) + self.path = path + self.matrix_reporter = matrix_reporter_name + self.sobol_reporter = sobol_reporter_name + self.statistics_reporter = statistics_reporter_name + self.results_transfer = results_transfer_name + + if inputs and inputs != []: + self.inputs = inputs + else: + inputs = [] + for key in self.reference_dict["time_steps"][self.time_step].keys(): + if f"{matrix_reporter_name}/" in key: inputs.append(f"{key.split('/')[-2]}/{key.split('/')[-1]}") + self.inputs = inputs + + if inputs == []: + for key in self.reference_dict["time_steps"][self.time_step][matrix_reporter_name].keys(): + if "results" not in key: inputs.append(key) + self.inputs = inputs + + self.sobol = {'confidence_intervals': self.reference_dict['reporters'][sobol_reporter_name]['confidence_intervals']['levels'], + 'number_of_samples': self.reference_dict['reporters'][sobol_reporter_name]['confidence_intervals']['replicates'], + 'inputs': inputs, + 'number_of_inputs': self.reference_dict['reporters'][sobol_reporter_name]['num_params'], + 'outputs': [(i.split(':'))[-2] for i in self.reference_dict['reporters'][sobol_reporter_name]['values'].keys()], + 'number_of_outputs': len(self.reference_dict['reporters'][sobol_reporter_name]['values'].keys())} + self.statistics = {'confidence_intervals': self.reference_dict['reporters'][sobol_reporter_name]['confidence_intervals']['levels'], + 'number_of_samples': self.reference_dict['reporters'][sobol_reporter_name]['confidence_intervals']['replicates'], + 'inputs': inputs, + 'number_of_inputs': len(inputs), + 'outputs': [], + 'number_of_outputs': len(self.reference_dict['reporters'][statistics_reporter_name].keys())} + stat_outs = [] + for key in self.reference_dict['time_steps'][0]['stats'].keys(): + out = key.split(':') + #print(out);v=input('Continue?') + out = out[-2] + if out not in stat_outs: stat_outs.append(out) + self.statistics['outputs'] = stat_outs + return + + def get_sobol_1st(self): + keys = self.sobol['confidence_intervals'] + sobol_first = {} + + for output in self.sobol['outputs']: + sobol_first[output] = {} + + sobol_first[output]['mean'] = {} + j = 0 + for inp in self.sobol['inputs']: + sobol_first[output]['mean'][inp] = self.reference_dict["time_steps"][self.time_step][self.sobol_reporter][f"{self.results_transfer}_results:{output}:value"]["FIRST_ORDER"][0][j] + j +=1 + i = 0 + for key in keys: + j = 0 + sobol_first[output][key] = {} + for inp in self.sobol['inputs']: + sobol_first[output][key][inp] = self.reference_dict["time_steps"][self.time_step][self.sobol_reporter][f"{self.results_transfer}_results:{output}:value"]["FIRST_ORDER"][1][i][j] + j +=1 + i += 1 + + sobol_first_df = {} + for output in self.sobol['outputs']: + sobol_first_df[output] = pd.DataFrame(sobol_first[output]) + + self.sobol['FIRST_ORDER'] = sobol_first_df + return(sobol_first_df) + + def get_sobol_2nd(self): + keys = self.sobol['confidence_intervals'] + sobol_scd = {} + for output in self.sobol['outputs']: + sobol_scd[output] = {'mean':{}} + + j = 0 + for inp in self.inputs: + sobol_scd[output]['mean'][inp] = self.reference_dict["time_steps"][self.time_step][self.sobol_reporter][f"{self.results_transfer}_results:{output}:value"]["SECOND_ORDER"][0][j] + j += 1 + + i = 0 + for key in keys: + sobol_scd[output][key] = {} + j = 0 + for inp in self.inputs: + sobol_scd[output][key][inp] = self.reference_dict["time_steps"][self.time_step][self.sobol_reporter][f"{self.results_transfer}_results:{output}:value"]["SECOND_ORDER"][1][i][j] + j += 1 + i += 1 + + sobol_scd_df = {} + for output in self.sobol['outputs']: + sobol_scd_df[output] = pd.DataFrame(sobol_scd[output]) + + self.sobol['SECOND_ORDER'] = sobol_scd_df + return(sobol_scd_df) + + def get_sobol_total(self): + keys = self.sobol['confidence_intervals'] + sobol_tot = {} + + for output in self.sobol['outputs']: + sobol_tot[output] = {} + + sobol_tot[output]['mean'] = {} + j = 0 + for inp in self.sobol['inputs']: + sobol_tot[output]['mean'][inp] = self.reference_dict["time_steps"][self.time_step][self.sobol_reporter][f"{self.results_transfer}_results:{output}:value"]["TOTAL"][0][j] + j +=1 + i = 0 + for key in keys: + j = 0 + sobol_tot[output][key] = {} + for inp in self.sobol['inputs']: + sobol_tot[output][key][inp] = self.reference_dict["time_steps"][self.time_step][self.sobol_reporter][f"{self.results_transfer}_results:{output}:value"]["TOTAL"][1][i][j] + j +=1 + i += 1 + + sobol_tot_df = {} + for output in self.sobol['outputs']: + sobol_tot_df[output] = pd.DataFrame(sobol_tot[output]) + + self.sobol['TOTAL'] = sobol_tot_df + return(sobol_tot_df) + + def get_var_accounted_for(self): + if 'SECOND_ORDER' not in self.sobol.keys(): + self.get_sobol_2nd() + + self.sobol['var_accounted_for'] = {} + for output in self.sobol['outputs']: + var_acc_for = 0 + + for col in self.reference_dict["time_steps"][0]["sobol"][f"results_results:{output}:value"]["SECOND_ORDER"][0]: + for row in col: + var_acc_for += row + + self.sobol['var_accounted_for'][output] = var_acc_for + return(self.sobol['var_accounted_for']) + + def get_statistics(self): + self.statistics['statistics'] = {} + for output in self.statistics['outputs']: + self.statistics['statistics'][output] = {'value':{}, 'stddev':{}} + self.statistics['statistics'][output]['value']['mean'] = self.reference_dict['time_steps'][self.time_step][self.statistics_reporter][f"{self.results_transfer}_results:{output}:value_MEAN"][0] + self.statistics['statistics'][output]['stddev']['mean'] = self.reference_dict['time_steps'][self.time_step][self.statistics_reporter][f"{self.results_transfer}_results:{output}:value_STDDEV"][0] + i = 0 + for ci in self.statistics['confidence_intervals']: + self.statistics['statistics'][output]['value'][ci] = self.reference_dict['time_steps'][self.time_step][self.statistics_reporter][f"{self.results_transfer}_results:{output}:value_MEAN"][1][i] + self.statistics['statistics'][output]['stddev'][ci] = self.reference_dict['time_steps'][self.time_step][self.statistics_reporter][f"{self.results_transfer}_results:{output}:value_STDDEV"][1][i] + i += 1 + + for output in self.statistics['outputs']: + self.statistics['statistics'][output] = pd.DataFrame(self.statistics['statistics'][output]) + + return(self.statistics['statistics']) + + def get_matrix(self): + matrix = {'inputs':{}, 'outputs': {}} + # First, establish inputs and outputs + inputs = self.inputs + outputs = self.sobol['outputs'] + # Second, extract input matrix, store as dataframe + inp_matrix = {} + for inp in inputs: + try: + inp_matrix[inp] = self.reference_dict["time_steps"][self.time_step][f"{self.matrix_reporter}/{inp}"]["value"] + except: + try: + inp_matrix[inp] = self.reference_dict["time_steps"][self.time_step][self.matrix_reporter][inp] + except: + print(f"Could not find {inp} matrix for {self.path}") + + # Third, extract output matricies for each output, store as dictionary of {output: dataframe} + outp_matrix = {} + for outp in outputs: + outp_matrix[outp] = self.reference_dict["time_steps"][self.time_step][self.matrix_reporter][f"{self.matrix_reporter}_results:{outp}:value"] + + # package as attribute + self.matrix = {"inputs": pd.DataFrame(inp_matrix), "outputs": pd.DataFrame(outp_matrix)} + return(self.matrix) + + def save_object(self, path: str, *args, do_not_print: list[str] = [], suppress_save = False, **kwargs)-> str: + attributes = self.__dict__ + print_attributes = {} + for k,v in attributes.items(): + if k not in do_not_print: + print_attributes[k] = v + + if suppress_save: + return(print_attributes) + else: + with open(f'{path}.json', 'w+') as ftw: + ftw.write(json.dumps(json_serial_killer(print_attributes), indent=4)) + print(f"Object attributes written to {path}.json as a json file.") + return(print_attributes) + + def estimate_state_frequency(self, state_limit, output, use_matrix = True, method = 'general', pdf=False, plot = True, normalize = True, factor = 'min', function=None, + path = '', dpi =300, save_fig = False): + path = path if path else f"frequency_of_{state_limit}_for{output}_by_{"matrix" if use_matrix else function}.png" + if use_matrix: + print(f"Using a frequency approach based on <{self.statistics['number_of_samples']}> samples.") + total_samples = len(self.matrix['outputs'][output]) + exceeding_samples = 0 + for i in self.matrix['outputs'][output]: + exceeding_samples += 1 if i > state_limit else 0 + + frequency = exceeding_samples/total_samples + print(frequency) + return(frequency) + + else: + print(f"Fitting data to a {function} probability distribution.") + data = np.asarray(self.matrix["outputs"][output]) + if normalize: + minima = np.min(data); mean = np.mean(data); median = np.median(data) + if method == 'mean': + data = np.divide(np.subtract(data, mean), mean) + state_limit = (state_limit - mean)/mean + elif method == 'min': + data = np.divide(np.subtract(data, minima), minima) + state_limit = (state_limit - minima)/minima + elif method == 'median': + data = np.divide(np.subtract(data, median), median) + state_limit = (state_limit - minima)/minima + frequency = 0 + + if method == 'general': + curve_args = function.fit(data) + if pdf and plot: + fig, ax = self.plot_histogram(labels=[output], plot = [output], normalize = False, density=True, factor=factor) + ax.plot(np.linspace(min(data), max(data)), function.pdf(np.linspace(min(data), max(data)), *curve_args)) + elif plot: + fig, ax = self.plot_percentiles(labels=[output], plot = [output], normalize = False, flip = True) + ax.plot(np.linspace(min(data), max(data)), function.cdf(np.linspace(min(data), max(data)), *curve_args)) + frequency = function.sf(state_limit, *curve_args) + chi_squared, p_value = scipy.chisquare(scipy.relfreq(data, numbins=int(0.01*len(data)))[0], + scipy.relfreq(function.pdf(np.linspace(min(data), max(data), num=len(data)), *curve_args), numbins=int(0.01*len(data)))[0], axis=0) + #print(f'Chi-squared: {chi_squared}\nP-value: {p_value} ({p_value-1})') + if save_fig: fig.savefig(path, dpi=dpi) + + print(f"Frequency: {frequency}") + return(frequency, curve_args) + + def plot_percentiles(self, labels = [], plot = [], flip = False, normalize = True, factor = 'mean', + xlabel = None, ylabel = None, pdf= False, path = '', dpi = 300, save_fig = False): + # Next: formulate dictionary of {output: {percentiles: [], values: []}} + plot = plot if plot else self.statistics['outputs'] + xlabel = 'Percentile' if not xlabel else xlabel; ylabel = 'Normalized Value' if not ylabel else ylabel + flip = True if pdf else flip + path = path if path else f"{plot}_percentiles.png" + plotting_dict = {} + means = {} + for output in plot: + means[output] = list(self.statistics['statistics'][output]['value'])[0] + plotting_dict[output] = {'percentiles': self.statistics['confidence_intervals'], + 'values': list(self.statistics['statistics'][output]['value'])[1:]} + # First: normalize confidence intervals with respect to the mean + for k, v in plotting_dict.items(): + #mean = plotting_dict[k]['values'][0] + if normalize: + plotting_dict[k]['values'] = [i/means[k] for i in v['values']] + # Plot all using lineplot, markers, and legend + fig, ax = plt.subplots(1,1, figsize = (6,4)) + ax.set_title('Cumulative Distributions'); ax.set_xlabel(xlabel); ax.set_ylabel(ylabel) + ax.set_facecolor('aliceblue');ax.grid(visible = True, which = 'both', axis = 'both', color = 'gainsboro', linestyle = ':') + markers = [',','.','o','^','v','<','>','s','o','P','+','x','D'] + i = 0 + for label, data in plotting_dict.items(): + if flip: + percentiles = data['percentiles'] if not pdf else [0] + [(data['percentiles'][i]-data['percentiles'][i-1])/(data['values'][i]-data['values'][i-1]) for i in range(1, len(data['percentiles']))] + plotting_dict[label]['percentiles'] = percentiles + ax.plot(data['values'], percentiles, label = label, marker = markers[i]) + ax.set_xlabel('Normalized Value'); ax.set_ylabel('Percentile') + else: + ax.plot(data['percentiles'], data['values'], label = label, marker = markers[i]) + i += 1 + if labels: + ax.legend(labels = labels, loc = 'upper center', ncols = 3, fontsize = 'small', bbox_to_anchor = (0.5, -0.15)) + else: + ax.legend(loc = 'upper center', ncols = 3, fontsize = 'small', bbox_to_anchor = (0.5, -0.15)) + + if save_fig: fig.savefig(path, dpi=dpi) + return(plotting_dict, (fig,ax)) + + def plot_histogram(self, labels = [], plot = [], density=False, normalize = True, factor = 'mean', + xlabel = None, ylabel = None, plot_inp=False, path = '', dpi = 300, save_fig = False): + path = path if path else f"{plot}_histogram.png" + if plot_inp: + plot = list(self.matrix['inputs'].columns) if not plot else plot + else: + plot = plot if plot else list(self.matrix["outputs"].columns) + + labels = labels if labels else plot + ylabel = 'Frequency' if not ylabel else ylabel + histogram_dict = {} + + for outp in plot: + histogram_dict[outp] = list(self.matrix["inputs"][outp]) if plot_inp else list(self.matrix["outputs"][outp]) + if normalize: + if factor == 'mean': + xlabel = '(X-Mean)/Mean' if not xlabel else xlabel + mean = np.mean(histogram_dict[outp]) + histogram_dict[outp] = [(i/mean - 1) for i in histogram_dict[outp]] + elif factor == 'min': + xlabel = '(X-Min)/Min' if not xlabel else xlabel + minima = np.min(histogram_dict[outp]) + histogram_dict[outp] = [((i-minima)/minima) for i in histogram_dict[outp]] + elif factor == 'median': + xlabel = '(X-Median)/Median' if not xlabel else xlabel + median = np.median(histogram_dict[outp]) + histogram_dict[outp] = [((i-median)/median) for i in histogram_dict[outp]] + + fig, ax = plt.subplots(1,1, figsize = (6,4)) + ax.set_title('Histogram Distributions'); ax.set_xlabel(xlabel); ax.set_ylabel(ylabel) + ax.set_facecolor('aliceblue');ax.grid(visible = True, which = 'both', axis = 'both', color = 'gainsboro', linestyle = ':') + + for outp, hist in histogram_dict.items(): + ax.hist(hist, fill=False, histtype="step", stacked=True, label=outp, bins=int(len(hist)/100), density = density) + if labels: + ax.legend(labels = labels, loc = 'upper center', ncols = 3, fontsize = 'small', bbox_to_anchor = (0.5, -0.15)) + else: + ax.legend(loc = 'upper center', ncols = 3, fontsize = 'small', bbox_to_anchor = (0.5, -0.15)) + + if save_fig: fig.savefig(path, dpi=dpi) + return((fig,ax)) + + def plot_violin(self, labels = [], plot = [], normalize = True, factor = 'mean', + ylabel = None, xlabel = None, path = '', dpi = 300, save_fig = False): + plot = plot if plot else self.matrix["outputs"] + labels = labels if labels else list(self.matrix['outputs'].index) + ylabel = '(X-Mean)/Mean' if not ylabel else ylabel + path = path if path else f"{plot}_violin_plot.png" + vio_dict = {} + + for outp in plot: + vio_dict[outp] = list(self.matrix["outputs"][outp]) + if normalize: + if factor == 'mean': + xlabel = '(X-Mean)/Mean' if not xlabel else xlabel + mean = np.mean(vio_dict[outp]) + vio_dict[outp] = [(i/mean - 1) for i in vio_dict[outp]] + elif factor == 'min': + xlabel = '(X-Min)/Min' if not xlabel else xlabel + minima = np.min(vio_dict[outp]) + vio_dict[outp] = [((i-minima)/minima) for i in vio_dict[outp]] + elif factor == 'median': + xlabel = '(X-Median)/Median' if not xlabel else xlabel + median = np.median(vio_dict[outp]) + vio_dict[outp] = [((i-median)/median) for i in vio_dict[outp]] + + fig, ax = plt.subplots(1,1, figsize = (6,4)) + ax.set_title('Output Distributions'); ax.set_ylabel(ylabel) + ax.set_facecolor('aliceblue');ax.grid(visible = True, which = 'both', axis = 'both', color = 'gainsboro', linestyle = ':') + + positions = [i+1 for i in range(0, len(vio_dict.keys()))] + viola = ax.violinplot(vio_dict.values(), positions, showmeans=True, showextrema=True) + + i = 0 + for part in viola['bodies']: + part.set_facecolor(colors[i]); part.set_edgecolor('black') + i += 1 + + if labels: + ax.legend(labels = labels, loc = 'upper center', ncols = 3, fontsize = 'small', bbox_to_anchor = (0.5, -0.15)) + else: + ax.legend(loc = 'upper center', ncols = 3, fontsize = 'small', bbox_to_anchor = (0.5, -0.15)) + + if save_fig: fig.savefig(path, dpi=dpi) + return() + + def plot_sobol(self, outp = '', order = 'FIRST_ORDER', inp_labels = ['Mobile T Flux', 'Cool. T Conc.','Heat Flux','Cool. Temp.'], + outp_labels=['H3 Permeation','H3 FLux', 'Coo. Heat Flux','Cu Max T','CCZ Max T','W Max T','H3 Retention'], + lci = 0.05, uci = 0.95, path = '', dpi = 300, save_fig = False): + path = path if path else f"{order}_sensitivity_indicies.png" + fig, ax = plt.subplots(len(self.inputs),1, figsize = (8,8)) + ax[0].set_title(f'{order} Sensitivity Indicies') + x = [j+1 for j in range(0, len(self.sobol['outputs']))] + + i = 0 + for inp in self.inputs: + ax[i].set_ylabel(f'{inp_labels[i]}');#ax[i].set_ylim(-0.25,1.25) + ax[i].set_facecolor('aliceblue');ax[i].grid(visible = True, which = 'both', axis = 'both', color = 'gainsboro', linestyle = ':') + + y = np.asarray([self.sobol[order][outp]['mean'][inp] for outp in self.sobol['outputs']]); #print(y) + n_err = np.asarray([self.sobol[order][outp][lci][inp] for outp in self.sobol['outputs']]); n_err = np.abs(np.subtract(n_err, y)) + p_err = np.asarray([self.sobol[order][outp][uci][inp] for outp in self.sobol['outputs']]); p_err = np.abs(np.subtract(p_err, y)) + + ax[i].errorbar(x, y, yerr = [n_err, p_err], linewidth=0, elinewidth=1, marker='o') + i += 1 + + # if i == 1: + # for j in range(0, len(self.sobol['outputs'])): + # print(f"{y[j]}:{n_err[j]}:{p_err[j]}") + + + x_string = '' + k = 1 + for j in outp_labels: + x_string += f"{k} = {j} " + if k % 4 == 0: + x_string += '\n' + k += 1 + ax[-1].set_xlabel(x_string, loc='left') + #print(self.sobol[order].keys()) + if save_fig: fig.savefig(path, dpi=dpi) + return() + + def sobol_heat_map(self, outp = 'max_temperature_CuCrZr', outp_label = 'Tritium Permeation', + xlabels = ['Tritium Flux','Coolant Tritium \nConcentration','Heat Flux','Coolant Temperature'], + ylabels = ['Tritium Flux','Coolant Tritium \nConcentration','Heat Flux','Coolant Temperature'], + path = '', dpi = 300, save_fig = False): + # Construct new dataframe + path = path if path else f"sobol_2ND_ORDER_{outp}_heatmap.png" + new_df = {} + operating_df = self.sobol['SECOND_ORDER'][outp]; rows = list(operating_df.index) + for row in rows: + new_df[row] = {} + i = 0 + for subrow in rows: + new_df[row][subrow] = operating_df['mean'][row][i] + i += 1 + new_df = pd.DataFrame(new_df) + axes = seaborn.heatmap(new_df, cmap='bwr', xticklabels=xlabels, yticklabels=ylabels, label=outp_label, annot=True, vmin=-1.0, vmax=2.0) + if save_fig: + figure = axes.get_figure(); figure.savefig(path, dpi=dpi) + return(new_df) + + def confidence_interval_heatmap(self, order = 'FIRST_ORDER', outp = 'max_temperature_CuCrZr', outp_label = 'CCZ Maximum Temperature', + xlabels = ['Tritium Flux','Coolant Tritium \nConcentration','Heat Flux','Coolant Temperature'], + min_cf = 0.05, max_cf = 0.95, heatmap = True, title = None, path = '', dpi = 200, save_fig = False): + # Construct new dataframe + ylabels = [min_cf, 'mean', max_cf]; title = title if title else outp_label + path = path if path else f"{order}_{outp}_heatmap.png" + new_df = {} + operating_df = self.sobol[order][outp]; rows = list(operating_df.index) + for row in rows: + new_df[row] = {min_cf: operating_df[min_cf][row], 'Mean': operating_df['mean'][row], max_cf: operating_df[max_cf][row]} + new_df = pd.DataFrame(new_df) + if heatmap: + axes = seaborn.heatmap(new_df, cmap='bwr', xticklabels=xlabels, yticklabels=ylabels, label=outp_label, annot=True) + if save_fig: + figure = axes.get_figure(); figure.savefig(path, dpi=dpi) + else: + styler = new_df.style \ + .format(precision=3) + #new_df + return(new_df) + + def confidence_interval_table(self, outp = 'max_temperature_CuCrZr', outp_label = 'CCZ Maximum Temperature', + xlabels = ['Tritium Flux','Coolant Tritium \nConcentration','Heat Flux','Coolant Temperature'], + min_cf = 0.05, max_cf = 0.95, precision = 2): + xlabels = xlabels if xlabels else self.inputs + col_dict = {} + for k in range(0, len(self.inputs)): + col_dict[self.inputs[k]] = xlabels[k] + + df = {} + for inp in self.inputs: + df[inp] = {} + for n_inp in range(0, len(self.inputs)): + df[inp][n_inp] ='' + df[inp][n_inp] += f"{np.round(self.sobol["SECOND_ORDER"][outp]["mean"][inp][n_inp], precision)} " + df[inp][n_inp] += f"({np.round(self.sobol["SECOND_ORDER"][outp][min_cf][inp][n_inp], precision)}," + df[inp][n_inp] += f"{np.round(self.sobol["SECOND_ORDER"][outp][max_cf][inp][n_inp], precision)})" + df = pd.DataFrame(df) + df.style.background_gradient(cmap=seaborn.light_palette("blue", as_cmap=True)) + df.style.set_caption(f"{outp} Sensitivity Indicies") + df.style.set_properties(**{'text-align': 'center'}) + df.index = xlabels; df.rename(columns=col_dict, inplace=True) + return(df) + + def plot_inp_out(self, outp, inp, xlabel = None, ylabel = None, curve_fit_function = lambda x, a, b: a +b*x, guess_args = [0.0, 1.0], path = '', dpi = 300, save_fig = False): + xlabel = inp if not xlabel else xlabel; ylabel = outp if not ylabel else ylabel + path = path if path else f"{outp}_vs_{inp}.png" + + fig, ax = plt.subplots(1,1, figsize = (6,4)) + ax.set_title(f'{ylabel} vs {xlabel}'); ax.set_ylabel(ylabel); ax.set_xlabel(xlabel) + ax.set_facecolor('aliceblue');ax.grid(visible = True, which = 'both', axis = 'both', color = 'gainsboro', linestyle = ':') + + x_vector = self.matrix['inputs'][inp] + y_vector = self.matrix['outputs'][outp] + ax.scatter(x_vector, y_vector, marker='o') + + if curve_fit_function: + fit_args, pcov = optimize.curve_fit(curve_fit_function, x_vector, y_vector, p0 = guess_args) + #print(f"Optimized parameters: {fit_args}") + y_pred = curve_fit_function(x_vector, *fit_args) + + ss_tot = np.sum((y_vector - np.mean(y_vector))**2) + ss_res = np.sum((y_vector - y_pred)**2) + r_sqr = 1 - ss_res/ss_tot + + #print(f"R2: {r_sqr}") + if save_fig: fig.savefig(path, dpi=dpi) + return() + +do_these = ['coolant_heat_flux', 'max_temperature_Cu','max_temperature_CuCrZr', 'max_temperature_W', 'total_retention'] +labels = ["Coolant Heat Flux","Cu Max T","CCZ Max T","W Max T", "Total Retention"] +A1 = SOBOL("Sobol Studies/Accident 1 Sobol/Accident_1_Sobol_out_v2.json") +A2 = SOBOL("Sobol Studies/Accident 2 Sobol/Accident_2_Sobol_out_v2.json") +SS = SOBOL("Sobol Studies/Steady Sobol v1/Sobol_v4_out.json") +A1.get_matrix(); A2.get_matrix(); SS.get_matrix() +A1.get_sobol_1st(); A2.get_sobol_1st(); SS.get_sobol_1st() +A1.get_sobol_2nd(); A2.get_sobol_2nd(); SS.get_sobol_2nd() +A2.get_sobol_total(); A2.get_sobol_total(); SS.get_sobol_total() +A1.get_statistics(), A2.get_statistics(); SS.get_statistics() diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/steady_state_CCZ_state_frequency.png b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/steady_state_CCZ_state_frequency.png new file mode 100644 index 000000000..9e894c004 Binary files /dev/null and b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/steady_state_CCZ_state_frequency.png differ diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/steady_state_W_sensitivity.png b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/steady_state_W_sensitivity.png new file mode 100644 index 000000000..7975b7f13 Binary files /dev/null and b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/steady_state_W_sensitivity.png differ diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/steady_state_inputs.png b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/steady_state_inputs.png new file mode 100644 index 000000000..bb101ce14 Binary files /dev/null and b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/steady_state_inputs.png differ diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/steady_state_outputs.png b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/steady_state_outputs.png new file mode 100644 index 000000000..08ec76654 Binary files /dev/null and b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/steady_state_outputs.png differ diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/steady_state_retention_sensitivity.png b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/steady_state_retention_sensitivity.png new file mode 100644 index 000000000..e8358cdd2 Binary files /dev/null and b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/steady_state_retention_sensitivity.png differ diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/tmap_processing_v1.py b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/tmap_processing_v1.py new file mode 100644 index 000000000..e2772d954 --- /dev/null +++ b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/tmap_processing_v1.py @@ -0,0 +1,1190 @@ +# DETAILS: N. H. Snow Rev1 +# Good luck to anyone who will attempt to edit or use this file. + +from matplotlib import pyplot as plt; from matplotlib.colors import TABLEAU_COLORS as plt_colors +import pandas as pd; from pandas import DataFrame +import numpy as np; from numpy import ndarray +import typing; from typing import Union +import json +import os +from importlib import reload +from scipy import optimize; import scipy.stats as scipy +import seaborn +from IPython.display import HTML + +plt = reload(plt) +plt.rcParams['font.family'] = 'Cambria' +plt.rcParams['font.size'] = 11 +colors = list(plt_colors.values()) + +def json_serial_killer(an_obj, **kwargs): + return_dict = {} + try: + if isinstance(an_obj, DataFrame): + raise(AttributeError) + else: + obj_attributes = an_obj.__dict__ + for k, v in obj_attributes.items(): + return_dict[k] = json_serial_killer(v) + return(return_dict) + except(AttributeError): + if isinstance(an_obj, dict): + obj_attributes = an_obj + for k, v in obj_attributes.items(): + return_dict[k] = json_serial_killer(v) + return(return_dict) + elif isinstance(an_obj, list): + return_list = [] + for item in an_obj: + return_list.append(json_serial_killer(item)) + return(return_list) + elif isinstance(an_obj, ndarray): + an_obj = an_obj.tolist() + return_list = [] + for item in an_obj: + return_list.append(json_serial_killer(item)) + return(return_list) + elif isinstance(an_obj, (int, float, str)): + return(an_obj) + elif isinstance(an_obj, DataFrame): + return(an_obj.to_dict()) + +def read_json(path, **kwargs): + with open(path, 'r') as ftw: + my_json = json.load(ftw) + return(my_json) + +def call_inner_method(inner_obj, inner_method): + return inner_obj.inner_method + +def json_to_RESULTS(json_path = None, post_dictionary = None, **kwargs): + json_dict = read_json(json_path) if not post_dictionary else post_dictionary + json_results = RESULTS(empty=True) + json_results.path = json_dict['path'] if 'path' in json_dict.keys() else None + json_results.data_frame = json_dict['data_frame'] if 'data_frame' in json_dict.keys() else None + json_results.data_vectors = json_dict['data_vectors'] if 'data_vectors' in json_dict.keys() else {} + for vector,results in json_results.data_vectors.items(): + json_results.data_vectors[vector] = np.asarray(results) + json_results.post_processing_vectors = json_dict['post_processing_vectors'] if 'post_processing_vectors' in json_dict.keys() else {} + for post_vector, step_dict in json_results.post_processing_vectors.items(): + json_results.post_processing_vectors[post_vector] = {} + for step, result_object in step_dict.items(): + json_results.post_processing_vectors[post_vector][step] = json_to_RESULTS(post_dictionary=result_object) + return(json_results) + +class RESULTS: + def __init__(self, path: str = None, *args, empty: bool = False, **kwargs): + self.path = path if not empty else None + self.data_frame = pd.read_csv(path) if not empty else None + self.data_vectors = {} + self.post_processing_vectors = {} + self.plots = {} + return + # Re-associates a csv (dataframe) with the results object + def Reassociate(self, path: str = None): + path = path if path else self.path + self.data_frame = pd.read_csv(path) + return() + + ################################################################################################################## Reading Data Methods ######### + # Reads vector from self's dataframe. Returns a numpy array. + def read_vector(self, vector: str, *args) ->ndarray: + data_vector = np.asarray(self.data_frame[vector].tolist()) + self.data_vectors[vector] = data_vector + return(data_vector) + + # Reads multiple vectors, returns a dictionary of vectors and their ndarrays + def read_vectors(self, vector_list: list[str], **kwargs) ->dict[str,ndarray]: + vector_dict = {} + for vector in vector_list: + vector_dict[vector] = self.read_vector(vector) + return() + + # Reads the results from VectorPostProcessors, expecting a directory containing the .csv files, using the naming convention set by MOOSE. + def read_vector_post_processor_data(self, directory_path: str, post_processing_vector: str, *args, vector_list: list[str] = [], **kwargs)-> any: + vector_post_processing_outputs = [] + for file in os.listdir(directory_path): + if post_processing_vector in file: vector_post_processing_outputs.append(file) + if vector_post_processing_outputs == []: + return('No data available') + self.post_processing_vectors[post_processing_vector] = {} + for file in vector_post_processing_outputs: + self.post_processing_vectors[post_processing_vector][(file[:-4].split('_'))[-1]] = RESULTS(os.path.join(directory_path, file)) + for vector in vector_list: + self.post_processing_vectors[post_processing_vector][(file[:-4].split('_'))[-1]].read_vector(vector) + return() + + # Save a vector or post_processor_vector as a json file. Vector must exist as a .data_vector entry in the RESULTS object or one of it's nested RESULTS objects. + def save_vector(self, *args, path: str = '', vector: str = None, post_processor_vector: str = None, step: str = None, suppress_json: bool = False, **kwargs)-> Union[None, dict]: + if post_processor_vector: + saved_vector = self.post_processing_vectors[post_processor_vector][step].data_vectors[vector] + else: + saved_vector = self.data_vectors[vector] + + if post_processor_vector: + vector = f'{post_processor_vector}_{step}_{vector}' # rename for output + + if suppress_json: + return({'name':vector, 'data':saved_vector.tolist()}) + else: + with open(f'{path}.json', 'w+') as ftw: + ftw.write(json.dumps({'name':vector, 'data':saved_vector.tolist()}, indent=4)) + return() + + # Saving multiple vectors to the same json file. + def save_vectors(self, path: str, *args, vector_list: list[str] = None, post_processor_vector_list: list[str] = [], step_list: list[str] = [], **kwargs)-> None: + saved_dict = {} + for vector in vector_list: + if post_processor_vector_list: + for post_vector in post_processor_vector_list: + for step in step_list: + saved_dict[f'{post_vector}_{step}_{vector}'] = self.save_vector(vector = vector, post_processor_vector = post_vector, + step = step, suppress_json = True) + else: + saved_dict[vector] = self.save_vector(vector=vector, suppress_json=True) + + with open(f'{path}.json', 'w+') as ftw: + ftw.write(json.dumps({'name':vector, 'data':saved_dict}, indent=4)) + return() + + # Save the results object, de-composing most or all attributes of the RESULTS object. Be wary of non-serializeable objects or data. + def save_object(self, path: str, *args, do_not_print: list[str] = ['data_frame', 'plots'], suppress_save = False, **kwargs)-> str: + do_not_print.append('post_processor_vectors') + attributes = self.__dict__ + print_attributes = {} + for k,v in attributes.items(): + if k not in do_not_print: + print_attributes[k] = v + + post_processing_vectors_dict = {} + for vector, step_dict in self.post_processing_vectors.items(): + post_processing_vectors_dict[vector] = {} + for step, result in step_dict.items(): + post_processing_vectors_dict[vector][step] = result.save_object(path = '', *args, do_not_print = do_not_print, suppress_save = True) + + print_attributes['post_processing_vectors'] = post_processing_vectors_dict + + if suppress_save: + return(print_attributes) + else: + with open(f'{path}.json', 'w+') as ftw: + ftw.write(json.dumps(json_serial_killer(print_attributes), indent=4)) + print(f"Object attributes written to {path}.json as a json file.") + return(print_attributes) + + ################################################################################################################## Manipulating Data Methods #### + # Scales a vector given a user-defined factor, see also normalize_vector. + def get_scaled_vector(self, vector: str, factor: float, *args, new_name: str = '', **kwargs)-> ndarray: + new_vector = new_name if new_name else vector + self.data_vectors[new_vector] = np.multiply(self.data_vectors[vector], factor) + return(self.data_vectors[new_vector]) + + # Normalize a vector according to its total or its maximum (default) + def get_normalized_vector(self, vector: str, *args, by_total: bool = False, new_name: str = '', **kwargs)-> ndarray: + un_normalized = self.data_vectors[vector] + norm_factor = sum(un_normalized.tolist()) if by_total else max(un_normalized.tolist()) + normalized = np.divide(un_normalized, norm_factor) + new_vector = new_name if new_name else f'{vector}_normalized' + self.data_vectors[new_vector] = normalized + return(normalized) + + # Integrates function to aquire cumulative value, + def get_cumulative_function(self, vector: str, *args, new_name: str = '', **kwargs)-> ndarray: + root_function = self.data_vectors[vector].tolist() + cumulative_function = [root_function[0]] + for value in root_function[1:]: + cumulative_function.append(value + cumulative_function[-1]) + new_vector = new_name if new_name else f'{vector}_cumulative' + self.data_vectors[new_vector] = np.asarray(cumulative_function) + return(cumulative_function) + + # Differentiates function to get spacing/difference e.g. time step if operating on time + def get_differential_function(self, vector: str, *args, new_name: str = '', **kwargs)-> ndarray: + new_vector = new_name if new_name else f'{vector}_differential' + root_function = self.data_vectors[vector].tolist() + differential_function = [root_function[0]] + for i in range(1, len(root_function)): + differential_function.append(root_function[i] - root_function[i-1]) + self.data_vectors[new_vector] = np.asarray(differential_function) + return(differential_function) + + # Get the dot product between two vectors (scalar) or element wise multiplication (element_wise) + def get_vector_dot_product(self, vector1: str, vector2: str, element_wise: bool = True, *args, new_name: str = '', **kwargs)-> Union[float, ndarray]: + new_name = new_name if new_name else f'{vector1}_{vector2}_dot_product' + func_1 = self.data_vectors[vector1] + func_2 = self.data_vectors[vector2] + dot_product = np.dot(func_1, func_2) if not element_wise else np.multiply(func_1, func_2) + self.data_vectors[new_name] = dot_product + return(dot_product) + + # Sums an arbitrary number of vectors + def sum_vectors(self, vector_list: list[str], *args, new_name: str = '', **kwargs)-> ndarray: + if new_name: + new_vector = new_name + else: + new_vector = 'sum_of' + for i in vector_list: + new_vector += f'_{i}' + + summed_vector = self.data_vectors[vector_list[0]] + for vector in vector_list[1:]: + summed_vector = np.add(summed_vector, self.data_vectors[vector]) + + self.data_vectors[new_vector] = summed_vector + return(summed_vector) + + # Finds the closest point to a given value for a specified vector. Also returns the values of other corresponding vectors at that same index. Only valid for PostProcessors not VectorPostProcessors + def get_closest_point(self, value: Union[float, int], vector: str, *args, corresponding_vectors: list[str] = [], **kwargs)-> dict[str, Union[float, int]]: + my_vector = self.data_vectors[vector].tolist() + distance = [np.abs(i-value) for i in my_vector] + closest_index = distance.index(min(distance)) + closest = {'index':closest_index, + 'value': my_vector[closest_index], + 'corresponding_values' : [float(self.data_vectors[i][closest_index]) for i in corresponding_vectors]} + return(closest) + + def manipulate_post_processor_data(self, post_processor_vector, *args, function = None, **kwargs): + #active_function = function + for time_step, step_results in self.post_processing_vectors[post_processor_vector].items(): + results = function(step_results, *args) + return() + + def apply_kernel(self, vector, function, *args, post_processing_vector = None, step = None, new_name = None, **kwargs): + new_name = new_name if new_name else f"{vector}_{function}" + if not post_processing_vector: + result = function(self.data_vectors[vector], *args, **kwargs) + self.data_vectors[new_name] = result + else: + result = function(self.post_processing_vectors[post_processing_vector][step].data_vectors[vector]) + self.post_processing_vectors[post_processing_vector][step].data_vectors[new_name] = result + return(result) + + ################################################################################################################## Plotting Data Methods ######### + def show_plot(self, fig_to_show): + plt.show(self.plots[fig_to_show]) + return() + def clear_plot(self): + plt.clf() + return() + + def plot_time_series_vector(self, x, y, post_processing_vector, + xlabel = '', ylabel = '', title = '', xmin = 0, xmax = 0, ymin = 0, ymax = 0,xscale = 'linear', yscale = 'linear', display_rate = 0, **kwargs): + xlabel = xlabel if xlabel else x; ylabel = ylabel if ylabel else y + title = title if title else f'{xlabel} vs {ylabel}' + plt.ion() + fig, ax = plt.subplots(1,1, figsize=(6,4)) + ax.set_xlabel(xlabel); ax.set_ylabel(ylabel); ax.set_title(title) + ax.set_xscale(xscale); ax.set_yscale(yscale) + ax.set_facecolor('aliceblue'); ax.grid(visible = True, which = 'both', axis = 'both', color = 'gainsboro', linestyle = ':', ) + for time_step, step_result in self.post_processing_vectors[post_processing_vector].items(): + if time_step != '0000': + ax.set_title(f'{title} at {np.round(self.data_vectors['time'][int(time_step)], 1)}s') + xvector = step_result.data_vectors[x]*1000 # convert to mm + yvector = step_result.data_vectors[y] + if len(ax.get_lines())>4: + (ax.get_lines())[0].remove() + ax.plot(xvector, yvector, label = time_step, color = 'darkblue', linewidth = 1.5) + plt.pause(display_rate) + plt.show() + ax.legend() + plt.ioff() + return() + + def plot_time_series_vectors(self, x, y_list, post_processing_vector, + xlabel = '', ylabels = [], titles = [], xmin = 0, xmax = 0, ymin = 0, ymax = 0,xscale = 'linear', yscale = 'linear', display_rate = 0, **kwargs): + + xlabel = xlabel if xlabel else x; + titles = titles if titles else [f'{xlabel} vs {ylabels[i]}' for i in range (0, len(y_list))] + plt.ion() + + fig, ax = plt.subplots(len(y_list),1, figsize=(12,4)) + i = 0 + for y in y_list: + ax[i].set_xlabel(xlabel); ax[i].set_ylabel(ylabels[i]); ax[i].set_title(titles[i]) + ax[i].set_xscale(xscale); ax[i].set_yscale(yscale) + ax[i].set_facecolor('aliceblue'); ax[i].grid(visible = True, which = 'both', axis = 'both', color = 'gainsboro', linestyle = ':', ) + i += 1 + + for time_step, step_result in self.post_processing_vectors[post_processing_vector].items(): + if time_step != '0000': + i = 0 + xvector = step_result.data_vectors[x]*1000 # convert to mm + for y in y_list: + ax[i].set_title(f'{titles[i]} at {np.round(self.data_vectors['time'][int(time_step)], 1)}s') + yvector = step_result.data_vectors[y] + + if len(ax[i].get_lines())>4: + (ax[i].get_lines())[0].remove() + ax[i].plot(xvector, yvector, label = time_step, color = 'darkblue', linewidth = 1.5) + i += 1 + + plt.pause(display_rate) + plt.show() + for axes in ax: + axes.legend() + plt.ioff() + return() + + def general_plot(self, x_list, y_list, figure_title = '', labels = [],post_processor_vector = None, step = None, + x_label = 'Abscissa', y_label = 'Ordinate(s)', x_min = None, x_max = None, y_min = None, y_max = None, x_scale = 'linear', yscale = 'linear', + marker = 'o', linewidth = 1.5 ): + x_list = x_list if len(x_list)>1 else [x_list[0] for i in y_list]; labels = labels if labels else y_list; labels = labels if len(labels)>1 else [labels[0] for i in y_list] + fig, ax = plt.subplots(1,1, figsize=(6,4)) + ax.set_xlabel(x_label); ax.set_ylabel(y_label); ax.set_xscale(x_scale); ax.set_yscale(yscale);ax.set_facecolor('aliceblue');ax.grid(visible = True, which = 'both', axis = 'both', color = 'gainsboro', linestyle = ':') + ax.set_title(figure_title) + + for i in range(0, len(y_list)): + if not post_processor_vector: + ax.plot(self.data_vectors[x_list[i]], self.data_vectors[y_list[i]], label = labels[i], linewidth = 1.5) + else: + ax.plot(self.post_processing_vectors[post_processor_vector][step].data_vectors[x_list[i]], + self.post_processing_vectors[post_processor_vector][step].data_vectors[y_list[i]], label = labels[i], color = 'darkblue', linewidth = 1.5) + + ax.set_xlim(x_min, x_max) + ax.set_ylim(y_min, y_max) + ax.legend() + return() + + def general_plot2(self, x, y, figure_name = '', post_processor_vector = None, step = None, clear_fig = True, operating_fig = None, + x_label = '', y_label = '', plot_label = '', marker = 'o', linewidth = 1.5, + x_min = 0, x_max = 0, y_min = 0, y_max = 0, x_scale = 'linear', y_scale = 'linear', **kwargs): + if clear_fig: + if figure_name: + new_fig = plt.figure(num=figure_name, figsize = [6.0, 4.0], dpi = 300, facecolor = 'aliceblue', edgecolor='slategrey') + else: + figure_name = str(len(self.plots.keys())+1) + new_fig = plt.figure(num=figure_name, figsize = [6.0, 4.0], dpi = 300, facecolor = 'aliceblue', edgecolor='slategrey') + new_axes = new_fig.add_axes(rect=(1,1,1,1), projection = 'rectilinear') + else: + plt.figure(self.plots[operating_fig]) + + x_vector = self.data_vectors[x] if not post_processor_vector else self.post_processing_vectors[post_processor_vector][step].data_vectors[x] + y_vector = self.data_vectors[y] if not post_processor_vector else self.post_processing_vectors[post_processor_vector][step].data_vectors[y] + x_limits = [0,0]; x_limits[0] = x_min if x_min else min(x_vector); x_limits[1] = x_max if x_max else max(x_vector) + y_limits = [0,0]; y_limits[0] = y_min if y_min else min(y_vector); y_limits[1] = y_max if y_max else max(y_vector) + + + new_axes.xlabel = x_label if x_label else x; plt.ylabel = y_label if y_label else y; plt.xlim(x_limits); plt.ylim(y_limits) + new_axes.xscale = x_scale; plt.yscale = y_scale + plt.plot(x_vector,y_vector, label = plot_label, marker = marker, linewidth = linewidth) + + self.plots[figure_name] = new_fig + return() + + def plot_vector(self, x, y, post_processor_vector = None, step = None, + xscale = 'linear',yscale = 'linear', x_units = '', y_units = '', + xlabel = None, ylabel = None, x_min = None, x_max = None, y_min = None, y_max = None): + xlabel = xlabel if xlabel else x; ylabel = ylabel if ylabel else y + x_vector = self.post_processing_vectors[post_processor_vector][step].data_vectors[x] if post_processor_vector else self.data_vectors[x] + y_vector = self.post_processing_vectors[post_processor_vector][step].data_vectors[y] if post_processor_vector else self.data_vectors[y] + + assert len(x_vector) == len(y_vector) + plt.plot(x_vector, y_vector) + plt.xlabel(xlabel); plt.ylabel(ylabel); plt.title(f'{ylabel} vs {xlabel}'); plt.yscale(yscale); plt.xscale(xscale) + #plt.xlim((0,1600)) + if x_max and x_min: + plt.xlim((x_min, x_max));print('HEY') + else: + print('huh') + if x_max and not x_min: plt.xlim(right=x_max) + if x_min and not x_max: plt.xlim(left=x_min) + if y_max and y_min: + plt.ylim((y_min, y_max)) + if y_max and not y_min: plt.ylim(top=y_max) + if y_min and not y_max: plt.ylim(bottom=y_min) + my_axs = plt.gca() + plt.show() + + def plot_arbitrary_number(self, x, y_list, post_processing_vector = None, plot_variable = None, + xscale = 'linear',yscale = 'linear', x_units = '', y_units = '', + xlabel = None, ylabel = None, + x_min = None, x_max = None, y_min = None, y_max = None, legend = False): + xlabel = xlabel if xlabel else 'X'; ylabel = ylabel if ylabel else 'Y' + x_vector = self.data_vectors[x] + + plt.xlabel(xlabel);plt.ylabel(ylabel);plt.title(f'{ylabel} vs {xlabel}');plt.yscale(yscale);plt.xscale(xscale) + plt.grid(visible=True, which='both', axis='both', color='gainsboro') + #plt.xlim((0,1600)) + if x_max and x_min: plt.xlim((x_min, x_max)) + #if x_max and not x_min: plt.xlim(right=x_max) + #if x_min and not x_max: plt.xlim(left=x_min) + if y_max and y_min: plt.ylim((y_min, y_max)) + #if y_max and not y_min: plt.ylim(top=y_max) + #if y_min and not y_max: plt.ylim(bottom=y_min) + for y in y_list: + y_vector = self.post_processing_vectors[post_processing_vector][y][plot_variable] if post_processing_vector else self.data_vectors[y] + plt.plot(x_vector, y_vector, label = y) + my_axs = plt.gca() + if legend: plt.legend() + plt.show() + + def plot_vector_pairs(self, xy_list, post_processing_vector = None, plot_variable = None, + xscale = 'linear',yscale = 'linear', x_units = '', y_units = '', + xlabel = None, ylabel = None, + x_min = None, x_max = None, y_min = None, y_max = None, legend = False): + xlabel = xlabel if xlabel else 'X'; ylabel = ylabel if ylabel else 'Y' + + #assert len(x_vector) == len(y_vector) + plt.xlabel(xlabel);plt.ylabel(ylabel);plt.title(f'{ylabel} vs {xlabel}');plt.yscale(yscale);plt.xscale(xscale) + #plt.xlim((0,1600)) + if x_max and x_min: plt.xlim((x_min, x_max)) + if x_max and not x_min: plt.xlim(right=x_max) + if x_min and not x_max: plt.xlim(left=x_min) + if y_max and y_min: plt.ylim((y_min, y_max)) + if y_max and not y_min: plt.ylim(top=y_max) + if y_min and not y_max: plt.ylim(bottom=y_min) + for xy in xy_list: + x = xy[0]; y = xy[1] + x_vector = self.post_processing_vectors[post_processing_vector][x].data_vectors[plot_variable] if post_processing_vector else self.data_vectors[x] + y_vector = self.post_processing_vectors[post_processing_vector][y].data_vectors[plot_variable] if post_processing_vector else self.data_vectors[y] + label = f'{y} vs {x}' if legend else None + plt.plot(x_vector, y_vector, label = label) + if legend: + plt.legend() + else: + pass + my_axs = plt.gca() + plt.show() + +class COMPARISON: + def __init__(self, comp1, comp2, **kwargs): + self.comp1 = RESULTS(comp1) if type(comp1) == str else comp1 + self.comp2 = RESULTS(comp2) if type(comp2) == str else comp2 + + self.comparisons = {} + self.shared = {} + return + + def get_shared_vector(self, vector): + self.shared[vector] = self.comp1.data_vectors[vector] if vector in self.comp1.data_vectors.keys() else self.comp2.data_vectors[vector] + return(self.shared[vector]) + + def abs_diff(self, vector): + self.comp1.read_vector(vector) + self.comp2.read_vector(vector) + + self.comparisons[f'{vector}_abs_diff'] = np.subtract(self.comp1.data_vectors[vector], self.comp2.data_vectors[vector]) + + return(self.comparisons[f'{vector}_abs_diff']) + + def rel_diff_2to1(self, vector): + self.comp1.read_vector(vector) + self.comp2.read_vector(vector) + + self.comparisons[f'{vector}_rel_diff_2to1'] = np.divide(np.subtract(self.comp2.data_vectors[vector], self.comp1.data_vectors[vector]), self.comp1.data_vectors[vector]) + + return(self.comparisons[f'{vector}_rel_diff_2to1']) + + def rel_diff_1to2(self, vector): + self.comp1.read_vector(vector) + self.comp2.read_vector(vector) + + self.comparisons[f'{vector}_rel_diff_1to2'] = np.divide(np.subtract(self.comp1.data_vectors[vector], self.comp2.data_vectors[vector]), self.comp2.data_vectors[vector]) + + return(self.comparisons[f'{vector}_rel_diff_1to2']) + + def Save_Object(self, path): + attributes = self.__dict__ + with open(f'{path}.json', 'w+') as ftw: + ftw.write(json.dumps(attributes, indent=4)) + return(f"Object attributes written to {path}.json as a json file.") + + def plot_vector(self, result_obj, x, y): + x_vector = result_obj.data_vectors[x] + y_vector = result_obj.data_vectors[y] + + assert len(x_vector) == len(y_vector) + plt.plot(x_vector, y_vector) + plt.xlabel(x);plt.ylabel(y);plt.title(f'{y} vs {x}') + my_axs = plt.gca() + plt.show() + + def plot_comparison(self, x, y, shared_x = True, x_object = None): + x_vector = x_object.data_vectors[x] if x_object else self.shared[x] + y_vector = self.comparisons[y] + + assert len(x_vector) == len(y_vector) + plt.plot(x_vector, y_vector) + plt.xlabel(x);plt.ylabel(y);plt.title(f'{y} vs {x}') + my_axs = plt.gca() + plt.show() + + def plot_two(self, x, y): + x1_vector = self.comp1.data_vectors[x] + x2_vector = self.comp2.data_vectors[x] + y1_vector = self.comp1.data_vectors[y] + y2_vector = self.comp2.data_vectors[y] + plt.plot(x1_vector, y1_vector, label = self.comp1.path) + plt.plot(x2_vector, y2_vector, label = self.comp2.path) + plt.xlabel(x);plt.ylabel(y);plt.title(f'{y} vs {x}') + plt.legend() + plt.show() + return() + + def general_plot(self, x_list_1, y_list_1, x_list_2, y_list_2, figure_title = '', labels_1 = [], labels_2 = [], post_processor_vector = None, step = None, + x_label = 'Abscissa', y_label = 'Ordinate(s)', x_min = 0, x_max = 0, y_min = 0, y_max = 0, x_scale = 'linear', yscale = 'linear', + marker = 'o', linewidth = 1.5 ): + + x_list_1 = x_list_1 if len(x_list_1)>1 else [x_list_1[0] for i in y_list_1]; labels_1 = labels_1 if labels_1 else y_list_1; labels_1 = labels_1 if len(labels_1)>1 else [labels_1[0] for i in y_list_1] + x_list_2 = x_list_2 if len(x_list_2)>1 else [x_list_2[0] for i in y_list_2]; labels_2 = labels_2 if labels_2 else y_list_2; labels_2 = labels_2 if len(labels_2)>1 else [labels_2[0] for i in y_list_2] + + fig, ax = plt.subplots(1,1, figsize=(6,4)) + ax.set_xlabel(x_label); ax.set_ylabel(y_label); ax.set_xscale(x_scale); ax.set_yscale(yscale);ax.set_facecolor('aliceblue');ax.grid(visible = True, which = 'both', axis = 'both', color = 'gainsboro', linestyle = ':') + ax.set_title(figure_title) + for i in range(0, len(y_list_1)): + if not post_processor_vector: + ax.plot(self.comp1.data_vectors[x_list_1[i]], self.comp1.data_vectors[y_list_1[i]], label = labels_1[i], linewidth = 1.5) + else: + ax.plot(self.comp1.post_processing_vectors[post_processor_vector][step].data_vectors[x_list_1[i]], + self.comp1.post_processing_vectors[post_processor_vector][step].data_vectors[y_list_1[i]], + label = labels_1[i], color = 'darkblue', linewidth = 1.5) + for i in range(0, len(y_list_2)): + if not post_processor_vector: + ax.plot(self.comp2.data_vectors[x_list_2[i]], self.comp2.data_vectors[y_list_2[i]], label = labels_2[i], linewidth = 1.5) + else: + ax.plot(self.comp2.post_processing_vectors[post_processor_vector][step].data_vectors[x_list_2[i]], + self.comp2.post_processing_vectors[post_processor_vector][step].data_vectors[y_list_2[i]], + label = labels_2[i], color = 'darkblue', linewidth = 1.5) + + ax.legend() + return() + +class SOBOL: + def __init__(self, path, inputs = [], time_step = 0): + #['Heat Flux','Tritium Flux', 'Coolant Temperature', 'Coolant Tritium Concentration'] + self.time_step = time_step + self.reference_dict = read_json(path) + self.path = path + + if inputs and inputs != []: + self.inputs = inputs + else: + inputs = [] + for key in self.reference_dict["time_steps"][self.time_step].keys(): + if "matrix/" in key: inputs.append(f"{key.split('/')[-2]}/{key.split('/')[-1]}")#;print(inputs[-1]) + self.inputs = inputs + + if inputs == []: + for key in self.reference_dict["time_steps"][self.time_step]["matrix"].keys(): + if "results" not in key: inputs.append(key)#; print(key) + self.inputs = inputs + + self.sobol = {'confidence_intervals': self.reference_dict['reporters']['sobol']['confidence_intervals']['levels'], + 'number_of_samples': self.reference_dict['reporters']['sobol']['confidence_intervals']['replicates'], + 'inputs': inputs, + 'number_of_inputs': self.reference_dict['reporters']['sobol']['num_params'], + 'outputs': [(i.split(':'))[-2] for i in self.reference_dict['reporters']['sobol']['values'].keys()], + 'number_of_outputs': len(self.reference_dict['reporters']['sobol']['values'].keys())} + self.statistics = {'confidence_intervals': self.reference_dict['reporters']['sobol']['confidence_intervals']['levels'], + 'number_of_samples': self.reference_dict['reporters']['sobol']['confidence_intervals']['replicates'], + 'inputs': inputs, + 'number_of_inputs': len(inputs), + 'outputs': [], + 'number_of_outputs': len(self.reference_dict['reporters']['stats'].keys())} + stat_outs = [] + for key in self.reference_dict['time_steps'][0]['stats'].keys(): + out = key.split(':') + #print(out);v=input('Continue?') + out = out[-2] + if out not in stat_outs: stat_outs.append(out) + self.statistics['outputs'] = stat_outs + return + + def get_sobol_1st(self): + keys = self.sobol['confidence_intervals'] + sobol_first = {} + + for output in self.sobol['outputs']: + sobol_first[output] = {} + + sobol_first[output]['mean'] = {} + j = 0 + for inp in self.sobol['inputs']: + sobol_first[output]['mean'][inp] = self.reference_dict["time_steps"][self.time_step]["sobol"][f"results_results:{output}:value"]["FIRST_ORDER"][0][j] + j +=1 + i = 0 + for key in keys: + j = 0 + sobol_first[output][key] = {} + for inp in self.sobol['inputs']: + sobol_first[output][key][inp] = self.reference_dict["time_steps"][self.time_step]["sobol"][f"results_results:{output}:value"]["FIRST_ORDER"][1][i][j] + j +=1 + i += 1 + + sobol_first_df = {} + for output in self.sobol['outputs']: + sobol_first_df[output] = pd.DataFrame(sobol_first[output]) + + self.sobol['FIRST_ORDER'] = sobol_first_df + return(sobol_first_df) + + def get_sobol_2nd(self): + keys = self.sobol['confidence_intervals'] + sobol_scd = {} + for output in self.sobol['outputs']: + sobol_scd[output] = {'mean':{}} + + j = 0 + for inp in self.inputs: + sobol_scd[output]['mean'][inp] = self.reference_dict["time_steps"][self.time_step]["sobol"][f"results_results:{output}:value"]["SECOND_ORDER"][0][j] + j += 1 + + i = 0 + for key in keys: + sobol_scd[output][key] = {} + j = 0 + for inp in self.inputs: + sobol_scd[output][key][inp] = self.reference_dict["time_steps"][self.time_step]["sobol"][f"results_results:{output}:value"]["SECOND_ORDER"][1][i][j] + j += 1 + i += 1 + + sobol_scd_df = {} + for output in self.sobol['outputs']: + sobol_scd_df[output] = pd.DataFrame(sobol_scd[output]) + + self.sobol['SECOND_ORDER'] = sobol_scd_df + return(sobol_scd_df) + + def get_sobol_total(self): + keys = self.sobol['confidence_intervals'] + sobol_tot = {} + + for output in self.sobol['outputs']: + sobol_tot[output] = {} + + sobol_tot[output]['mean'] = {} + j = 0 + for inp in self.sobol['inputs']: + sobol_tot[output]['mean'][inp] = self.reference_dict["time_steps"][self.time_step]["sobol"][f"results_results:{output}:value"]["TOTAL"][0][j] + j +=1 + i = 0 + for key in keys: + j = 0 + sobol_tot[output][key] = {} + for inp in self.sobol['inputs']: + sobol_tot[output][key][inp] = self.reference_dict["time_steps"][self.time_step]["sobol"][f"results_results:{output}:value"]["TOTAL"][1][i][j] + j +=1 + i += 1 + + sobol_tot_df = {} + for output in self.sobol['outputs']: + sobol_tot_df[output] = pd.DataFrame(sobol_tot[output]) + + self.sobol['TOTAL'] = sobol_tot_df + return(sobol_tot_df) + + def get_var_accounted_for(self): + if 'SECOND_ORDER' not in self.sobol.keys(): + self.get_sobol_2nd() + + self.sobol['var_accounted_for'] = {} + for output in self.sobol['outputs']: + var_acc_for = 0 + + for col in self.reference_dict["time_steps"][0]["sobol"][f"results_results:{output}:value"]["SECOND_ORDER"][0]: + for row in col: + var_acc_for += row + + self.sobol['var_accounted_for'][output] = var_acc_for + return(self.sobol['var_accounted_for']) + + def get_statistics(self): + self.statistics['statistics'] = {} + for output in self.statistics['outputs']: + self.statistics['statistics'][output] = {'value':{}, 'stddev':{}} + self.statistics['statistics'][output]['value']['mean'] = self.reference_dict['time_steps'][self.time_step]['stats'][f"results_results:{output}:value_MEAN"][0] + self.statistics['statistics'][output]['stddev']['mean'] = self.reference_dict['time_steps'][self.time_step]['stats'][f"results_results:{output}:value_STDDEV"][0] + i = 0 + for ci in self.statistics['confidence_intervals']: + self.statistics['statistics'][output]['value'][ci] = self.reference_dict['time_steps'][self.time_step]['stats'][f"results_results:{output}:value_MEAN"][1][i] + self.statistics['statistics'][output]['stddev'][ci] = self.reference_dict['time_steps'][self.time_step]['stats'][f"results_results:{output}:value_STDDEV"][1][i] + i += 1 + + for output in self.statistics['outputs']: + self.statistics['statistics'][output] = pd.DataFrame(self.statistics['statistics'][output]) + + return(self.statistics['statistics']) + + def get_matrix(self): + matrix = {'inputs':{}, 'outputs': {}} + # First, establish inputs and outputs + inputs = self.inputs + outputs = self.sobol['outputs'] + # Second, extract input matrix, store as dataframe + inp_matrix = {} + for inp in inputs: + try: + inp_matrix[inp] = self.reference_dict["time_steps"][self.time_step][f"matrix/{inp}"]["value"] + except: + try: + inp_matrix[inp] = self.reference_dict["time_steps"][self.time_step]["matrix"][inp] + except: + print(f"Could not find {inp} matrix for {self.path}") + + # Third, extract output matricies for each output, store as dictionary of {output: dataframe} + outp_matrix = {} + for outp in outputs: + outp_matrix[outp] = self.reference_dict["time_steps"][self.time_step]["matrix"][f"matrix_results:{outp}:value"] + + # package as attribute + self.matrix = {"inputs": pd.DataFrame(inp_matrix), "outputs": pd.DataFrame(outp_matrix)} + return(self.matrix) + + def save_object(self, path: str, *args, do_not_print: list[str] = [], suppress_save = False, **kwargs)-> str: + attributes = self.__dict__ + print_attributes = {} + for k,v in attributes.items(): + if k not in do_not_print: + print_attributes[k] = v + + if suppress_save: + return(print_attributes) + else: + with open(f'{path}.json', 'w+') as ftw: + ftw.write(json.dumps(json_serial_killer(print_attributes), indent=4)) + print(f"Object attributes written to {path}.json as a json file.") + return(print_attributes) + + def estimate_state_frequency(self, state_limit, output, use_matrix = True, method = 'gamma', pdf=False, plot = True, normalize = True, factor = 'min', function=None): + if use_matrix: + print(f"Using a frequency approach based on <{self.statistics['number_of_samples']}> samples.") + total_samples = len(self.matrix['outputs'][output]) + exceeding_samples = 0 + for i in self.matrix['outputs'][output]: + exceeding_samples += 1 if i > state_limit else 0 + + frequency = exceeding_samples/total_samples + print(frequency) + return(frequency) + + else: + print(f"Fitting data to a {method} probability distribution.") + data = np.asarray(self.matrix["outputs"][output]) + if normalize: + minima = np.min(data); mean = np.mean(data); median = np.median(data) + if method == 'mean': + data = np.divide(np.subtract(data, mean), mean) + state_limit = (state_limit - mean)/mean + elif method == 'min': + data = np.divide(np.subtract(data, minima), minima) + state_limit = (state_limit - minima)/minima + elif method == 'median': + data = np.divide(np.subtract(data, median), median) + state_limit = (state_limit - minima)/minima + frequency = 0 + + if method == 'gamma': + curve_args = scipy.gamma.fit(data) + if pdf and plot: + self.plot_histogram(labels=[output], plot = [output], normalize = normalize, density=True, factor=factor) + plt.plot(np.linspace(min(data)-50, max(data)+50, num=len(data)), scipy.gamma.pdf(np.linspace(min(data)-50, max(data)+50, num=len(data)), *curve_args)) + elif plot: + self.plot_percentiles(labels=[output], plot = [output], normalize = normalize, flip = True) + plt.plot(np.linspace(min(data), max(data), num=len(data)), scipy.gamma.cdf(np.linspace(min(data), max(data), num=len(data)), *curve_args)) + frequency = scipy.gamma.sf(state_limit, *curve_args) + chi_squared, p_value = scipy.chisquare(scipy.relfreq(data, numbins=int(0.01*len(data)))[0], + scipy.relfreq(scipy.gamma.pdf(np.linspace(min(data), max(data), num=len(data)), *curve_args), numbins=int(0.01*len(data)))[0], axis=0) + print(f'Chi-squared: {chi_squared}\nP-value: {p_value} ({p_value-1})') + + + elif method == 'normal': + curve_args = scipy.norm.fit(data) + if pdf and plot: + self.plot_histogram(labels=[output], plot = [output], normalize = normalize, density=True, factor=factor) + plt.plot(np.linspace(min(data), max(data), num=len(data)), scipy.norm.pdf(np.linspace(min(data), max(data), num=len(data)), *curve_args)) + elif plot: + self.plot_percentiles(labels=[output], plot = [output], normalize = normalize, flip = True) + plt.plot(np.linspace(min(data), max(data), num=len(data)), scipy.norm.cdf(np.linspace(min(data), max(data), num=len(data)), *curve_args)) + frequency = scipy.norm.sf(state_limit, *curve_args) + chi_squared, p_value = scipy.chisquare(scipy.relfreq(data, numbins=int(0.01*len(data)))[0], + scipy.relfreq(scipy.norm.pdf(np.linspace(min(data), max(data), num=len(data)), *curve_args), numbins=int(0.01*len(data)))[0], axis=0) + print(f'Chi-squared: {chi_squared}\nP-value: {p_value} ({p_value-1})') + + elif method == 'lognormal': + curve_args = scipy.lognorm.fit(data) + if pdf and plot: + self.plot_histogram(labels=[output], plot = [output], normalize = normalize, density=True, factor=factor) + plt.plot(np.linspace(min(data), max(data)), scipy.lognorm.pdf(np.linspace(min(data), max(data)), *curve_args)) + elif plot: + self.plot_percentiles(labels=[output], plot = [output], normalize = normalize, flip = True) + plt.plot(np.linspace(min(data), max(data)), scipy.lognorm.cdf(np.linspace(min(data), max(data)), *curve_args)) + frequency = scipy.lognorm.sf(state_limit, *curve_args) + chi_squared, p_value = scipy.chisquare(scipy.relfreq(data, numbins=int(0.01*len(data)))[0], + scipy.relfreq(scipy.lognorm.pdf(np.linspace(min(data), max(data), num=len(data)), *curve_args), numbins=int(0.01*len(data)))[0], axis=0) + print(f'Chi-squared: {chi_squared}\nP-value: {p_value} ({p_value-1})') + + elif method == 'beta': + curve_args = scipy.beta.fit(data) + if pdf and plot: + self.plot_histogram(labels=[output], plot = [output], normalize = normalize, density=True, factor=factor) + plt.plot(np.linspace(min(data), max(data)), scipy.beta.pdf(np.linspace(min(data), max(data)), *curve_args)) + elif plot: + self.plot_percentiles(labels=[output], plot = [output], normalize = normalize, flip = True) + plt.plot(np.linspace(min(data), max(data)), scipy.beta.cdf(np.linspace(min(data), max(data)), *curve_args)) + frequency = scipy.beta.sf(state_limit, *curve_args) + chi_squared, p_value = scipy.chisquare(scipy.relfreq(data, numbins=int(0.01*len(data)))[0], + scipy.relfreq(scipy.beta.pdf(np.linspace(min(data), max(data), num=len(data)), *curve_args), numbins=int(0.01*len(data)))[0], axis=0) + print(f'Chi-squared: {chi_squared}\nP-value: {p_value} ({p_value-1})') + + elif method == 'tukeylambda': + curve_args = scipy.tukeylambda.fit(data) + if pdf and plot: + self.plot_histogram(labels=[output], plot = [output], normalize = False, density=True, factor=factor) + plt.plot(np.linspace(min(data), max(data)), scipy.tukeylambda.pdf(np.linspace(min(data), max(data)), *curve_args)) + elif plot: + self.plot_percentiles(labels=[output], plot = [output], normalize = False, flip = True) + plt.plot(np.linspace(min(data), max(data)), scipy.tukeylambda.cdf(np.linspace(min(data), max(data)), *curve_args)) + frequency = scipy.tukeylambda.sf(state_limit, *curve_args) + chi_squared, p_value = scipy.chisquare(scipy.relfreq(data, numbins=int(0.01*len(data)))[0], + scipy.relfreq(scipy.tukeylambda.pdf(np.linspace(min(data), max(data), num=len(data)), *curve_args), numbins=int(0.01*len(data)))[0], axis=0) + print(f'Chi-squared: {chi_squared}\nP-value: {p_value} ({p_value-1})') + + elif method == 'power': + curve_args = scipy.powerlaw.fit(data) + if pdf and plot: + self.plot_histogram(labels=[output], plot = [output], normalize = False, density=True, factor=factor) + plt.plot(np.linspace(min(data), max(data)), scipy.powerlaw.pdf(np.linspace(min(data), max(data)), *curve_args)) + elif plot: + self.plot_percentiles(labels=[output], plot = [output], normalize = False, flip = True) + plt.plot(np.linspace(min(data), max(data)), scipy.powerlaw.cdf(np.linspace(min(data), max(data)), *curve_args)) + frequency = scipy.powerlaw.sf(state_limit, *curve_args) + chi_squared, p_value = scipy.chisquare(scipy.relfreq(data, numbins=int(0.01*len(data)))[0], + scipy.relfreq(scipy.powerlaw.pdf(np.linspace(min(data), max(data), num=len(data)), *curve_args), numbins=int(0.01*len(data)))[0], axis=0) + print(f'Chi-squared: {chi_squared}\nP-value: {p_value} ({p_value-1})') + + elif method == 'exponential': + curve_args = scipy.expon.fit(data) + if pdf and plot: + self.plot_histogram(labels=[output], plot = [output], normalize = False, density=True, factor=factor) + plt.plot(np.linspace(min(data), max(data)), scipy.expon.pdf(np.linspace(min(data), max(data)), *curve_args)) + elif plot: + self.plot_percentiles(labels=[output], plot = [output], normalize = False, flip = True) + plt.plot(np.linspace(min(data), max(data)), scipy.expon.cdf(np.linspace(min(data), max(data)), *curve_args)) + frequency = scipy.expon.sf(state_limit, *curve_args) + chi_squared, p_value = scipy.chisquare(scipy.relfreq(data, numbins=int(0.01*len(data)))[0], + scipy.relfreq(scipy.expon.pdf(np.linspace(min(data), max(data), num=len(data)), *curve_args), numbins=int(0.01*len(data)))[0], axis=0) + print(f'Chi-squared: {chi_squared}\nP-value: {p_value} ({p_value-1})') + + elif method == 'halfcauchy': + curve_args = scipy.halfcauchy.fit(data) + if pdf and plot: + self.plot_histogram(labels=[output], plot = [output], normalize = False, density=True, factor=factor) + plt.plot(np.linspace(min(data), max(data)), scipy.halfcauchy.pdf(np.linspace(min(data), max(data)), *curve_args)) + elif plot: + self.plot_percentiles(labels=[output], plot = [output], normalize = False, flip = True) + plt.plot(np.linspace(min(data), max(data)), scipy.halfcauchy.cdf(np.linspace(min(data), max(data)), *curve_args)) + frequency = scipy.halfcauchy.sf(state_limit, *curve_args) + chi_squared, p_value = scipy.chisquare(scipy.relfreq(data, numbins=int(0.01*len(data)))[0], + scipy.relfreq(scipy.halfcauchy.pdf(np.linspace(min(data), max(data), num=len(data)), *curve_args), numbins=int(0.01*len(data)))[0], axis=0) + print(f'Chi-squared: {chi_squared}\nP-value: {p_value} ({p_value-1})') + + elif method == 'general': + curve_args = function.fit(data) + if pdf and plot: + self.plot_histogram(labels=[output], plot = [output], normalize = False, density=True, factor=factor) + plt.plot(np.linspace(min(data), max(data)), function.pdf(np.linspace(min(data), max(data)), *curve_args)) + elif plot: + self.plot_percentiles(labels=[output], plot = [output], normalize = False, flip = True) + plt.plot(np.linspace(min(data), max(data)), function.cdf(np.linspace(min(data), max(data)), *curve_args)) + frequency = function.sf(state_limit, *curve_args) + chi_squared, p_value = scipy.chisquare(scipy.relfreq(data, numbins=int(0.01*len(data)))[0], + scipy.relfreq(function.pdf(np.linspace(min(data), max(data), num=len(data)), *curve_args), numbins=int(0.01*len(data)))[0], axis=0) + print(f'Chi-squared: {chi_squared}\nP-value: {p_value} ({p_value-1})') + + print(f"Frequency: {frequency}") + return(frequency, curve_args) + + def plot_percentiles(self, labels = [], plot = [], flip = False, normalize = True, factor = 'mean', + xlabel = None, ylabel = None, pdf= False): + # Next: formulate dictionary of {output: {percentiles: [], values: []}} + plot = plot if plot else self.statistics['outputs'] + xlabel = 'Percentile' if not xlabel else xlabel; ylabel = 'Normalized Value' if not ylabel else ylabel + flip = True if pdf else flip + plotting_dict = {} + means = {} + for output in plot: + means[output] = list(self.statistics['statistics'][output]['value'])[0] + plotting_dict[output] = {'percentiles': self.statistics['confidence_intervals'], + 'values': list(self.statistics['statistics'][output]['value'])[1:]} + # First: normalize confidence intervals with respect to the mean + for k, v in plotting_dict.items(): + #mean = plotting_dict[k]['values'][0] + if normalize: + plotting_dict[k]['values'] = [i/means[k] for i in v['values']] + # Plot all using lineplot, markers, and legend + fig, ax = plt.subplots(1,1, figsize = (6,4)) + ax.set_title('Cumulative Distributions'); ax.set_xlabel(xlabel); ax.set_ylabel(ylabel) + ax.set_facecolor('aliceblue');ax.grid(visible = True, which = 'both', axis = 'both', color = 'gainsboro', linestyle = ':') + markers = [',','.','o','^','v','<','>','s','o','P','+','x','D'] + i = 0 + for label, data in plotting_dict.items(): + if flip: + percentiles = data['percentiles'] if not pdf else [0] + [(data['percentiles'][i]-data['percentiles'][i-1])/(data['values'][i]-data['values'][i-1]) for i in range(1, len(data['percentiles']))] + plotting_dict[label]['percentiles'] = percentiles + ax.plot(data['values'], percentiles, label = label, marker = markers[i]) + ax.set_xlabel('Normalized Value'); ax.set_ylabel('Percentile') + else: + ax.plot(data['percentiles'], data['values'], label = label, marker = markers[i]) + i += 1 + if labels: + ax.legend(labels = labels, loc = 'upper center', ncols = 3, fontsize = 'small', bbox_to_anchor = (0.5, -0.15)) + else: + ax.legend(loc = 'upper center', ncols = 3, fontsize = 'small', bbox_to_anchor = (0.5, -0.15)) + return(plotting_dict) + + def plot_histogram(self, labels = [], plot = [], density=False, normalize = True, factor = 'mean', + xlabel = None, ylabel = None, plot_inp=False): + if plot_inp: + plot = list(self.matrix['inputs'].columns) if not plot else plot + else: + plot = plot if plot else list(self.matrix["outputs"].columns) + + labels = labels if labels else plot + ylabel = 'Frequency' if not ylabel else ylabel + histogram_dict = {} + + for outp in plot: + histogram_dict[outp] = list(self.matrix["inputs"][outp]) if plot_inp else list(self.matrix["outputs"][outp]) + if normalize: + if factor == 'mean': + xlabel = '(X-Mean)/Mean' if not xlabel else xlabel + mean = np.mean(histogram_dict[outp]) + histogram_dict[outp] = [(i/mean - 1) for i in histogram_dict[outp]] + elif factor == 'min': + xlabel = '(X-Min)/Min' if not xlabel else xlabel + minima = np.min(histogram_dict[outp]) + histogram_dict[outp] = [((i-minima)/minima) for i in histogram_dict[outp]] + elif factor == 'median': + xlabel = '(X-Median)/Median' if not xlabel else xlabel + median = np.median(histogram_dict[outp]) + histogram_dict[outp] = [((i-median)/median) for i in histogram_dict[outp]] + + fig, ax = plt.subplots(1,1, figsize = (6,4)) + ax.set_title('Histogram Distributions'); ax.set_xlabel(xlabel); ax.set_ylabel(ylabel) + ax.set_facecolor('aliceblue');ax.grid(visible = True, which = 'both', axis = 'both', color = 'gainsboro', linestyle = ':') + + for outp, hist in histogram_dict.items(): + ax.hist(hist, fill=False, histtype="step", stacked=True, label=outp, bins=int(len(hist)/100), density = density) + if labels: + ax.legend(labels = labels, loc = 'upper center', ncols = 3, fontsize = 'small', bbox_to_anchor = (0.5, -0.15)) + else: + ax.legend(loc = 'upper center', ncols = 3, fontsize = 'small', bbox_to_anchor = (0.5, -0.15)) + return() + + def plot_violin(self, labels = [], plot = [], normalize = True, factor = 'mean', + ylabel = None, xlabel = None): + plot = plot if plot else self.matrix["outputs"] + labels = labels if labels else list(self.matrix['outputs'].index) + ylabel = '(X-Mean)/Mean' if not ylabel else ylabel + vio_dict = {} + + for outp in plot: + vio_dict[outp] = list(self.matrix["outputs"][outp]) + if normalize: + if factor == 'mean': + xlabel = '(X-Mean)/Mean' if not xlabel else xlabel + mean = np.mean(vio_dict[outp]) + vio_dict[outp] = [(i/mean - 1) for i in vio_dict[outp]] + elif factor == 'min': + xlabel = '(X-Min)/Min' if not xlabel else xlabel + minima = np.min(vio_dict[outp]) + vio_dict[outp] = [((i-minima)/minima) for i in vio_dict[outp]] + elif factor == 'median': + xlabel = '(X-Median)/Median' if not xlabel else xlabel + median = np.median(vio_dict[outp]) + vio_dict[outp] = [((i-median)/median) for i in vio_dict[outp]] + + fig, ax = plt.subplots(1,1, figsize = (6,4)) + ax.set_title('Output Distributions'); ax.set_ylabel(ylabel) + ax.set_facecolor('aliceblue');ax.grid(visible = True, which = 'both', axis = 'both', color = 'gainsboro', linestyle = ':') + + positions = [i+1 for i in range(0, len(vio_dict.keys()))] + viola = ax.violinplot(vio_dict.values(), positions, showmeans=True, showextrema=True) + + i = 0 + for part in viola['bodies']: + part.set_facecolor(colors[i]); part.set_edgecolor('black') + i += 1 + + if labels: + ax.legend(labels = labels, loc = 'upper center', ncols = 3, fontsize = 'small', bbox_to_anchor = (0.5, -0.15)) + else: + ax.legend(loc = 'upper center', ncols = 3, fontsize = 'small', bbox_to_anchor = (0.5, -0.15)) + return() + + def plot_sobol(self, outp = '', order = 'FIRST_ORDER', inp_labels = ['Mobile T Flux', 'Cool. T Conc.','Heat Flux','Cool. Temp.'], + outp_labels=['H3 Permeation','H3 FLux', 'Coo. Heat Flux','Cu Max T','CCZ Max T','W Max T','H3 Retention'], + lci = 0.05, uci = 0.95): + + fig, ax = plt.subplots(len(self.inputs),1, figsize = (8,8)) + ax[0].set_title(f'{order} Sensitivity Indicies') + x = [j+1 for j in range(0, len(self.sobol['outputs']))] + + i = 0 + for inp in self.inputs: + ax[i].set_ylabel(f'{inp_labels[i]}');#ax[i].set_ylim(-0.25,1.25) + ax[i].set_facecolor('aliceblue');ax[i].grid(visible = True, which = 'both', axis = 'both', color = 'gainsboro', linestyle = ':') + + y = np.asarray([self.sobol[order][outp]['mean'][inp] for outp in self.sobol['outputs']]); #print(y) + n_err = np.asarray([self.sobol[order][outp][lci][inp] for outp in self.sobol['outputs']]); n_err = np.abs(np.subtract(n_err, y)) + p_err = np.asarray([self.sobol[order][outp][uci][inp] for outp in self.sobol['outputs']]); p_err = np.abs(np.subtract(p_err, y)) + + ax[i].errorbar(x, y, yerr = [n_err, p_err], linewidth=0, elinewidth=1, marker='o') + i += 1 + + # if i == 1: + # for j in range(0, len(self.sobol['outputs'])): + # print(f"{y[j]}:{n_err[j]}:{p_err[j]}") + + + x_string = '' + k = 1 + for j in outp_labels: + x_string += f"{k} = {j}\n" + k += 1 + ax[-1].set_xlabel(x_string, loc='left') + #print(self.sobol[order].keys()) + return() + + def sobol_heat_map(self, outp = 'max_temperature_CuCrZr', outp_label = 'Tritium Permeation', + xlabels = ['Tritium Flux','Coolant Tritium \nConcentration','Heat Flux','Coolant Temperature'], + ylabels = ['Tritium Flux','Coolant Tritium \nConcentration','Heat Flux','Coolant Temperature']): + # Construct new dataframe + new_df = {} + operating_df = self.sobol['SECOND_ORDER'][outp]; rows = list(operating_df.index) + for row in rows: + new_df[row] = {} + i = 0 + for subrow in rows: + new_df[row][subrow] = operating_df['mean'][row][i] + i += 1 + new_df = pd.DataFrame(new_df) + seaborn.heatmap(new_df, cmap='bwr', xticklabels=xlabels, yticklabels=ylabels, label=outp_label, annot=True, vmin=-1.0, vmax=2.0) + return(new_df) + + def confidence_interval_heatmap(self, order = 'FIRST_ORDER', outp = 'max_temperature_CuCrZr', outp_label = 'CCZ Maximum Temperature', + xlabels = ['Tritium Flux','Coolant Tritium \nConcentration','Heat Flux','Coolant Temperature'], + min_cf = 0.05, max_cf = 0.95, heatmap = True, title = None): + # Construct new dataframe + ylabels = [min_cf, 'mean', max_cf]; title = title if title else outp_label + new_df = {} + operating_df = self.sobol[order][outp]; rows = list(operating_df.index) + for row in rows: + new_df[row] = {min_cf: operating_df[min_cf][row], 'Mean': operating_df['mean'][row], max_cf: operating_df[max_cf][row]} + new_df = pd.DataFrame(new_df) + if heatmap: + seaborn.heatmap(new_df, cmap='bwr', xticklabels=xlabels, yticklabels=ylabels, label=outp_label, annot=True) + else: + styler = new_df.style \ + .format(precision=3) + #new_df + return(new_df) + + def confidence_interval_table(self, outp = 'max_temperature_CuCrZr', outp_label = 'CCZ Maximum Temperature', + xlabels = ['Tritium Flux','Coolant Tritium \nConcentration','Heat Flux','Coolant Temperature'], + min_cf = 0.05, max_cf = 0.95, precision = 2): + xlabels = xlabels if xlabels else self.inputs + col_dict = {} + for k in range(0, len(self.inputs)): + col_dict[self.inputs[k]] = xlabels[k] + + df = {} + for inp in self.inputs: + df[inp] = {} + for n_inp in range(0, len(self.inputs)): + df[inp][n_inp] ='' + df[inp][n_inp] += f"{np.round(self.sobol["SECOND_ORDER"][outp]["mean"][inp][n_inp], precision)} " + df[inp][n_inp] += f"({np.round(self.sobol["SECOND_ORDER"][outp][min_cf][inp][n_inp], precision)}," + df[inp][n_inp] += f"{np.round(self.sobol["SECOND_ORDER"][outp][max_cf][inp][n_inp], precision)})" + df = pd.DataFrame(df) + df.style.background_gradient(cmap=seaborn.light_palette("blue", as_cmap=True)) + df.style.set_caption(f"{outp} Sensitivity Indicies") + df.style.set_properties(**{'text-align': 'center'}) + df.index = xlabels; df.rename(columns=col_dict, inplace=True) + + HTML(df.to_html(index=False)) + return(df.style) + + def plot_inp_out(self, outp, inp, xlabel = None, ylabel = None, curve_fit_function = lambda x, a, b: a +b*x, guess_args = [0.0, 1.0]): + xlabel = inp if not xlabel else xlabel; ylabel = outp if not ylabel else ylabel; + + fig, ax = plt.subplots(1,1, figsize = (6,4)) + ax.set_title(f'{ylabel} vs {xlabel}'); ax.set_ylabel(ylabel); ax.set_xlabel(xlabel) + ax.set_facecolor('aliceblue');ax.grid(visible = True, which = 'both', axis = 'both', color = 'gainsboro', linestyle = ':') + + x_vector = self.matrix['inputs'][inp] + y_vector = self.matrix['outputs'][outp] + ax.scatter(x_vector, y_vector, marker='o') + + if curve_fit_function: + fit_args, pcov = optimize.curve_fit(curve_fit_function, x_vector, y_vector, p0 = guess_args) + print(f"Optimized parameters: {fit_args}") + y_pred = curve_fit_function(x_vector, *fit_args) + + ss_tot = np.sum((y_vector - np.mean(y_vector))**2) + ss_res = np.sum((y_vector - y_pred)**2) + r_sqr = 1 - ss_res/ss_tot + + print(f"R2: {r_sqr}") + + return() + +def Polynomial_Kernel(vector, *args, **kwargs): + i = 0 + poly_vector = np.zeros(vector.shape) + for arg in args: + poly_vector += np.multiply(arg, np.power(vector, i)) + i += 1 + return(poly_vector) + +def Exponential_Kernel(vector, *args, pre_exponential_factor = 0, frequency = 0, power = -1, **kwargs): + exponential_vector = pre_exponential_factor*np.exp((frequency*np.power(vector, power))) + return(exponential_vector) + +do_these = ['coolant_heat_flux', 'max_temperature_Cu','max_temperature_CuCrZr', 'max_temperature_W', 'total_retention'] +labels = ["Coolant Heat Flux","Cu Max T","CCZ Max T","W Max T", "Total Retention"] +A1 = SOBOL("Sobol Studies/Accident 1 Sobol/Accident_1_Sobol_out_v2.json") +A2 = SOBOL("Sobol Studies/Accident 2 Sobol/Accident_2_Sobol_out_v2.json") +SS = SOBOL("Sobol Studies/Steady Sobol v1/Sobol_v4_out.json") +A1.get_matrix(); A2.get_matrix(); SS.get_matrix() +A1.get_sobol_1st(); A2.get_sobol_1st(); SS.get_sobol_1st() +A1.get_sobol_2nd(); A2.get_sobol_2nd(); SS.get_sobol_2nd() +A2.get_sobol_total(); A2.get_sobol_total(); SS.get_sobol_total() +A1.get_statistics(), A2.get_statistics(); SS.get_statistics() + +###S4 = SOBOL("Sobol_v4_out.json") +###S4.get_matrix();S4.get_sobol_1st();S4.get_sobol_2nd();S4.get_sobol_total();S4.get_statistics();S4.get_var_accounted_for() +#S4.plot_sobol(outp='F_permeation') +#S4.sobol_heat_map() +# ["Permeation","Tritium Exit Flux","Coolant Heat Flux","Cu Max T","CCZ Max T","W Max T", "Total Retention"] +# ['F_permeation','Scaled_Tritium_Flux','coolant_heat_flux', 'max_temperature_Cu','max_temperature_CuCrZr', 'max_temperature_W', 'total_retention'] + +#S4.plot_percentiles(labels=do_these, plot=do_these, flip=True) +#S4.plot_histogram(labels=do_these, plot=do_these) +#S4.plot_violin(labels=labels, plot=do_these) +#S4.estimate_state_frequency(623.15, 'max_temperature_CuCrZr') +#sob = SOBOL('Sobol_out.json') +#sob.get_statistics() +#print(sob.plot_percentiles(labels = ['Permeation','Coolant Tritium Flux','Coolant Heat Flux', +# 'Cu Max T','CCZ Max T','W Max T','Total Retention'])) + +##fnsf = json_to_RESULTS('FNSF_results_4.json') +##fnsf.plot_time_series_vectors('y',['temperature','C_total'],'line', +## display_rate=0.0001, xlabel = "Distance from Coolant Center [mm]", ylabels = ['Temperature','Tritium Concentration [g/m3]'], titles = ['FNSF Divertor Monoblock', '']) +# +# path_to_home = '../../../../' +# nominal_divertor = pd.read_csv(path_to_home+'Downloads/DIVMON_TEST_out_line_2747.csv') +# ss_divertor = pd.read_csv(path_to_home+'Downloads/convergence_testing_out_line_0521.csv') + +# nominal_centerline_temperature = np.asarray(nominal_divertor['temperature'].to_list()) +# steady_state_centerline_temperature = np.asarray(ss_divertor['temperature'].to_list()) + +# centerline_temperature_difference = np.subtract(nominal_centerline_temperature, steady_state_centerline_temperature) + +# nominal_flux = np.asarray(nominal_divertor['flux_y'].to_list()) +# steady_flux = np.asarray(ss_divertor['flux_y'].to_list()) + + +# centerline_flux_difference = np.divide(np.subtract(nominal_flux, steady_flux), nominal_flux) + +# plt.plot() diff --git a/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/transients_inputs.png b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/transients_inputs.png new file mode 100644 index 000000000..ac8782673 Binary files /dev/null and b/doc/content/examples/figures/divertor_monoblock_sensitivity_figures/transients_inputs.png differ diff --git a/doc/content/examples/index.md b/doc/content/examples/index.md index 52a39a06a..4afc878d0 100644 --- a/doc/content/examples/index.md +++ b/doc/content/examples/index.md @@ -21,6 +21,7 @@ As examples, we propose two fuel cycle models. TMAP8 is used to model tritium transport in a divertor monoblock to elucidate the effects of pulsed operation (up to fifty 1600-second plasma discharge and cool-down cycles) on the tritium in-vessel inventory source term and ex-vessel release term (i.e., tritium retention and permeation) for safety analysis. This example reproduces the results presented in [!cite](Shimada2024114438). +A series of sensitivity studies were performed on the [Divertor Monoblock](examples/divertor_monoblock/index.md) model including: (1)steady-pulse operation, (2) a shutdown transient, and (3) a ELM transient [Divertor Monoblock Sensitivity](divertor_monoblock/sensitivity.md). ## [Pore-Scale Tritium Transport in Imported Microstructures](examples/pore_scale_transport/index.md) diff --git a/test/tests/divertor_monoblock/elm_transient_runner.i b/test/tests/divertor_monoblock/elm_transient_runner.i new file mode 100644 index 000000000..90e8dcb33 --- /dev/null +++ b/test/tests/divertor_monoblock/elm_transient_runner.i @@ -0,0 +1,1205 @@ +### Nomenclatures # Nelson S. Comments/Annotations +### +### C_mobile_j mobile H concentration in "j" material, where j = CuCrZr, Cu, W +### C_trapped_j trapped H concentration in "j" material, where j = CuCrZr, Cu, W +### C_total_j total H concentration in "j" material, where j = CuCrZr, Cu, W +### +### S_empty_j empty site concentration in "j" material, where j = CuCrZr, Cu, W +### S_trapped_j trapped site concentration in "j" material, where j = CuCrZr, Cu, W +### S_total_j total site H concentration in "j" material, where j = CuCrZr, Cu, W +### +### F_permeation permeation flux +### F_recombination recombination flux +### +### Sc_ Scaled +### Int_ Integrated +### ScInt_ Scaled and integrated # Nelson S. Comments/Annotations +### CHANGES ### +### 1. Added additional outputs for: +### a. Average and maximum temperatures for all materials +### b. Maximum tritium concentrations for all materials +### c. Temperature and tritium flux along block boundaries +### 2. Converted to continous-pulse for reduced computation time +### a. 1 Pulse = (500s)*(tritium flux) fluence +### b. + +### VARIABLES ### +# PRIOR +inter_pwr = 10e6 +coolant_temp = 552 +elm_value = 1147e6 +elm_duration = 1.32e-3 +W_cond_factor = 1.0 +tungsten_atomic_density = ${units 6.338e28 m^-3} +# # BOUNDARY CONDITIONS +#incident_tritium_flux = 7.90e-13 +#incident_heat_flux = 1.0e7 +#coolant_temperature = 552 +#coolant_tritium_concentration = 1.0e-18 +## The MESH forms the spatial description of the problem +## Mesh generated by ConcentricCircleMeshGenerator and saved as "2DMonoblock.e" +[Controls/stochastic] # Sends data to Stochastic + type = SamplerReceiver +[] +[Mesh] # Begins the Mesh description block; the mesh spatially describes the problem, based on blocks (subdomains), meshes (geometries), nodes, etc.. + [ccmg] # name of the mesh + type = ConcentricCircleMeshGenerator # type of mesh generator following parameters will be dependent on the type + num_sectors = 36 # azimuthmal (angle) sections/divisions + rings = '1 30 20 110' # number of divisions/meshing between defined (next line) radii -> creates 4 (N+1) blocks (I don't know why this is made for radii and not azimuthmal sections) + radii = '${units 6 mm -> m} ${units 7.5 mm -> m} ${units 8.5 mm -> m}' # radii deliminating different blocks/regions/mesh spacings (mesh generator denotes blocks by radii, this line defines 3 blocks) + has_outer_square = on # creates a square boundary for the problem, using the pitch (next line) as the width/height (4th block is generated here) + pitch = ${units 28 mm -> m} # square width/height of problem + portion = left_half # only generate left (-14 0.1, but it creates negative C_mobile _Cu with << 0.1 + # jump_prop_name = solubility_ratio_4to3 + jump_prop_name = solubility_ratio # material property used in variable jump + boundary = '4to3' # boundary to apply the interface condition to. + [] + [tied_3to2] + type = ADPenaltyInterfaceDiffusion + variable = C_mobile_Cu + neighbor_var = C_mobile_CuCrZr + penalty = 0.05 + # it will not converge with > 0.1, but it creates negative C_mobile _Cu with << 0.1 + # jump_prop_name = solubility_ratio_3to2 + jump_prop_name = solubility_ratio + boundary = '3to2' + [] +[] + +[NodalKernels] # Nodal Kernels are used to solve equations that specifically belong on a node. + ############################## NodalKernels for W (block = 4) + [time_W] + type = TimeDerivativeNodalKernel # This kernel: forms the contribution to the residual and jacobian of the timer derivative term from an ODE being solves at ALL nodes: https://mooseframework.inl.gov/source/nodalkernels/TimeDerivativeNodalKernel.html + variable = C_trapped_W # variable the residual operates on + [] + [trapping_W] + type = TrappingNodalKernel # Defines time dependent trapping rate + variable = C_trapped_W # variable operating on + temperature = temperature + alpha_t = 2.75e11 # 1e15 # Trapping rate coefficient + N = 1.0e0 # = (1e0) x (${tungsten_atomic_density} #/m^3) # Host atom density + # Ct0 = 1.0e-4 # E.A. Hodille et al 2021 Nucl. Fusion 61 126003, trap 1 + Ct0 = 1.0e-4 # E.A. Hodille et al 2021 Nucl. Fusion 61 1260033, trap 2 # Host trapping fraction + trap_per_free = 1.0e0 # 1.0e1 # Estimate ratio of trapped to free species + mobile_concentration = 'C_mobile_W' # Corresponding mobile species variable + extra_vector_tags = ref + [] + [release_W] + type = ReleasingNodalKernel # Release rate of trapped species: https://mooseframework.inl.gov/TMAP8/source/nodal_kernels/ReleasingNodalKernel.html + alpha_r = 8.4e12 # 1.0e13 + temperature = temperature + # detrapping_energy = 9863.9 # = 0.85 eV E.A. Hodille et al 2021 Nucl. Fusion 61 126003, trap 1 + detrapping_energy = 11604.6 # Units of kelvin. + # = 1.00 eV E.A. Hodille et al 2021 Nucl. Fusion 61 126003, trap 2 + variable = C_trapped_W + [] + ############################## NodalKernels for Cu (block = 3) + [time_Cu] + type = TimeDerivativeNodalKernel + variable = C_trapped_Cu + [] + [trapping_Cu] + type = TrappingNodalKernel + variable = C_trapped_Cu + temperature = temperature + alpha_t = 2.75e11 # 1e15 + N = 1.0e0 # = ${tungsten_atomic_density} #/m^3 (W lattice density) + Ct0 = 5.0e-5 # R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 3 + trap_per_free = 1.0e0 # 1.0e1 + mobile_concentration = 'C_mobile_Cu' + extra_vector_tags = ref + [] + [release_Cu] + type = ReleasingNodalKernel + alpha_r = 8.4e12 # 1.0e13 + temperature = temperature + detrapping_energy = 5802.3 # = 0.50eV R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 3 + variable = C_trapped_Cu + [] + ############################## NodalKernels for CuCrZr (block = 2) + [time_CuCrZr] + type = TimeDerivativeNodalKernel + variable = C_trapped_CuCrZr + [] + [trapping_CuCrZr] + type = TrappingNodalKernel + variable = C_trapped_CuCrZr + temperature = temperature + alpha_t = 2.75e11 # 1e15 + N = 1.0e0 # = ${tungsten_atomic_density} #/m^3 (W lattice density) + Ct0 = 5.0e-5 # R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 4 + # Ct0 = 4.0e-2 # R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 5 + trap_per_free = 1.0e0 # 1.0e1 + mobile_concentration = 'C_mobile_CuCrZr' + extra_vector_tags = ref + [] + [release_CuCrZr] + type = ReleasingNodalKernel + alpha_r = 8.4e12 # 1.0e13 + temperature = temperature + detrapping_energy = 5802.3 # = 0.50eV R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 4 + # detrapping_energy = 9631.8 # = 0.83 eV R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 5 + variable = C_trapped_CuCrZr + [] +[] + +[BCs] # Begin problem boundary conditions + [C_mob_W_top_flux] # User defined name + type = FunctionNeumannBC # PDE flux boundary condition: https://mooseframework.inl.gov/source/bcs/FunctionNeumannBC.html + variable = C_mobile_W # Applicable variable + function = mobile_flux_bc_func + boundary = 'top' # boundary name to apply condition to + [] + [mobile_tube] + type = DirichletBC # Constant boundary condition, specific value for the applicable variable at the specified boundary + variable = C_mobile_CuCrZr # controls tritium concentration at inner tube location (un-normalized concentration) -> exact units unknown + value = 1.0e-18 # Value at boundary + boundary = '2to1' + [] + [temp_top] + type = FunctionNeumannBC # Applies to the variable, temperature, defined by the temp_flux_bc_func function. + variable = temperature # FunctionNeumannBC + function = temp_flux_bc_func + boundary = 'top' + [] + [temp_tube] + type = DirichletBC # Constant BC, but the exact value is defined by a later function. 'Constant' is allowed to be spatially and temporally dependent: https://mooseframework.inl.gov/source/bcs/FunctionDirichletBC.html + variable = temperature # specifies inner tube temperature at the boundary: 2to1 (CuCrZr to H20) + value = ${coolant_temp} + boundary = '2to1' + [] +[] +[Functions] # Specific physical relationship, allowed to be used by multiple objects + ### Maximum mobile flux of 7.90e-13 at the top surface (1.0e-4 [m]) + ### 1.80e23/m^2-s = (5.0e23/m^2-s) *(1-0.999) = (7.90e-13)*(${tungsten_atomic_density})/(1.0e-4) at steady state + [mobile_flux_bc_func] + type = ParsedFunction + expression = """if(t<2e2, (${inter_pwr}*7.9e-13/1e7), + if(t<(2e2+${elm_duration}*1/3), + (${inter_pwr}*7.9e-13/1e7)+(t-2e2)/(${elm_duration}*1/3)*((${elm_value}*7.9e-13/1e7)-(${inter_pwr}*7.9e-13/1e7)), + if(t<(2e2+(${elm_duration}*1/3)+(${elm_duration}*2/3)), + (${elm_value}*7.9e-13/1e7)-(t-2e2-(${elm_duration}*1/3))/(${elm_duration}*2/3)*((${elm_value}*7.9e-13/1e7)-(${inter_pwr}*7.9e-13/1e7)), (${inter_pwr}*7.9e-13/1e7))))""" + [] + ### Heat flux of 10MW/m^2 at steady state + [temp_flux_bc_func] + type = ParsedFunction + expression = """if(t<2e2, ${inter_pwr}, + if(t<(2e2+${elm_duration}*1/3), + ${inter_pwr}+(t-2e2)/(${elm_duration}*1/3)*(${elm_value}-${inter_pwr}), + if(t<(2e2+(${elm_duration}*1/3)+(${elm_duration}*2/3)), + ${elm_value}-(t-2e2-(${elm_duration}*1/3))/(${elm_duration}*2/3)*(${elm_value}-${inter_pwr}), ${inter_pwr})))""" + [] + [timestep_function] + type = ParsedFunction + expression = """if(t<2e2, 500, + if(t<(2e2+${elm_duration}*1/3+${elm_duration}*2/3), 0.0001, 500))""" + [] + [temp_ss] + type = ParsedFunction + expression = "-1.59786e4*x^2 -1.11629611e4*x + 4.84297313e2 + 1.9491599e6*y^2 + 1.55723201e4*y - 7.312884e5*x*y" + [] +[] +[Materials] # Material properties and relationships to apply to subdomains. + ############################## Materials for W (block = 4) + [diffusivity_W] + type = ADParsedMaterial + property_name = diffusivity_W + coupled_variables = 'temperature' + block = 4 + expression = '2.4e-7*exp(-4525.8/temperature)' # H diffusivity in W + outputs = all + [] + [solubility_W] + type = ADParsedMaterial + property_name = solubility_W + coupled_variables = 'temperature' + block = 4 + # expression = '2.95e-5 *exp(-12069.0/temperature)' # H solubility in W = (1.87e24)/(${tungsten_atomic_density}) [#/m^3] + expression = '2.95e-5 *exp(-12069.0/temperature) + 4.95e-8 * exp(-6614.6/temperature)' # H solubility in W = (1.87e24)/(${tungsten_atomic_density}) [#/m^3] + outputs = all + [] + [converter_to_regular_W] + type = MaterialADConverter + ad_props_in = 'diffusivity_W' + reg_props_out = 'diffusivity_W_nonAD' + block = 4 + [] + [heat_transfer_W] + type = GenericConstantMaterial + prop_names = 'density_W' + prop_values = '19300' # [g/m^3] + block = 4 + [] + [specific_heat_W] + type = ParsedMaterial + property_name = specific_heat_W + coupled_variables = 'temperature' + block = 4 + expression = '1.16e2 + 7.11e-2 * temperature - 6.58e-5 * temperature^2 + 3.24e-8 * temperature^3 -5.45e-12 * temperature^4' # ~ 132[J/kg-K] + outputs = all + [] + [thermal_conductivity_W] + type = ParsedMaterial + property_name = thermal_conductivity_W + coupled_variables = 'temperature' + block = 4 + # expression = '-7.8e-9 * temperature^3 + 5.0e-5 * temperature^2 - 1.1e-1 * temperature + 1.8e2' # ~ 173.0 [ W/m-K] from R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, + expression = '${W_cond_factor}*(2.41e2 - 2.90e-1 * temperature + 2.54e-4 * temperature^2 - 1.03e-7 * temperature^3 + 1.52e-11 * temperature^4)' # ~ 173.0 [ W/m-K] + outputs = all + [] + ############################## Materials for Cu (block = 3) + [diffusivity_Cu] + type = ADParsedMaterial + property_name = diffusivity_Cu + coupled_variables = 'temperature' + block = 3 + expression = '6.60e-7*exp(-4525.8/temperature)' # H diffusivity in Cu + outputs = all + [] + [solubility_Cu] + type = ADParsedMaterial + property_name = solubility_Cu + coupled_variables = 'temperature' + block = 3 + expression = '4.95e-5*exp(-6614.6/temperature)' # H solubility in Cu = (3.14e24)/(${tungsten_atomic_density}) [#/m^3] + outputs = all + [] + [converter_to_regular_Cu] + type = MaterialADConverter + ad_props_in = 'diffusivity_Cu' + reg_props_out = 'diffusivity_Cu_nonAD' + block = 3 + [] + [heat_transfer_Cu] + type = GenericConstantMaterial + prop_names = 'density_Cu' + prop_values = '8960.0' # [g/m^3] + block = 3 + [] + [specific_heat_Cu] + type = ParsedMaterial + property_name = specific_heat_Cu + coupled_variables = 'temperature' + block = 3 + expression = '3.16e2 + 3.18e-1 * temperature - 3.49e-4 * temperature^2 + 1.66e-7 * temperature^3' # ~ 384 [J/kg-K] + outputs = all + [] + [thermal_conductivity_Cu] + type = ParsedMaterial + property_name = thermal_conductivity_Cu + coupled_variables = 'temperature' + block = 3 + # expression = '-3.9e-8 * temperature^3 + 3.8e-5 * temperature^2 - 7.9e-2 * temperature + 4.0e2' # ~ 401.0 [ W/m-K] from R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, + expression = '4.21e2 - 6.85e-2 * temperature' # ~ 400.0 [ W/m-K] + outputs = all + [] + ############################## Materials for CuCrZr (block = 2) + [diffusivity_CuCrZr] + type = ADParsedMaterial + property_name = diffusivity_CuCrZr + coupled_variables = 'temperature' + block = 2 + expression = '3.90e-7*exp(-4873.9/temperature)' # H diffusivity in CuCrZr + outputs = all + [] + [solubility_CuCrZr] + type = ADParsedMaterial + property_name = solubility_CuCrZr + coupled_variables = 'temperature' + block = 2 + expression = '6.75e-6*exp(-4525.8/temperature)' # H solubility in CuCrZr = (4.28e23)/(${tungsten_atomic_density}) [#/m^3] + outputs = all + [] + [converter_to_regular_CuCrZr] + type = MaterialADConverter + ad_props_in = 'diffusivity_CuCrZr' + reg_props_out = 'diffusivity_CuCrZr_nonAD' + block = 2 + [] + [heat_transfer_CuCrZr] + type = GenericConstantMaterial + prop_names = 'density_CuCrZr specific_heat_CuCrZr' + prop_values = '8900.0 390.0' # [g/m^3], [ W/m-K], [J/kg-K] + block = 2 + [] + [thermal_conductivity_CuCrZr] + type = ParsedMaterial + property_name = thermal_conductivity_CuCrZr + coupled_variables = 'temperature' + block = 2 + # expression = '5.3e-7 * temperature^3 - 6.5e-4 * temperature^2 + 2.6e-1 * temperature + 3.1e2' # ~ 320.0 [ W/m-K] from R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, + expression = '3.87e2 - 1.28e-1 * temperature' # ~ 349 [ W/m-K] + outputs = all + [] + ############################## Materials for others + [interface_jump_4to3] + type = SolubilityRatioMaterial + solubility_primary = solubility_W + solubility_secondary = solubility_Cu + boundary = '4to3' + concentration_primary = C_mobile_W + concentration_secondary = C_mobile_Cu + [] + [interface_jump_3to2] + type = SolubilityRatioMaterial + solubility_primary = solubility_Cu + solubility_secondary = solubility_CuCrZr + boundary = '3to2' + concentration_primary = C_mobile_Cu + concentration_secondary = C_mobile_CuCrZr + [] +[] + +[Postprocessors] # Postprocessors compute single scalar values based on node results +# ########################################################### Side Postprocessors !!! Added !!! + [F_recombination] + type = SideDiffusiveFluxAverage # Calculating average flux (integrated) over the specificed boundary + boundary = 'top' + diffusivity = 5.01e-24 # (3.01604928)/(6.02e23)/[gram(T)/m^2] + # diffusivity = 5.508e-19 # (1.0e3)*(1.0e3)/(6.02e23)/(3.01604928) [gram(T)/m^2] + variable = Sc_C_total_W + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [F_permeation] # Uses conversion factor, not really flux + type = SideDiffusiveFluxAverage + boundary = '2to1' + diffusivity = 5.01e-24 # (3.01604928)/(6.02e23)/[gram(T)/m^2] + # diffusivity = 5.508e-19 # (1.0e3)*(1.0e3)/(6.02e23)/(3.01604928) [gram(T)/m^2] + variable = Sc_C_total_CuCrZr + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Tritium_SideFluxIntegral] + type = SideDiffusiveFluxIntegral + boundary = '2to1' + diffusivity = diffusivity_CuCrZr_nonAD + variable = Sc_C_total_CuCrZr + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Scaled_Tritium_Flux] + type = ParsedPostprocessor + expression = '5.01e-24 * Tritium_SideFluxIntegral' + pp_names = Tritium_SideFluxIntegral + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [coolant_heat_flux] # units of W/m2 + type = SideDiffusiveFluxAverage + boundary = '2to1' + diffusivity = thermal_conductivity_CuCrZr + variable = temperature + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + ############################################################ Postprocessors for W (block = 4) + [Int_C_mobile_W] + type = ElementIntegralVariablePostprocessor # Compute the volume integral over a subdomain + variable = C_mobile_W + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_mobile_W] + type = ScalePostprocessor # Scales some other PostProcessor by a real value + value = Int_C_mobile_W + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + [] + [Int_C_trapped_W] + type = ElementIntegralVariablePostprocessor + variable = C_trapped_W + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_trapped_W] + type = ScalePostprocessor + value = Int_C_trapped_W + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + [] + [Int_C_total_W] + type = ElementIntegralVariablePostprocessor + variable = C_total_W + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_total_W] + type = ScalePostprocessor + value = Int_C_total_W + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + [] + # ############################################################ Postprocessors for Cu (block = 3) + [Int_C_mobile_Cu] + type = ElementIntegralVariablePostprocessor + variable = C_mobile_Cu + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_mobile_Cu] + type = ScalePostprocessor + value = Int_C_mobile_Cu + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Int_C_trapped_Cu] + type = ElementIntegralVariablePostprocessor + variable = C_trapped_Cu + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_trapped_Cu] + type = ScalePostprocessor + value = Int_C_trapped_Cu + scaling_factor = 3.44e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Int_C_total_Cu] + type = ElementIntegralVariablePostprocessor + variable = C_total_Cu + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_total_Cu] + type = ScalePostprocessor + value = Int_C_total_Cu + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + # ############################################################ Postprocessors for CuCrZr (block = 2) + [Int_C_mobile_CuCrZr] + type = ElementIntegralVariablePostprocessor + variable = C_mobile_CuCrZr + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_mobile_CuCrZr] + type = ScalePostprocessor + value = Int_C_mobile_CuCrZr + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Int_C_trapped_CuCrZr] + type = ElementIntegralVariablePostprocessor + variable = C_trapped_CuCrZr + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_trapped_CuCrZr] + type = ScalePostprocessor + value = Int_C_trapped_CuCrZr + scaling_factor = 3.44e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Int_C_total_CuCrZr] + type = ElementIntegralVariablePostprocessor + variable = C_total_CuCrZr + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_total_CuCrZr] + type = ScalePostprocessor + value = Int_C_total_CuCrZr + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + ############################################################ Postprocessors for others + [dt] + type = TimestepSize # Reports timestep size + [] + [temperature_top] # Reports variable value at a specific point (x,y,z) + type = PointValue + variable = temperature + point = '0 14.0e-3 0' + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [temperature_tube] + type = PointValue + variable = temperature + point = '0 6.0e-3 0' + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + # limit timestep + [timestep_max_pp] # s + type = FunctionValuePostprocessor + function = timestep_function + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_temperature_W] # !!! ADDED !!! + type = ElementExtremeValue + block = 4 + variable = 'temperature' + value_type = max + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_temperature_Cu] # !!! ADDED !!! + type = ElementExtremeValue + block = 3 + variable = 'temperature' + value_type = max + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_temperature_CuCrZr] # !!! ADDED !!! + type = ElementExtremeValue + block = 2 + variable = 'temperature' + value_type = max + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [avg_temperature_W] # !!! ADDED !!! + type = ElementAverageValue + variable = temperature + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [avg_temperature_Cu] # !!! ADDED !!! + type = ElementAverageValue + variable = temperature + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [avg_temperature_CuCrZr] # !!! ADDED !!! + type = ElementAverageValue + variable = temperature + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_concentration_W] # !!! ADDED !!! + type = ElementExtremeValue + variable = 'C_total_W' + value_type = max + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_concentration_Cu] # !!! ADDED !!! + type = ElementExtremeValue + variable = 'C_total_Cu' + value_type = max + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_concentration_CuCrZr] # !!! ADDED !!! + type = ElementExtremeValue + variable = 'C_total_CuCrZr' + value_type = max + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [area_W] # !!! ADDED !!! + type = VolumePostprocessor + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [area_Cu] # !!! ADDED !!! + type = VolumePostprocessor + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [area_CuCrZr] # !!! ADDED !!! + type = VolumePostprocessor + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [total_retention] # !!! ADDED !!! + type = SumPostprocessor + values = 'ScInt_C_total_W ScInt_C_total_Cu ScInt_C_total_CuCrZr' + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] +### TIME MAXIMA + [time_max_T_W] + type = TimeExtremeValue + postprocessor = max_temperature_W + output_type = extreme_value + value_type = max + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [time_max_T_Cu] + type = TimeExtremeValue + postprocessor = max_temperature_Cu + output_type = extreme_value + value_type = max + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [time_max_T_CuCrZr] + type = TimeExtremeValue + postprocessor = max_temperature_CuCrZr + output_type = extreme_value + value_type = max + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] +[] +[Preconditioning] # https://mooseframework.inl.gov/syntax/Preconditioning/ + [smp] + type = SMP + full = true + [] +[] + +[Executioner] # Solution parameters + type = Transient + scheme = bdf2 + solve_type = NEWTON + petsc_options_iname = '-pc_type' + petsc_options_value = 'lu' + nl_rel_tol = 1e-5 # 1e-6 # Relative independent parameter tolerance + nl_abs_tol = 1e-6 # 1e-7 # Absolute tolerance + end_time = 2.01e2 # 50 ITER shots (3.0e4 s plasma, 2.0e4 SSP) # Total simulation time + automatic_scaling = true + line_search = 'none' + dtmin = 1e-6 # Minimum time step for convergence, time step size is reduced upon non-convergence, but dtmin is an absolute limit. Passing this will result in an error + error_on_dtmin = False + nl_max_its = 36 # Maximum number of iterations for convergence + [TimeStepper] + type = IterationAdaptiveDT + dt = 125 + time_t = '0 2e2 2.01e2' + time_dt = '100 0.0001 100' + optimal_iterations = 15 + iteration_window = 1 + growth_factor = 2.0 + cutback_factor = 0.8 + timestep_limiting_postprocessor = timestep_max_pp + [] +[] + +[Outputs] # Control outputs, include exodus and csv. + execute_on = 'none' +[] + diff --git a/test/tests/divertor_monoblock/elm_transient_sobol.i b/test/tests/divertor_monoblock/elm_transient_sobol.i new file mode 100644 index 000000000..ac4041220 --- /dev/null +++ b/test/tests/divertor_monoblock/elm_transient_sobol.i @@ -0,0 +1,160 @@ +[StochasticTools] # Designate as the Controller/Main Input +[] +[MultiApps] # Designate a subapp to control later + [runner] + type = SamplerFullSolveMultiApp + sampler = sobol + input_files = 'elm_transient_runner.i' + mode = batch-reset + keep_full_output_history = True + [] +[] +[Controls] # Control inputs from Main->Subapp + [cmdline] + type = MultiAppSamplerControl + multi_app = runner + sampler = sobol + param_names = """elm_value + elm_duration + coolant_temp + W_cond_factor""" + [] +[] +[Distributions] # Define probability distributions of parameters for sampling + [P_val] + type = Normal + mean = 1147e6 + standard_deviation = 57.35e6 + [] + [P_dur] + type = Normal + mean = 1.32e-3 + standard_deviation = 0.066e-3 + [] + [C_tem] + type = Normal + mean = 552 + standard_deviation = 27.6 + [] + [W_cond] + type = Uniform + lower_bound = 0.9 + upper_bound = 1.0 + [] +[] +[Samplers] # Sampling methodology using the probability distributions above + [hypercube_1] + type = LatinHypercube + distributions = 'P_val P_dur C_tem W_cond' + num_rows = 10 # N Samples + seed = 1001 + execute_on = 'PRE_MULTIAPP_SETUP' + [] + [hypercube_2] + type = LatinHypercube + distributions = 'P_val P_dur C_tem W_cond' + num_rows = 10 # N Samples + seed = 1002 + execute_on = 'PRE_MULTIAPP_SETUP' + [] + [sobol] + type = Sobol + sampler_a = hypercube_1 + sampler_b = hypercube_2 + execute_on = 'PRE_MULTIAPP_SETUP' + [] +[] +[Transfers] # Define values to extract from subapp + [results] + type = SamplerReporterTransfer + from_multi_app = runner + sampler = sobol + stochastic_reporter = results + #execute_on = 'MULTIAPP_FIXED_POINT_END' + from_reporter = """F_permeation/value + Scaled_Tritium_Flux/value + total_retention/value + coolant_heat_flux/value + max_temperature_W/value + max_temperature_Cu/value + max_temperature_CuCrZr/value + time_max_T_W/value + time_max_T_Cu/value + time_max_T_CuCrZr/value""" + [] + [matrix_results] + type = SamplerReporterTransfer + from_multi_app = runner + sampler = sobol + stochastic_reporter = matrix + from_reporter = """F_permeation/value + Scaled_Tritium_Flux/value + total_retention/value + coolant_heat_flux/value + max_temperature_W/value + max_temperature_Cu/value + max_temperature_CuCrZr/value + time_max_T_W/value + time_max_T_Cu/value + time_max_T_CuCrZr/value""" + [] +[] +[Reporters] + [results] + type = StochasticReporter + execute_on = 'FINAL' # INITIAL TIMESTEP_END MULTIAPP_FIXED_POINT_END + [] + [stats] + type = StatisticsReporter + reporters = """results/results:F_permeation:value + results/results:Scaled_Tritium_Flux:value + results/results:total_retention:value + results/results:coolant_heat_flux:value + results/results:max_temperature_W:value + results/results:max_temperature_Cu:value + results/results:max_temperature_CuCrZr:value + results/results:time_max_T_W:value + results/results:time_max_T_Cu:value + results/results:time_max_T_CuCrZr:value""" + compute = 'mean stddev' + ci_method = 'percentile' + ci_levels = """0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 + 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95""" + execute_on = 'FINAL' + [] + [sobol] + type = SobolReporter + sampler = sobol + reporters = """results/results:F_permeation:value + results/results:Scaled_Tritium_Flux:value + results/results:total_retention:value + results/results:coolant_heat_flux:value + results/results:max_temperature_W:value + results/results:max_temperature_Cu:value + results/results:max_temperature_CuCrZr:value + results/results:time_max_T_W:value + results/results:time_max_T_Cu:value + results/results:time_max_T_CuCrZr:value""" + ci_levels = """0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 + 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95""" + execute_on = 'FINAL' + [] + [matrix] + type = StochasticMatrix + sampler = sobol + sampler_column_names = """peak_value + peak_duration + coolant_temp + W_cond_factor""" + execute_on = 'FINAL' + parallel_type = ROOT + [] +[] +[Outputs] + [out] + type = JSON + distributed = False + execute_on = 'FINAL' + execute_reporters_on = 'FINAL' + [] +[] diff --git a/test/tests/divertor_monoblock/gold/elm_transient_sobol.json b/test/tests/divertor_monoblock/gold/elm_transient_sobol.json new file mode 100644 index 000000000..4324236b0 --- /dev/null +++ b/test/tests/divertor_monoblock/gold/elm_transient_sobol.json @@ -0,0 +1,9916 @@ +{ + "app_name": "main", + "current_time": "Wed Sep 24 13:17:58 2025", + "executable": "/home/adriaan/source/TMAP8/tmap8-opt", + "executable_time": "Wed Sep 24 09:45:22 2025", + "libmesh_version": "8d86fc999b8682441722d52851cbab29e14e5bfd", + "moose_version": "git commit 854afd15a7 on 2025-09-21", + "petsc_version": "3.23.4", + "reporters": { + "matrix": { + "type": "StochasticMatrix", + "values": { + "W_cond_factor": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "coolant_temp": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "matrix_results:F_permeation:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "matrix_results:Scaled_Tritium_Flux:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "matrix_results:converged": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "matrix_results:coolant_heat_flux:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "matrix_results:max_temperature_Cu:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "matrix_results:max_temperature_CuCrZr:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "matrix_results:max_temperature_W:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "matrix_results:time_max_T_Cu:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "matrix_results:time_max_T_CuCrZr:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "matrix_results:time_max_T_W:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "matrix_results:total_retention:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "peak_duration": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "peak_value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + } + } + }, + "results": { + "type": "StochasticReporter", + "values": { + "results:F_permeation:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "results:Scaled_Tritium_Flux:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "results:converged": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "results:coolant_heat_flux:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "results:max_temperature_Cu:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "results:max_temperature_CuCrZr:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "results:max_temperature_W:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "results:time_max_T_Cu:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "results:time_max_T_CuCrZr:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "results:time_max_T_W:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + }, + "results:total_retention:value": { + "row_begin": 0, + "row_end": 50, + "type": "std::vector" + } + } + }, + "sobol": { + "confidence_intervals": { + "levels": [ + 0.05, + 0.1, + 0.15, + 0.2, + 0.25, + 0.3, + 0.35, + 0.4, + 0.45, + 0.5, + 0.55, + 0.6, + 0.65, + 0.7, + 0.75, + 0.8, + 0.85, + 0.9, + 0.95 + ], + "method": "percentile", + "replicates": 10000, + "seed": 1 + }, + "indices": [ + "FIRST_ORDER", + "TOTAL", + "SECOND_ORDER" + ], + "num_params": 4, + "type": "SobolReporter", + "values": { + "results_results:F_permeation:value": { + "type": "SobolIndices" + }, + "results_results:Scaled_Tritium_Flux:value": { + "type": "SobolIndices" + }, + "results_results:coolant_heat_flux:value": { + "type": "SobolIndices" + }, + "results_results:max_temperature_Cu:value": { + "type": "SobolIndices" + }, + "results_results:max_temperature_CuCrZr:value": { + "type": "SobolIndices" + }, + "results_results:max_temperature_W:value": { + "type": "SobolIndices" + }, + "results_results:time_max_T_Cu:value": { + "type": "SobolIndices" + }, + "results_results:time_max_T_CuCrZr:value": { + "type": "SobolIndices" + }, + "results_results:time_max_T_W:value": { + "type": "SobolIndices" + }, + "results_results:total_retention:value": { + "type": "SobolIndices" + } + } + }, + "stats": { + "confidence_intervals": { + "levels": [ + 0.05, + 0.1, + 0.15, + 0.2, + 0.25, + 0.3, + 0.35, + 0.4, + 0.45, + 0.5, + 0.55, + 0.6, + 0.65, + 0.7, + 0.75, + 0.8, + 0.85, + 0.9, + 0.95 + ], + "method": "percentile", + "replicates": 10000, + "seed": 1 + }, + "type": "StatisticsReporter", + "values": { + "results_results:F_permeation:value_MEAN": { + "stat": "MEAN", + "type": "std::pair>" + }, + "results_results:F_permeation:value_STDDEV": { + "stat": "STDDEV", + "type": "std::pair>" + }, + "results_results:Scaled_Tritium_Flux:value_MEAN": { + "stat": "MEAN", + "type": 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Comments/Annotations +### +### C_mobile_j mobile H concentration in "j" material, where j = CuCrZr, Cu, W +### C_trapped_j trapped H concentration in "j" material, where j = CuCrZr, Cu, W +### C_total_j total H concentration in "j" material, where j = CuCrZr, Cu, W +### +### S_empty_j empty site concentration in "j" material, where j = CuCrZr, Cu, W +### S_trapped_j trapped site concentration in "j" material, where j = CuCrZr, Cu, W +### S_total_j total site H concentration in "j" material, where j = CuCrZr, Cu, W +### +### F_permeation permeation flux +### F_recombination recombination flux +### +### Sc_ Scaled +### Int_ Integrated +### ScInt_ Scaled and integrated # Nelson S. Comments/Annotations +### CHANGES ### +### 1. Added additional outputs for: +### a. Average and maximum temperatures for all materials +### b. Maximum tritium concentrations for all materials +### c. Temperature and tritium flux along block boundaries +### 2. Converted to continous-pulse for reduced computation time +### a. 1 Pulse = (500s)*(tritium flux) fluence +### b. + +### VARIABLES ### +# PRIOR +peak_value = 2.0e7 +peak_duration = 1.0 +coolant_temp = 552 +W_cond_factor = 1.0 +tungsten_atomic_density = ${units 6.338e28 m^-3} +# # BOUNDARY CONDITIONS +#incident_tritium_flux = 7.90e-13 +#incident_heat_flux = 1.0e7 +#coolant_temperature = 552 +#coolant_tritium_concentration = 1.0e-18 +## The MESH forms the spatial description of the problem +## Mesh generated by ConcentricCircleMeshGenerator and saved as "2DMonoblock.e" +[Controls/stochastic] # Sends data to Stochastic + type = SamplerReceiver +[] +[Mesh] # Begins the Mesh description block; the mesh spatially describes the problem, based on blocks (subdomains), meshes (geometries), nodes, etc.. + [ccmg] # name of the mesh + type = ConcentricCircleMeshGenerator # type of mesh generator following parameters will be dependent on the type + num_sectors = 12 # azimuthmal (angle) sections/divisions + rings = '1 6 4 22' # number of divisions/meshing between defined (next line) radii -> creates 4 (N+1) blocks (I don't know why this is made for radii and not azimuthmal sections) + radii = '${units 6 mm -> m} ${units 7.5 mm -> m} ${units 8.5 mm -> m}' # radii deliminating different blocks/regions/mesh spacings (mesh generator denotes blocks by radii, this line defines 3 blocks) + has_outer_square = on # creates a square boundary for the problem, using the pitch (next line) as the width/height (4th block is generated here) + pitch = ${units 28 mm -> m} # square width/height of problem + portion = left_half # only generate left (-14 0.1, but it creates negative C_mobile _Cu with << 0.1 + # jump_prop_name = solubility_ratio_4to3 + jump_prop_name = solubility_ratio # material property used in variable jump + boundary = '4to3' # boundary to apply the interface condition to. + [] + [tied_3to2] + type = ADPenaltyInterfaceDiffusion + variable = C_mobile_Cu + neighbor_var = C_mobile_CuCrZr + penalty = 0.05 + # it will not converge with > 0.1, but it creates negative C_mobile _Cu with << 0.1 + # jump_prop_name = solubility_ratio_3to2 + jump_prop_name = solubility_ratio + boundary = '3to2' + [] +[] + +[NodalKernels] # Nodal Kernels are used to solve equations that specifically belong on a node. + ############################## NodalKernels for W (block = 4) + [time_W] + type = TimeDerivativeNodalKernel # This kernel: forms the contribution to the residual and jacobian of the timer derivative term from an ODE being solves at ALL nodes: https://mooseframework.inl.gov/source/nodalkernels/TimeDerivativeNodalKernel.html + variable = C_trapped_W # variable the residual operates on + [] + [trapping_W] + type = TrappingNodalKernel # Defines time dependent trapping rate + variable = C_trapped_W # variable operating on + temperature = temperature + alpha_t = 2.75e11 # 1e15 # Trapping rate coefficient + N = 1.0e0 # = (1e0) x (${tungsten_atomic_density} #/m^3) # Host atom density + # Ct0 = 1.0e-4 # E.A. Hodille et al 2021 Nucl. Fusion 61 126003, trap 1 + Ct0 = 1.0e-4 # E.A. Hodille et al 2021 Nucl. Fusion 61 1260033, trap 2 # Host trapping fraction + trap_per_free = 1.0e0 # 1.0e1 # Estimate ratio of trapped to free species + mobile_concentration = 'C_mobile_W' # Corresponding mobile species variable + extra_vector_tags = ref + [] + [release_W] + type = ReleasingNodalKernel # Release rate of trapped species: https://mooseframework.inl.gov/TMAP8/source/nodal_kernels/ReleasingNodalKernel.html + alpha_r = 8.4e12 # 1.0e13 + temperature = temperature + # detrapping_energy = 9863.9 # = 0.85 eV E.A. Hodille et al 2021 Nucl. Fusion 61 126003, trap 1 + detrapping_energy = 11604.6 # Units of kelvin. + # = 1.00 eV E.A. Hodille et al 2021 Nucl. Fusion 61 126003, trap 2 + variable = C_trapped_W + [] + ############################## NodalKernels for Cu (block = 3) + [time_Cu] + type = TimeDerivativeNodalKernel + variable = C_trapped_Cu + [] + [trapping_Cu] + type = TrappingNodalKernel + variable = C_trapped_Cu + temperature = temperature + alpha_t = 2.75e11 # 1e15 + N = 1.0e0 # = ${tungsten_atomic_density} #/m^3 (W lattice density) + Ct0 = 5.0e-5 # R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 3 + trap_per_free = 1.0e0 # 1.0e1 + mobile_concentration = 'C_mobile_Cu' + extra_vector_tags = ref + [] + [release_Cu] + type = ReleasingNodalKernel + alpha_r = 8.4e12 # 1.0e13 + temperature = temperature + detrapping_energy = 5802.3 # = 0.50eV R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 3 + variable = C_trapped_Cu + [] + ############################## NodalKernels for CuCrZr (block = 2) + [time_CuCrZr] + type = TimeDerivativeNodalKernel + variable = C_trapped_CuCrZr + [] + [trapping_CuCrZr] + type = TrappingNodalKernel + variable = C_trapped_CuCrZr + temperature = temperature + alpha_t = 2.75e11 # 1e15 + N = 1.0e0 # = ${tungsten_atomic_density} #/m^3 (W lattice density) + Ct0 = 5.0e-5 # R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 4 + # Ct0 = 4.0e-2 # R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 5 + trap_per_free = 1.0e0 # 1.0e1 + mobile_concentration = 'C_mobile_CuCrZr' + extra_vector_tags = ref + [] + [release_CuCrZr] + type = ReleasingNodalKernel + alpha_r = 8.4e12 # 1.0e13 + temperature = temperature + detrapping_energy = 5802.3 # = 0.50eV R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 4 + # detrapping_energy = 9631.8 # = 0.83 eV R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 5 + variable = C_trapped_CuCrZr + [] +[] + +[BCs] # Begin problem boundary conditions + [C_mob_W_top_flux] # User defined name + type = FunctionNeumannBC # PDE flux boundary condition: https://mooseframework.inl.gov/source/bcs/FunctionNeumannBC.html + variable = C_mobile_W # Applicable variable + function = mobile_flux_bc_func + boundary = 'top' # boundary name to apply condition to + [] + [mobile_tube] + type = DirichletBC # Constant boundary condition, specific value for the applicable variable at the specified boundary + variable = C_mobile_CuCrZr # controls tritium concentration at inner tube location (un-normalized concentration) -> exact units unknown + value = 1.0e-18 # Value at boundary + boundary = '2to1' + [] + [temp_top] + type = FunctionNeumannBC # Applies to the variable, temperature, defined by the temp_flux_bc_func function. + variable = temperature # FunctionNeumannBC + function = temp_flux_bc_func + boundary = 'top' + [] + [temp_tube] + type = DirichletBC # Constant BC, but the exact value is defined by a later function. 'Constant' is allowed to be spatially and temporally dependent: https://mooseframework.inl.gov/source/bcs/FunctionDirichletBC.html + variable = temperature # specifies inner tube temperature at the boundary: 2to1 (CuCrZr to H20) + value = ${coolant_temp} + boundary = '2to1' + [] +[] +[Functions] # Specific physical relationship, allowed to be used by multiple objects + ### Maximum mobile flux of 7.90e-13 at the top surface (1.0e-4 [m]) + ### 1.80e23/m^2-s = (5.0e23/m^2-s) *(1-0.999) = (7.90e-13)*(${tungsten_atomic_density})/(1.0e-4) at steady state + [mobile_flux_bc_func] + type = ParsedFunction + expression = """if(t<2e4, 7.90e-13, + if(t<(2e4+${peak_duration}), ${peak_value}/1.0e7*7.90e-13, 7.90e-14))""" + [] + ### Heat flux of 10MW/m^2 at steady state + [temp_flux_bc_func] + type = ParsedFunction + expression = """if(t<2e4, 1.0e7, + if(t<(2e4+${peak_duration}), ${peak_value}, 1.0e6))""" + [] + [timestep_function] + type = ParsedFunction + expression = """if(t<2e4, 500, + if(t<(2e4+${peak_duration}+1), 0.10, 500))""" + [] +[] +[Materials] # Material properties and relationships to apply to subdomains. + ############################## Materials for W (block = 4) + [diffusivity_W] + type = ADParsedMaterial + property_name = diffusivity_W + coupled_variables = 'temperature' + block = 4 + expression = '2.4e-7*exp(-4525.8/temperature)' # H diffusivity in W + outputs = all + [] + [solubility_W] + type = ADParsedMaterial + property_name = solubility_W + coupled_variables = 'temperature' + block = 4 + # expression = '2.95e-5 *exp(-12069.0/temperature)' # H solubility in W = (1.87e24)/(${tungsten_atomic_density}) [#/m^3] + expression = '2.95e-5 *exp(-12069.0/temperature) + 4.95e-8 * exp(-6614.6/temperature)' # H solubility in W = (1.87e24)/(${tungsten_atomic_density}) [#/m^3] + outputs = all + [] + [converter_to_regular_W] + type = MaterialADConverter + ad_props_in = 'diffusivity_W' + reg_props_out = 'diffusivity_W_nonAD' + block = 4 + [] + [heat_transfer_W] + type = GenericConstantMaterial + prop_names = 'density_W' + prop_values = '19300' # [g/m^3] + block = 4 + [] + [specific_heat_W] + type = ParsedMaterial + property_name = specific_heat_W + coupled_variables = 'temperature' + block = 4 + expression = '1.16e2 + 7.11e-2 * temperature - 6.58e-5 * temperature^2 + 3.24e-8 * temperature^3 -5.45e-12 * temperature^4' # ~ 132[J/kg-K] + outputs = all + [] + [thermal_conductivity_W] + type = ParsedMaterial + property_name = thermal_conductivity_W + coupled_variables = 'temperature' + block = 4 + # expression = '-7.8e-9 * temperature^3 + 5.0e-5 * temperature^2 - 1.1e-1 * temperature + 1.8e2' # ~ 173.0 [ W/m-K] from R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, + expression = '${W_cond_factor}*(2.41e2 - 2.90e-1 * temperature + 2.54e-4 * temperature^2 - 1.03e-7 * temperature^3 + 1.52e-11 * temperature^4)' # ~ 173.0 [ W/m-K] + outputs = all + [] + ############################## Materials for Cu (block = 3) + [diffusivity_Cu] + type = ADParsedMaterial + property_name = diffusivity_Cu + coupled_variables = 'temperature' + block = 3 + expression = '6.60e-7*exp(-4525.8/temperature)' # H diffusivity in Cu + outputs = all + [] + [solubility_Cu] + type = ADParsedMaterial + property_name = solubility_Cu + coupled_variables = 'temperature' + block = 3 + expression = '4.95e-5*exp(-6614.6/temperature)' # H solubility in Cu = (3.14e24)/(${tungsten_atomic_density}) [#/m^3] + outputs = all + [] + [converter_to_regular_Cu] + type = MaterialADConverter + ad_props_in = 'diffusivity_Cu' + reg_props_out = 'diffusivity_Cu_nonAD' + block = 3 + [] + [heat_transfer_Cu] + type = GenericConstantMaterial + prop_names = 'density_Cu' + prop_values = '8960.0' # [g/m^3] + block = 3 + [] + [specific_heat_Cu] + type = ParsedMaterial + property_name = specific_heat_Cu + coupled_variables = 'temperature' + block = 3 + expression = '3.16e2 + 3.18e-1 * temperature - 3.49e-4 * temperature^2 + 1.66e-7 * temperature^3' # ~ 384 [J/kg-K] + outputs = all + [] + [thermal_conductivity_Cu] + type = ParsedMaterial + property_name = thermal_conductivity_Cu + coupled_variables = 'temperature' + block = 3 + # expression = '-3.9e-8 * temperature^3 + 3.8e-5 * temperature^2 - 7.9e-2 * temperature + 4.0e2' # ~ 401.0 [ W/m-K] from R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, + expression = '4.21e2 - 6.85e-2 * temperature' # ~ 400.0 [ W/m-K] + outputs = all + [] + ############################## Materials for CuCrZr (block = 2) + [diffusivity_CuCrZr] + type = ADParsedMaterial + property_name = diffusivity_CuCrZr + coupled_variables = 'temperature' + block = 2 + expression = '3.90e-7*exp(-4873.9/temperature)' # H diffusivity in CuCrZr + outputs = all + [] + [solubility_CuCrZr] + type = ADParsedMaterial + property_name = solubility_CuCrZr + coupled_variables = 'temperature' + block = 2 + expression = '6.75e-6*exp(-4525.8/temperature)' # H solubility in CuCrZr = (4.28e23)/(${tungsten_atomic_density}) [#/m^3] + outputs = all + [] + [converter_to_regular_CuCrZr] + type = MaterialADConverter + ad_props_in = 'diffusivity_CuCrZr' + reg_props_out = 'diffusivity_CuCrZr_nonAD' + block = 2 + [] + [heat_transfer_CuCrZr] + type = GenericConstantMaterial + prop_names = 'density_CuCrZr specific_heat_CuCrZr' + prop_values = '8900.0 390.0' # [g/m^3], [ W/m-K], [J/kg-K] + block = 2 + [] + [thermal_conductivity_CuCrZr] + type = ParsedMaterial + property_name = thermal_conductivity_CuCrZr + coupled_variables = 'temperature' + block = 2 + # expression = '5.3e-7 * temperature^3 - 6.5e-4 * temperature^2 + 2.6e-1 * temperature + 3.1e2' # ~ 320.0 [ W/m-K] from R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, + expression = '3.87e2 - 1.28e-1 * temperature' # ~ 349 [ W/m-K] + outputs = all + [] + ############################## Materials for others + [interface_jump_4to3] + type = SolubilityRatioMaterial + solubility_primary = solubility_W + solubility_secondary = solubility_Cu + boundary = '4to3' + concentration_primary = C_mobile_W + concentration_secondary = C_mobile_Cu + [] + [interface_jump_3to2] + type = SolubilityRatioMaterial + solubility_primary = solubility_Cu + solubility_secondary = solubility_CuCrZr + boundary = '3to2' + concentration_primary = C_mobile_Cu + concentration_secondary = C_mobile_CuCrZr + [] +[] + +[Postprocessors] # Postprocessors compute single scalar values based on node results +# ########################################################### Side Postprocessors !!! Added !!! + [F_recombination] + type = SideDiffusiveFluxAverage # Calculating average flux (integrated) over the specificed boundary + boundary = 'top' + diffusivity = 5.01e-24 # (3.01604928)/(6.02e23)/[gram(T)/m^2] + # diffusivity = 5.508e-19 # (1.0e3)*(1.0e3)/(6.02e23)/(3.01604928) [gram(T)/m^2] + variable = Sc_C_total_W + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [F_permeation] # Uses conversion factor, not really flux + type = SideDiffusiveFluxAverage + boundary = '2to1' + diffusivity = 5.01e-24 # (3.01604928)/(6.02e23)/[gram(T)/m^2] + # diffusivity = 5.508e-19 # (1.0e3)*(1.0e3)/(6.02e23)/(3.01604928) [gram(T)/m^2] + variable = Sc_C_total_CuCrZr + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Tritium_SideFluxIntegral] + type = SideDiffusiveFluxIntegral + boundary = '2to1' + diffusivity = diffusivity_CuCrZr_nonAD + variable = Sc_C_total_CuCrZr + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Scaled_Tritium_Flux] + type = ParsedPostprocessor + expression = '5.01e-24 * Tritium_SideFluxIntegral' + pp_names = Tritium_SideFluxIntegral + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [coolant_heat_flux] # units of W/m2 + type = SideDiffusiveFluxAverage + boundary = '2to1' + diffusivity = thermal_conductivity_CuCrZr + variable = temperature + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + ############################################################ Postprocessors for W (block = 4) + [Int_C_mobile_W] + type = ElementIntegralVariablePostprocessor # Compute the volume integral over a subdomain + variable = C_mobile_W + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_mobile_W] + type = ScalePostprocessor # Scales some other PostProcessor by a real value + value = Int_C_mobile_W + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + [] + [Int_C_trapped_W] + type = ElementIntegralVariablePostprocessor + variable = C_trapped_W + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_trapped_W] + type = ScalePostprocessor + value = Int_C_trapped_W + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + [] + [Int_C_total_W] + type = ElementIntegralVariablePostprocessor + variable = C_total_W + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_total_W] + type = ScalePostprocessor + value = Int_C_total_W + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + [] + # ############################################################ Postprocessors for Cu (block = 3) + [Int_C_mobile_Cu] + type = ElementIntegralVariablePostprocessor + variable = C_mobile_Cu + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_mobile_Cu] + type = ScalePostprocessor + value = Int_C_mobile_Cu + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Int_C_trapped_Cu] + type = ElementIntegralVariablePostprocessor + variable = C_trapped_Cu + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_trapped_Cu] + type = ScalePostprocessor + value = Int_C_trapped_Cu + scaling_factor = 3.44e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Int_C_total_Cu] + type = ElementIntegralVariablePostprocessor + variable = C_total_Cu + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_total_Cu] + type = ScalePostprocessor + value = Int_C_total_Cu + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + # ############################################################ Postprocessors for CuCrZr (block = 2) + [Int_C_mobile_CuCrZr] + type = ElementIntegralVariablePostprocessor + variable = C_mobile_CuCrZr + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_mobile_CuCrZr] + type = ScalePostprocessor + value = Int_C_mobile_CuCrZr + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Int_C_trapped_CuCrZr] + type = ElementIntegralVariablePostprocessor + variable = C_trapped_CuCrZr + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_trapped_CuCrZr] + type = ScalePostprocessor + value = Int_C_trapped_CuCrZr + scaling_factor = 3.44e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Int_C_total_CuCrZr] + type = ElementIntegralVariablePostprocessor + variable = C_total_CuCrZr + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_total_CuCrZr] + type = ScalePostprocessor + value = Int_C_total_CuCrZr + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + ############################################################ Postprocessors for others + [dt] + type = TimestepSize # Reports timestep size + [] + [temperature_top] # Reports variable value at a specific point (x,y,z) + type = PointValue + variable = temperature + point = '0 14.0e-3 0' + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [temperature_tube] + type = PointValue + variable = temperature + point = '0 6.0e-3 0' + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + # limit timestep + [timestep_max_pp] # s + type = FunctionValuePostprocessor + function = timestep_function + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_temperature_W] # !!! ADDED !!! + type = ElementExtremeValue + block = 4 + variable = 'temperature' + value_type = max + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_temperature_Cu] # !!! ADDED !!! + type = ElementExtremeValue + block = 3 + variable = 'temperature' + value_type = max + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_temperature_CuCrZr] # !!! ADDED !!! + type = ElementExtremeValue + block = 2 + variable = 'temperature' + value_type = max + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [avg_temperature_W] # !!! ADDED !!! + type = ElementAverageValue + variable = temperature + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [avg_temperature_Cu] # !!! ADDED !!! + type = ElementAverageValue + variable = temperature + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [avg_temperature_CuCrZr] # !!! ADDED !!! + type = ElementAverageValue + variable = temperature + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_concentration_W] # !!! ADDED !!! + type = ElementExtremeValue + variable = 'C_total_W' + value_type = max + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_concentration_Cu] # !!! ADDED !!! + type = ElementExtremeValue + variable = 'C_total_Cu' + value_type = max + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_concentration_CuCrZr] # !!! ADDED !!! + type = ElementExtremeValue + variable = 'C_total_CuCrZr' + value_type = max + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [area_W] # !!! ADDED !!! + type = VolumePostprocessor + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [area_Cu] # !!! ADDED !!! + type = VolumePostprocessor + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [area_CuCrZr] # !!! ADDED !!! + type = VolumePostprocessor + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [total_retention] # !!! ADDED !!! + type = SumPostprocessor + values = 'ScInt_C_total_W ScInt_C_total_Cu ScInt_C_total_CuCrZr' + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] +### TIME MAXIMA + [time_max_T_W] + type = TimeExtremeValue + postprocessor = max_temperature_W + output_type = extreme_value + value_type = max + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [time_max_T_Cu] + type = TimeExtremeValue + postprocessor = max_temperature_Cu + output_type = extreme_value + value_type = max + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [time_max_T_CuCrZr] + type = TimeExtremeValue + postprocessor = max_temperature_CuCrZr + output_type = extreme_value + value_type = max + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] +[] +[Preconditioning] # https://mooseframework.inl.gov/syntax/Preconditioning/ + [smp] + type = SMP + full = true + [] +[] + +[Executioner] # Solution parameters + type = Transient + scheme = bdf2 + solve_type = NEWTON + petsc_options_iname = '-pc_type' + petsc_options_value = 'lu' + nl_rel_tol = 1e-2 # 1e-6 # Relative independent parameter tolerance + nl_abs_tol = 1e-1 # 1e-7 # Absolute tolerance + end_time = 2.5e1 # 50 ITER shots (3.0e4 s plasma, 2.0e4 SSP) # Total simulation time + automatic_scaling = true + line_search = 'none' + dtmin = 1e-6 + error_on_dtmin = False # Minimum time step for convergence, time step size is reduced upon non-convergence, but dtmin is an absolute limit. Passing this will result in an error + nl_max_its = 36 # Maximum number of iterations for convergence + [TimeStepper] + type = IterationAdaptiveDT + dt = 100 + time_t = '0 2e4 2.01e4' + time_dt = '100 0.001 100' + optimal_iterations = 10 + iteration_window = 1 + growth_factor = 2.0 + cutback_factor = 0.8 + timestep_limiting_postprocessor = timestep_max_pp + [] +[] + +[Outputs] # Control outputs, include exodus and csv. + execute_on = 'none' +[] + diff --git a/test/tests/divertor_monoblock/shutdown_transient_sobol.i b/test/tests/divertor_monoblock/shutdown_transient_sobol.i new file mode 100644 index 000000000..42e17cf51 --- /dev/null +++ b/test/tests/divertor_monoblock/shutdown_transient_sobol.i @@ -0,0 +1,161 @@ +[StochasticTools] # Designate as the Controller/Main Input +[] +[MultiApps] # Designate a subapp to control later + [runner] + type = SamplerFullSolveMultiApp + sampler = sobol + input_files = 'shutdown_transient_runner.i' + mode = batch-reset + keep_full_output_history = True + ignore_solve_not_converge = True + [] +[] +[Controls] # Control inputs from Main->Subapp + [cmdline] + type = MultiAppSamplerControl + multi_app = runner + sampler = sobol + param_names = """peak_value + peak_duration + coolant_temp + W_cond_factor""" + [] +[] +[Distributions] # Define probability distributions of parameters for sampling + [P_val] + type = Normal + mean = 2e7 + standard_deviation = 1e6 + [] + [P_dur] + type = Normal + mean = 1 + standard_deviation = 0.05 + [] + [C_tem] + type = Normal + mean = 552 + standard_deviation = 27.6 + [] + [W_cond] + type = Uniform + lower_bound = 0.9 + upper_bound = 1.0 + [] +[] +[Samplers] # Sampling methodology using the probability distributions above + [hypercube_1] + type = LatinHypercube + distributions = 'P_val P_dur C_tem W_cond' + num_rows = 10 # N Samples + seed = 1001 + execute_on = 'PRE_MULTIAPP_SETUP' + [] + [hypercube_2] + type = LatinHypercube + distributions = 'P_val P_dur C_tem W_cond' + num_rows = 10 # N Samples + seed = 1002 + execute_on = 'PRE_MULTIAPP_SETUP' + [] + [sobol] + type = Sobol + sampler_a = hypercube_1 + sampler_b = hypercube_2 + execute_on = 'PRE_MULTIAPP_SETUP' + [] +[] +[Transfers] # Define values to extract from subapp + [results] + type = SamplerReporterTransfer + from_multi_app = runner + sampler = sobol + stochastic_reporter = results + #execute_on = 'MULTIAPP_FIXED_POINT_END' + from_reporter = """F_permeation/value + Scaled_Tritium_Flux/value + total_retention/value + coolant_heat_flux/value + max_temperature_W/value + max_temperature_Cu/value + max_temperature_CuCrZr/value + time_max_T_W/value + time_max_T_Cu/value + time_max_T_CuCrZr/value""" + [] + [matrix_results] + type = SamplerReporterTransfer + from_multi_app = runner + sampler = sobol + stochastic_reporter = matrix + from_reporter = """F_permeation/value + Scaled_Tritium_Flux/value + total_retention/value + coolant_heat_flux/value + max_temperature_W/value + max_temperature_Cu/value + max_temperature_CuCrZr/value + time_max_T_W/value + time_max_T_Cu/value + time_max_T_CuCrZr/value""" + [] +[] +[Reporters] + [results] + type = StochasticReporter + execute_on = 'FINAL' # INITIAL TIMESTEP_END MULTIAPP_FIXED_POINT_END + [] + [stats] + type = StatisticsReporter + reporters = """results/results:F_permeation:value + results/results:Scaled_Tritium_Flux:value + results/results:total_retention:value + results/results:coolant_heat_flux:value + results/results:max_temperature_W:value + results/results:max_temperature_Cu:value + results/results:max_temperature_CuCrZr:value + results/results:time_max_T_W:value + results/results:time_max_T_Cu:value + results/results:time_max_T_CuCrZr:value""" + compute = 'mean stddev' + ci_method = 'percentile' + ci_levels = """0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 + 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95""" + execute_on = 'FINAL' + [] + [sobol] + type = SobolReporter + sampler = sobol + reporters = """results/results:F_permeation:value + results/results:Scaled_Tritium_Flux:value + results/results:total_retention:value + results/results:coolant_heat_flux:value + results/results:max_temperature_W:value + results/results:max_temperature_Cu:value + results/results:max_temperature_CuCrZr:value + results/results:time_max_T_W:value + results/results:time_max_T_Cu:value + results/results:time_max_T_CuCrZr:value""" + ci_levels = """0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 + 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95""" + execute_on = 'FINAL' + [] + [matrix] + type = StochasticMatrix + sampler = sobol + sampler_column_names = """peak_value + peak_duration + coolant_temp + W_cond_factor""" + execute_on = 'FINAL' + parallel_type = ROOT + [] +[] +[Outputs] + [out] + type = JSON + distributed = True + execute_on = 'FINAL' + execute_reporters_on = 'FINAL' + [] +[] diff --git a/test/tests/divertor_monoblock/steady_state_runner.i b/test/tests/divertor_monoblock/steady_state_runner.i new file mode 100644 index 000000000..974ff9570 --- /dev/null +++ b/test/tests/divertor_monoblock/steady_state_runner.i @@ -0,0 +1,1137 @@ +### Nomenclatures # Nelson S. Comments/Annotations +### +### C_mobile_j mobile H concentration in "j" material, where j = CuCrZr, Cu, W +### C_trapped_j trapped H concentration in "j" material, where j = CuCrZr, Cu, W +### C_total_j total H concentration in "j" material, where j = CuCrZr, Cu, W +### +### S_empty_j empty site concentration in "j" material, where j = CuCrZr, Cu, W +### S_trapped_j trapped site concentration in "j" material, where j = CuCrZr, Cu, W +### S_total_j total site H concentration in "j" material, where j = CuCrZr, Cu, W +### +### F_permeation permeation flux +### F_recombination recombination flux +### +### Sc_ Scaled +### Int_ Integrated +### ScInt_ Scaled and integrated # Nelson S. Comments/Annotations +### CHANGES ### +### 1. Added additional outputs for: +### a. Average and maximum temperatures for all materials +### b. Maximum tritium concentrations for all materials +### c. Temperature and tritium flux along block boundaries +### 2. Converted to continous-pulse for reduced computation time +### a. 1 Pulse = (500s)*(tritium flux) fluence +### b. + +tungsten_atomic_density = ${units 6.338e28 m^-3} + +[Controls/stochastic] # Sends data to Stochastic + type = SamplerReceiver +[] +[Mesh] # Begins the Mesh description block; the mesh spatially describes the problem, based on blocks (subdomains), meshes (geometries), nodes, etc.. + [ccmg] # name of the mesh + type = ConcentricCircleMeshGenerator # type of mesh generator following parameters will be dependent on the type + num_sectors = 12 # azimuthmal (angle) sections/divisions + rings = '1 6 4 22' # number of divisions/meshing between defined (next line) radii -> creates 4 (N+1) blocks (I don't know why this is made for radii and not azimuthmal sections) + radii = '${units 6 mm -> m} ${units 7.5 mm -> m} ${units 8.5 mm -> m}' # radii deliminating different blocks/regions/mesh spacings (mesh generator denotes blocks by radii, this line defines 3 blocks) + has_outer_square = on # creates a square boundary for the problem, using the pitch (next line) as the width/height (4th block is generated here) + pitch = ${units 28 mm -> m} # square width/height of problem + portion = left_half # only generate left (-14 0.1, but it creates negative C_mobile _Cu with << 0.1 + # jump_prop_name = solubility_ratio_4to3 + jump_prop_name = solubility_ratio # material property used in variable jump + boundary = '4to3' # boundary to apply the interface condition to. + [] + [tied_3to2] + type = ADPenaltyInterfaceDiffusion + variable = C_mobile_Cu + neighbor_var = C_mobile_CuCrZr + penalty = 0.05 + # it will not converge with > 0.1, but it creates negative C_mobile _Cu with << 0.1 + # jump_prop_name = solubility_ratio_3to2 + jump_prop_name = solubility_ratio + boundary = '3to2' + [] +[] + +[NodalKernels] # Nodal Kernels are used to solve equations that specifically belong on a node. + ############################## NodalKernels for W (block = 4) + [time_W] + type = TimeDerivativeNodalKernel # This kernel: forms the contribution to the residual and jacobian of the timer derivative term from an ODE being solves at ALL nodes: https://mooseframework.inl.gov/source/nodalkernels/TimeDerivativeNodalKernel.html + variable = C_trapped_W # variable the residual operates on + [] + [trapping_W] + type = TrappingNodalKernel # Defines time dependent trapping rate + variable = C_trapped_W # variable operating on + temperature = temperature + alpha_t = 2.75e11 # 1e15 # Trapping rate coefficient + N = 1.0e0 # = (1e0) x (${tungsten_atomic_density} #/m^3) # Host atom density + # Ct0 = 1.0e-4 # E.A. Hodille et al 2021 Nucl. Fusion 61 126003, trap 1 + Ct0 = 1.0e-4 # E.A. Hodille et al 2021 Nucl. Fusion 61 1260033, trap 2 # Host trapping fraction + trap_per_free = 1.0e0 # 1.0e1 # Estimate ratio of trapped to free species + mobile_concentration = 'C_mobile_W' # Corresponding mobile species variable + extra_vector_tags = ref + [] + [release_W] + type = ReleasingNodalKernel # Release rate of trapped species: https://mooseframework.inl.gov/TMAP8/source/nodal_kernels/ReleasingNodalKernel.html + alpha_r = 8.4e12 # 1.0e13 + temperature = temperature + # detrapping_energy = 9863.9 # = 0.85 eV E.A. Hodille et al 2021 Nucl. Fusion 61 126003, trap 1 + detrapping_energy = 11604.6 # Units of kelvin. + # = 1.00 eV E.A. Hodille et al 2021 Nucl. Fusion 61 126003, trap 2 + variable = C_trapped_W + [] + ############################## NodalKernels for Cu (block = 3) + [time_Cu] + type = TimeDerivativeNodalKernel + variable = C_trapped_Cu + [] + [trapping_Cu] + type = TrappingNodalKernel + variable = C_trapped_Cu + temperature = temperature + alpha_t = 2.75e11 # 1e15 + N = 1.0e0 # = ${tungsten_atomic_density} #/m^3 (W lattice density) + Ct0 = 5.0e-5 # R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 3 + trap_per_free = 1.0e0 # 1.0e1 + mobile_concentration = 'C_mobile_Cu' + extra_vector_tags = ref + [] + [release_Cu] + type = ReleasingNodalKernel + alpha_r = 8.4e12 # 1.0e13 + temperature = temperature + detrapping_energy = 5802.3 # = 0.50eV R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 3 + variable = C_trapped_Cu + [] + ############################## NodalKernels for CuCrZr (block = 2) + [time_CuCrZr] + type = TimeDerivativeNodalKernel + variable = C_trapped_CuCrZr + [] + [trapping_CuCrZr] + type = TrappingNodalKernel + variable = C_trapped_CuCrZr + temperature = temperature + alpha_t = 2.75e11 # 1e15 + N = 1.0e0 # = ${tungsten_atomic_density} #/m^3 (W lattice density) + Ct0 = 5.0e-5 # R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 4 + # Ct0 = 4.0e-2 # R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 5 + trap_per_free = 1.0e0 # 1.0e1 + mobile_concentration = 'C_mobile_CuCrZr' + extra_vector_tags = ref + [] + [release_CuCrZr] + type = ReleasingNodalKernel + alpha_r = 8.4e12 # 1.0e13 + temperature = temperature + detrapping_energy = 5802.3 # = 0.50eV R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 4 + # detrapping_energy = 9631.8 # = 0.83 eV R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, trap 5 + variable = C_trapped_CuCrZr + [] +[] + +[BCs] # Begin problem boundary conditions + [C_mob_W_top_flux] # User defined name + type = NeumannBC # PDE flux boundary condition: https://mooseframework.inl.gov/source/bcs/FunctionNeumannBC.html + variable = C_mobile_W # Applicable variable + value = 7.90e-13 + boundary = 'top' # boundary name to apply condition to + [] + [mobile_tube] + type = DirichletBC # Constant boundary condition, specific value for the applicable variable at the specified boundary + variable = C_mobile_CuCrZr # controls tritium concentration at inner tube location (un-normalized concentration) -> exact units unknown + value = 1.0e-18 # Value at boundary + boundary = '2to1' + [] + [temp_top] + type = NeumannBC # Applies to the variable, temperature, defined by the temp_flux_bc_func function. + variable = temperature # FunctionNeumannBC + value = 1.0e7 + boundary = 'top' + [] + [temp_tube] + type = DirichletBC # Constant BC, but the exact value is defined by a later function. 'Constant' is allowed to be spatially and temporally dependent: https://mooseframework.inl.gov/source/bcs/FunctionDirichletBC.html + variable = temperature # specifies inner tube temperature at the boundary: 2to1 (CuCrZr to H20) + value = 552 + boundary = '2to1' + [] +[] +[Functions] # Specific physical relationship, allowed to be used by multiple objects + [timestep_function] + type = ParsedFunction + expression = 'if(t<100, 25, 400)' + [] +[] +[Materials] # Material properties and relationships to apply to subdomains. + ############################## Materials for W (block = 4) + [diffusivity_W] + type = ADParsedMaterial + property_name = diffusivity_W + coupled_variables = 'temperature' + block = 4 + expression = '2.4e-7*exp(-4525.8/temperature)' # H diffusivity in W + outputs = all + [] + [solubility_W] + type = ADParsedMaterial + property_name = solubility_W + coupled_variables = 'temperature' + block = 4 + # expression = '2.95e-5 *exp(-12069.0/temperature)' # H solubility in W = (1.87e24)/(${tungsten_atomic_density}) [#/m^3] + expression = '2.95e-5 *exp(-12069.0/temperature) + 4.95e-8 * exp(-6614.6/temperature)' # H solubility in W = (1.87e24)/(${tungsten_atomic_density}) [#/m^3] + outputs = all + [] + [converter_to_regular_W] + type = MaterialADConverter + ad_props_in = 'diffusivity_W' + reg_props_out = 'diffusivity_W_nonAD' + block = 4 + [] + [heat_transfer_W] + type = GenericConstantMaterial + prop_names = 'density_W' + prop_values = '19300' # [g/m^3] + block = 4 + [] + [specific_heat_W] + type = ParsedMaterial + property_name = specific_heat_W + coupled_variables = 'temperature' + block = 4 + expression = '1.16e2 + 7.11e-2 * temperature - 6.58e-5 * temperature^2 + 3.24e-8 * temperature^3 -5.45e-12 * temperature^4' # ~ 132[J/kg-K] + outputs = all + [] + [thermal_conductivity_W] + type = ParsedMaterial + property_name = thermal_conductivity_W + coupled_variables = 'temperature' + block = 4 + # expression = '-7.8e-9 * temperature^3 + 5.0e-5 * temperature^2 - 1.1e-1 * temperature + 1.8e2' # ~ 173.0 [ W/m-K] from R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, + expression = '2.41e2 - 2.90e-1 * temperature + 2.54e-4 * temperature^2 - 1.03e-7 * temperature^3 + 1.52e-11 * temperature^4' # ~ 173.0 [ W/m-K] + outputs = all + [] + ############################## Materials for Cu (block = 3) + [diffusivity_Cu] + type = ADParsedMaterial + property_name = diffusivity_Cu + coupled_variables = 'temperature' + block = 3 + expression = '6.60e-7*exp(-4525.8/temperature)' # H diffusivity in Cu + outputs = all + [] + [solubility_Cu] + type = ADParsedMaterial + property_name = solubility_Cu + coupled_variables = 'temperature' + block = 3 + expression = '4.95e-5*exp(-6614.6/temperature)' # H solubility in Cu = (3.14e24)/(${tungsten_atomic_density}) [#/m^3] + outputs = all + [] + [converter_to_regular_Cu] + type = MaterialADConverter + ad_props_in = 'diffusivity_Cu' + reg_props_out = 'diffusivity_Cu_nonAD' + block = 3 + [] + [heat_transfer_Cu] + type = GenericConstantMaterial + prop_names = 'density_Cu' + prop_values = '8960.0' # [g/m^3] + block = 3 + [] + [specific_heat_Cu] + type = ParsedMaterial + property_name = specific_heat_Cu + coupled_variables = 'temperature' + block = 3 + expression = '3.16e2 + 3.18e-1 * temperature - 3.49e-4 * temperature^2 + 1.66e-7 * temperature^3' # ~ 384 [J/kg-K] + outputs = all + [] + [thermal_conductivity_Cu] + type = ParsedMaterial + property_name = thermal_conductivity_Cu + coupled_variables = 'temperature' + block = 3 + # expression = '-3.9e-8 * temperature^3 + 3.8e-5 * temperature^2 - 7.9e-2 * temperature + 4.0e2' # ~ 401.0 [ W/m-K] from R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, + expression = '4.21e2 - 6.85e-2 * temperature' # ~ 400.0 [ W/m-K] + outputs = all + [] + ############################## Materials for CuCrZr (block = 2) + [diffusivity_CuCrZr] + type = ADParsedMaterial + property_name = diffusivity_CuCrZr + coupled_variables = 'temperature' + block = 2 + expression = '3.90e-7*exp(-4873.9/temperature)' # H diffusivity in CuCrZr + outputs = all + [] + [solubility_CuCrZr] + type = ADParsedMaterial + property_name = solubility_CuCrZr + coupled_variables = 'temperature' + block = 2 + expression = '6.75e-6*exp(-4525.8/temperature)' # H solubility in CuCrZr = (4.28e23)/(${tungsten_atomic_density}) [#/m^3] + outputs = all + [] + [converter_to_regular_CuCrZr] + type = MaterialADConverter + ad_props_in = 'diffusivity_CuCrZr' + reg_props_out = 'diffusivity_CuCrZr_nonAD' + block = 2 + [] + [heat_transfer_CuCrZr] + type = GenericConstantMaterial + prop_names = 'density_CuCrZr specific_heat_CuCrZr' + prop_values = '8900.0 390.0' # [g/m^3], [ W/m-K], [J/kg-K] + block = 2 + [] + [thermal_conductivity_CuCrZr] + type = ParsedMaterial + property_name = thermal_conductivity_CuCrZr + coupled_variables = 'temperature' + block = 2 + # expression = '5.3e-7 * temperature^3 - 6.5e-4 * temperature^2 + 2.6e-1 * temperature + 3.1e2' # ~ 320.0 [ W/m-K] from R. Delaporte-Mathurin et al 2021 Nucl. Fusion 61 036038, + expression = '3.87e2 - 1.28e-1 * temperature' # ~ 349 [ W/m-K] + outputs = all + [] + ############################## Materials for others + [interface_jump_4to3] + type = SolubilityRatioMaterial + solubility_primary = solubility_W + solubility_secondary = solubility_Cu + boundary = '4to3' + concentration_primary = C_mobile_W + concentration_secondary = C_mobile_Cu + [] + [interface_jump_3to2] + type = SolubilityRatioMaterial + solubility_primary = solubility_Cu + solubility_secondary = solubility_CuCrZr + boundary = '3to2' + concentration_primary = C_mobile_Cu + concentration_secondary = C_mobile_CuCrZr + [] +[] + +[Postprocessors] # Postprocessors compute single scalar values based on node results +# ########################################################### Side Postprocessors !!! Added !!! + [F_recombination] + type = SideDiffusiveFluxAverage # Calculating average flux (integrated) over the specificed boundary + boundary = 'top' + diffusivity = 5.01e-24 # (3.01604928)/(6.02e23)/[gram(T)/m^2] + # diffusivity = 5.508e-19 # (1.0e3)*(1.0e3)/(6.02e23)/(3.01604928) [gram(T)/m^2] + variable = Sc_C_total_W + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [F_permeation] # Uses conversion factor, not really flux + type = SideDiffusiveFluxAverage + boundary = '2to1' + diffusivity = 5.01e-24 # (3.01604928)/(6.02e23)/[gram(T)/m^2] + # diffusivity = 5.508e-19 # (1.0e3)*(1.0e3)/(6.02e23)/(3.01604928) [gram(T)/m^2] + variable = Sc_C_total_CuCrZr + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Tritium_SideFluxIntegral] + type = SideDiffusiveFluxIntegral + boundary = '2to1' + diffusivity = diffusivity_CuCrZr_nonAD + variable = Sc_C_total_CuCrZr + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Scaled_Tritium_Flux] + type = ParsedPostprocessor + expression = '5.01e-24 * Tritium_SideFluxIntegral' + pp_names = Tritium_SideFluxIntegral + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [coolant_heat_flux] # units of W/m2 + type = SideDiffusiveFluxAverage + boundary = '2to1' + diffusivity = thermal_conductivity_CuCrZr + variable = temperature + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + ############################################################ Postprocessors for W (block = 4) + [Int_C_mobile_W] + type = ElementIntegralVariablePostprocessor # Compute the volume integral over a subdomain + variable = C_mobile_W + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_mobile_W] + type = ScalePostprocessor # Scales some other PostProcessor by a real value + value = Int_C_mobile_W + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + [] + [Int_C_trapped_W] + type = ElementIntegralVariablePostprocessor + variable = C_trapped_W + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_trapped_W] + type = ScalePostprocessor + value = Int_C_trapped_W + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + [] + [Int_C_total_W] + type = ElementIntegralVariablePostprocessor + variable = C_total_W + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_total_W] + type = ScalePostprocessor + value = Int_C_total_W + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + [] + # ############################################################ Postprocessors for Cu (block = 3) + [Int_C_mobile_Cu] + type = ElementIntegralVariablePostprocessor + variable = C_mobile_Cu + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_mobile_Cu] + type = ScalePostprocessor + value = Int_C_mobile_Cu + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Int_C_trapped_Cu] + type = ElementIntegralVariablePostprocessor + variable = C_trapped_Cu + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_trapped_Cu] + type = ScalePostprocessor + value = Int_C_trapped_Cu + scaling_factor = 3.44e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Int_C_total_Cu] + type = ElementIntegralVariablePostprocessor + variable = C_total_Cu + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_total_Cu] + type = ScalePostprocessor + value = Int_C_total_Cu + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + # ############################################################ Postprocessors for CuCrZr (block = 2) + [Int_C_mobile_CuCrZr] + type = ElementIntegralVariablePostprocessor + variable = C_mobile_CuCrZr + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_mobile_CuCrZr] + type = ScalePostprocessor + value = Int_C_mobile_CuCrZr + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Int_C_trapped_CuCrZr] + type = ElementIntegralVariablePostprocessor + variable = C_trapped_CuCrZr + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_trapped_CuCrZr] + type = ScalePostprocessor + value = Int_C_trapped_CuCrZr + scaling_factor = 3.44e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [Int_C_total_CuCrZr] + type = ElementIntegralVariablePostprocessor + variable = C_total_CuCrZr + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [ScInt_C_total_CuCrZr] + type = ScalePostprocessor + value = Int_C_total_CuCrZr + scaling_factor = 3.491e10 # (1.0e3)*(1.0e3)*(${tungsten_atomic_density})/(6.02e23)/(3.01604928) [gram(T)/m^2] + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + ############################################################ Postprocessors for others + [dt] + type = TimestepSize # Reports timestep size + [] + [temperature_top] # Reports variable value at a specific point (x,y,z) + type = PointValue + variable = temperature + point = '0 14.0e-3 0' + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [temperature_tube] + type = PointValue + variable = temperature + point = '0 6.0e-3 0' + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + # limit timestep + [timestep_max_pp] # s + type = FunctionValuePostprocessor + function = timestep_function + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_temperature_W] # !!! ADDED !!! + type = ElementExtremeValue + block = 4 + variable = 'temperature' + value_type = max + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_temperature_Cu] # !!! ADDED !!! + type = ElementExtremeValue + block = 3 + variable = 'temperature' + value_type = max + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_temperature_CuCrZr] # !!! ADDED !!! + type = ElementExtremeValue + block = 2 + variable = 'temperature' + value_type = max + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [avg_temperature_W] # !!! ADDED !!! + type = ElementAverageValue + variable = temperature + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [avg_temperature_Cu] # !!! ADDED !!! + type = ElementAverageValue + variable = temperature + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [avg_temperature_CuCrZr] # !!! ADDED !!! + type = ElementAverageValue + variable = temperature + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_concentration_W] # !!! ADDED !!! + type = ElementExtremeValue + variable = 'C_total_W' + value_type = max + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_concentration_Cu] # !!! ADDED !!! + type = ElementExtremeValue + variable = 'C_total_Cu' + value_type = max + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [max_concentration_CuCrZr] # !!! ADDED !!! + type = ElementExtremeValue + variable = 'C_total_CuCrZr' + value_type = max + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [area_W] # !!! ADDED !!! + type = VolumePostprocessor + block = 4 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [area_Cu] # !!! ADDED !!! + type = VolumePostprocessor + block = 3 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [area_CuCrZr] # !!! ADDED !!! + type = VolumePostprocessor + block = 2 + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] + [total_retention] # !!! ADDED !!! + type = SumPostprocessor + values = 'ScInt_C_total_W ScInt_C_total_Cu ScInt_C_total_CuCrZr' + execute_on = 'MULTIAPP_FIXED_POINT_END FINAL' + [] +[] + +[Preconditioning] # https://mooseframework.inl.gov/syntax/Preconditioning/ + [smp] + type = SMP + full = true + [] +[] + +[Executioner] # Solution parameters + type = Transient + scheme = bdf2 + solve_type = NEWTON + petsc_options_iname = '-pc_type' + petsc_options_value = 'lu' + nl_rel_tol = 1e-2 # 1e-6 # Relative independent parameter tolerance + nl_abs_tol = 1e-1 # 1e-7 # Absolute tolerance + end_time = 2.5e1 # 50 ITER shots (3.0e4 s plasma, 2.0e4 SSP) # Total simulation time + automatic_scaling = true + line_search = 'none' + dtmin = 1e-4 # Minimum time step for convergence, time step size is reduced upon non-convergence, but dtmin is an absolute limit. Passing this will result in an error + nl_max_its = 24 # Maximum number of iterations for convergence + [TimeStepper] + type = IterationAdaptiveDT + dt = 20 + optimal_iterations = 15 + iteration_window = 1 + growth_factor = 2.0 + cutback_factor = 0.8 + timestep_limiting_postprocessor = timestep_max_pp + [] +[] + +[Outputs] # Control outputs, include exodus and csv. + execute_on = 'none' +[] + diff --git a/test/tests/divertor_monoblock/steady_state_sobol.i b/test/tests/divertor_monoblock/steady_state_sobol.i new file mode 100644 index 000000000..54fbc7966 --- /dev/null +++ b/test/tests/divertor_monoblock/steady_state_sobol.i @@ -0,0 +1,141 @@ +[StochasticTools] # Designate as the Controller/Main Input +[] +[MultiApps] # Designate a subapp to control later + [runner] + type = SamplerFullSolveMultiApp + sampler = sobol + input_files = 'steady_state_runner.i' + mode = batch-reset + keep_full_output_history = True + [] +[] +[Controls] # Control inputs from Main->Subapp + [cmdline] + type = MultiAppSamplerControl + multi_app = runner + sampler = sobol + param_names = """BCs/temp_top/value + BCs/C_mob_W_top_flux/value + BCs/temp_tube/value""" + [] +[] +[Distributions] # Define probability distributions of parameters for sampling + [H_top] + type = Uniform + lower_bound = 9.5e6 + upper_bound = 10.5e6 + [] + [C_top] + type = Uniform + lower_bound = 7.505e-13 + upper_bound = 8.295e-13 + [] + [H_bot] + type = Uniform + lower_bound = 524.4 + upper_bound = 579.6 + [] +[] +[Samplers] # Sampling methodology using the probability distributions above + [hypercube_1] + type = LatinHypercube + distributions = 'H_top C_top H_bot' + num_rows = 10 # N Samples + seed = 1001 + execute_on = 'PRE_MULTIAPP_SETUP' + [] + [hypercube_2] + type = LatinHypercube + distributions = 'H_top C_top H_bot' + num_rows = 10 # N Samples + seed = 1002 + execute_on = 'PRE_MULTIAPP_SETUP' + [] + [sobol] + type = Sobol + sampler_a = hypercube_1 + sampler_b = hypercube_2 + execute_on = 'PRE_MULTIAPP_SETUP' + [] +[] +[Transfers] # Define values to extract from subapp + [results] + type = SamplerReporterTransfer + from_multi_app = runner + sampler = sobol + stochastic_reporter = results + #execute_on = 'MULTIAPP_FIXED_POINT_END' + from_reporter = """F_permeation/value + Scaled_Tritium_Flux/value + total_retention/value + coolant_heat_flux/value + max_temperature_W/value + max_temperature_Cu/value + max_temperature_CuCrZr/value""" + [] + [matrix_results] + type = SamplerReporterTransfer + from_multi_app = runner + sampler = sobol + stochastic_reporter = matrix + from_reporter = """F_permeation/value + Scaled_Tritium_Flux/value + total_retention/value + coolant_heat_flux/value + max_temperature_W/value + max_temperature_Cu/value + max_temperature_CuCrZr/value""" + [] +[] +[Reporters] + [results] + type = StochasticReporter + execute_on = 'FINAL' # INITIAL TIMESTEP_END MULTIAPP_FIXED_POINT_END + [] + [stats] + type = StatisticsReporter + reporters = """results/results:F_permeation:value + results/results:Scaled_Tritium_Flux:value + results/results:total_retention:value + results/results:coolant_heat_flux:value + results/results:max_temperature_W:value + results/results:max_temperature_Cu:value + results/results:max_temperature_CuCrZr:value""" + compute = 'mean stddev' + ci_method = 'percentile' + ci_levels = """0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 + 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95""" + execute_on = 'FINAL' + [] + [sobol] + type = SobolReporter + sampler = sobol + reporters = """results/results:F_permeation:value + results/results:Scaled_Tritium_Flux:value + results/results:total_retention:value + results/results:coolant_heat_flux:value + results/results:max_temperature_W:value + results/results:max_temperature_Cu:value + results/results:max_temperature_CuCrZr:value""" + ci_levels = """0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 + 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95""" + execute_on = 'FINAL' + [] + [matrix] + type = StochasticMatrix + sampler = sobol + sampler_column_names = """BCs/temp_top/value + BCs/C_mob_W_top_flux/value + BCs/temp_tube/value""" + execute_on = 'FINAL' + parallel_type = ROOT + [] +[] +[Outputs] + [out] + type = JSON + distributed = False + execute_on = 'FINAL' + execute_reporters_on = 'FINAL' + [] +[] diff --git a/test/tests/divertor_monoblock/tests b/test/tests/divertor_monoblock/tests index 9f9937ce6..c1de65714 100644 --- a/test/tests/divertor_monoblock/tests +++ b/test/tests/divertor_monoblock/tests @@ -1,49 +1,84 @@ [Tests] + [initial] issues = '#144' design = 'divertor_monoblock/index.md' - [Shimada2024_input_check] - type = RunApp - input = 'divertor_monoblock.i' - requirement = 'The system shall maintain a working input file to model heat and tritium transport in a divertor monoblock during pulsed operation.' - check_input = True - method = opt + requirement = 'The system shall maintain a working input file to model heat and tritium transport in a divertor monoblock during' + [Shimada2024_input_check] + type = RunApp + input = 'divertor_monoblock.i' + detail = 'pulsed operation (input check only).' + check_input = True + method = opt + [] + [Shimada2024_run] + type = Exodiff + input = 'divertor_monoblock.i' + exodiff = 'divertor_monoblock_exodus.e' + cli_args = 'Outputs/exodus/sync_only=true Executioner/end_time=1600 Mesh/ccmg/rings="1 10 8 40" Executioner/nl_rel_tol=1e-8 Executioner/nl_abs_tol=1e-11' + detail = 'pulsed operation.' + rel_err = 8e-4 # increasing the relative error tolerance because the test is sensitive to the environment. This error is still small for physical problems. + heavy = true + min_parallel = 2 + max_time = 1000 + [] + [Shimada2024_run-physics] + type = Exodiff + input = 'divertor_monoblock_physics.i' + exodiff = 'divertor_monoblock_physics_exodus.e' + cli_args = 'Outputs/exodus/sync_only=true Executioner/end_time=1600 Mesh/ccmg/rings="1 10 8 40" Executioner/nl_rel_tol=1e-8 Executioner/nl_abs_tol=1e-11' + detail = 'pulsed operation using a shorthand physics syntax' + rel_err = 8e-4 # increasing the relative error tolerance because the test is sensitive to the environment. This error is still small for physical problems. + heavy = true + min_parallel = 2 + max_time = 1000 + custom_cmp = 'physics_comp.cmp' + [] + [Shimada2024_run-physics_single-var] + type = CSVDiff + input = 'divertor_monoblock_physics-single-var.i' + csvdiff = 'divertor_monoblock_physics-single-var_out.csv' + cli_args = 'Outputs/exodus/sync_only=true + Executioner/end_time=100 + Mesh/ccmg/rings="1 10 8 40" + Executioner/nl_rel_tol=1e-8 Executioner/nl_abs_tol=1e-11' + detail = 'pulsed operation using a shorthand physics syntax and using a single variable for tracking the tritium concentration.' + rel_err = 8e-4 # increasing the relative error tolerance because the test is sensitive to the environment. This error is still small for physical problems. + abs_zero = 3e-9 + heavy = true + min_parallel = 2 + max_time = 1000 + [] [] - [Shimada2024_run] - type = Exodiff - input = 'divertor_monoblock.i' - exodiff = 'divertor_monoblock_exodus.e' - cli_args = 'Outputs/exodus/sync_only=true Executioner/end_time=1600 Mesh/ccmg/rings="1 10 8 40" Executioner/nl_rel_tol=1e-8 Executioner/nl_abs_tol=1e-11' - requirement = 'The system shall model heat and tritium transport in a divertor monoblock during pulsed operation.' - rel_err = 8e-4 # increasing the relative error tolerance because the test is sensitive to the environment. This error is still small for physical problems. - heavy = true - min_parallel = 2 - max_time = 1000 - [] - [Shimada2024_run-physics] - type = Exodiff - input = 'divertor_monoblock_physics.i' - exodiff = 'divertor_monoblock_physics_exodus.e' - cli_args = 'Outputs/exodus/sync_only=true Executioner/end_time=1600 Mesh/ccmg/rings="1 10 8 40" Executioner/nl_rel_tol=1e-8 Executioner/nl_abs_tol=1e-11' - requirement = 'The system shall model heat and tritium transport in a divertor monoblock during pulsed operation using a shorthand physics syntax' - rel_err = 8e-4 # increasing the relative error tolerance because the test is sensitive to the environment. This error is still small for physical problems. - heavy = true - min_parallel = 2 - max_time = 1000 - custom_cmp = 'physics_comp.cmp' - [] - [Shimada2024_run-physics_single-var] - type = CSVDiff - input = 'divertor_monoblock_physics-single-var.i' - csvdiff = 'divertor_monoblock_physics-single-var_out.csv' - cli_args = 'Outputs/exodus/sync_only=true - Executioner/end_time=100 - Mesh/ccmg/rings="1 10 8 40" - Executioner/nl_rel_tol=1e-8 Executioner/nl_abs_tol=1e-11' - requirement = 'The system shall model heat and tritium transport in a divertor monoblock during pulsed operation using a shorthand physics syntax and using a single variable for tracking the tritium concentration.' - rel_err = 8e-4 # increasing the relative error tolerance because the test is sensitive to the environment. This error is still small for physical problems. - abs_zero = 3e-9 - heavy = true - min_parallel = 2 - max_time = 1000 + [stochastics] + issues = '#281' + design = 'divertor_monoblock/sensitivity.md' + requirement = 'The system shall model heat and tritium transporter for the sensitvity study of a divertor monoblock during' + [steady_run] + type = JSONDiff + input = 'steady_state_sobol.i' + detail = 'steady operation' + cli_args = 'runner:Mesh/ccmg/rings="1 3 3 3" runner:Mesh/ccmg/num_sectors=4 Outputs/out/file_base=steady_state_sobol' + jsondiff = 'steady_state_sobol.json' + heavy = true + min_parallel = 4 + [] + [shutdown_run] + type = JSONDiff + input = 'shutdown_transient_sobol.i' + detail = 'a shutdown transient after steady operation' + cli_args = 'runner:Mesh/ccmg/rings="1 3 3 3" runner:Mesh/ccmg/num_sectors=4 Outputs/out/file_base=shutdown_transient_sobol' + jsondiff = 'shutdown_transient_sobol.json' + heavy = true + min_parallel = 4 + [] + [elm_run] + type = JSONDiff + input = 'elm_transient_sobol.i' + detail = 'an ELM transient after steady operation' + cli_args = 'runner:Mesh/ccmg/rings="1 3 3 3" Outputs/out/file_base=elm_transient_sobol runner:Executioner/end_time=2.000005e2 Samplers/hypercube_1/num_rows=5 Samplers/hypercube_2/num_rows=5 runner:/Mesh/ccmg/num_sectors=4' + jsondiff = 'elm_transient_sobol.json' + heavy = true + min_parallel = 4 + [] [] [] diff --git a/test/tests/ver-1kb/comparison_ver-1kb.py b/test/tests/ver-1kb/comparison_ver-1kb.py index 44dff9362..caf0ec36b 100644 --- a/test/tests/ver-1kb/comparison_ver-1kb.py +++ b/test/tests/ver-1kb/comparison_ver-1kb.py @@ -49,7 +49,7 @@ ax.set_yticks(np.arange(0, 3, 1)) ax.set_xlim(0,TMAP8_time.max()) ax.set_xlabel('Time (s)') -ax.set_ylabel(r"Concentrations ratio $C_{\text{encl1}} / C_{\text{encl2}}$") +ax.set_ylabel(r"Concentrations ratio $C_{\mathrm{encl1}} / C_{\mathrm{encl2}}$") ax.legend(loc="best") ax.grid(which='major', color='0.65', linestyle='--', alpha=0.3) RMSE = np.sqrt(np.mean((concentration_ratio[1:]-solubility_ratio)**2) ) @@ -118,7 +118,7 @@ ax.set_yticks(np.arange(0, 21, 10)) ax.set_xlim(0,TMAP8_time.max()) ax.set_xlabel('Time (s)') -ax.set_ylabel(r"Concentrations ratio $C_{\text{encl1}} / C_{\text{encl2}}$") +ax.set_ylabel(r"Concentrations ratio $C_{\mathrm{encl1}} / C_{\mathrm{encl2}}$") ax.legend(loc="best") ax.grid(which='major', color='0.65', linestyle='--', alpha=0.3) RMSE = np.sqrt(np.mean((concentration_ratio_k10[1:]-solubility_ratio)**2)) diff --git a/test/tests/ver-1kc-1/comparison_ver-1kc-1.py b/test/tests/ver-1kc-1/comparison_ver-1kc-1.py index 89a3180e0..5e9c03ae1 100644 --- a/test/tests/ver-1kc-1/comparison_ver-1kc-1.py +++ b/test/tests/ver-1kc-1/comparison_ver-1kc-1.py @@ -51,7 +51,7 @@ ax.set_yticks(np.arange(0, 21, 10)) ax.set_xlim(0,TMAP8_time_k10.max()) ax.set_xlabel('Time (s)') -ax.set_ylabel(r"Concentrations ratio $C_{\text{encl1}} / \sqrt{C_{\text{encl2}}}$") +ax.set_ylabel(r"Concentrations ratio $C_{\mathrm{encl1}} / \sqrt{C_{\mathrm{encl2}}}$") ax.legend(loc="best") ax.grid(which='major', color='0.65', linestyle='--', alpha=0.3) RMSE = np.sqrt(np.mean((concentration_ratio_k10[1:]-solubility_ratio)**2)) diff --git a/test/tests/ver-1kc-2/comparison_ver-1kc-2.py b/test/tests/ver-1kc-2/comparison_ver-1kc-2.py index bb40249ec..030a39a3d 100644 --- a/test/tests/ver-1kc-2/comparison_ver-1kc-2.py +++ b/test/tests/ver-1kc-2/comparison_ver-1kc-2.py @@ -79,7 +79,7 @@ ax.set_yticks(np.arange(0, 21, 10)) ax.set_xlim(0,TMAP8_time_k10.max()) ax.set_xlabel('Time (s)') -ax.set_ylabel(r"Concentrations ratio $C_{\text{encl1}} / \sqrt{C_{\text{encl2}}}$") +ax.set_ylabel(r"Concentrations ratio $C_{\mathrm{encl1}} / \sqrt{C_{\mathrm{encl2}}}$") ax.legend(loc="best") ax.grid(which='major', color='0.65', linestyle='--', alpha=0.3) RMSE = np.sqrt(np.mean((concentration_ratio_H2_k10[1:]-solubility_ratio)**2)) @@ -100,7 +100,7 @@ ax.set_yticks(np.arange(0, 21, 10)) ax.set_xlim(0,TMAP8_time_k10.max()) ax.set_xlabel('Time (s)') -ax.set_ylabel(r"Concentrations ratio $C_{\text{encl1}} / \sqrt{C_{\text{encl2}}}$") +ax.set_ylabel(r"Concentrations ratio $C_{\mathrm{encl1}} / \sqrt{C_{\mathrm{encl2}}}$") ax.legend(loc="best") ax.grid(which='major', color='0.65', linestyle='--', alpha=0.3) RMSE = np.sqrt(np.mean((concentration_ratio_T2_k10[1:]-solubility_ratio)**2)) @@ -121,7 +121,7 @@ ax.set_yticks(np.arange(0, 21, 10)) ax.set_xlim(0,TMAP8_time_k10.max()) ax.set_xlabel('Time (s)') -ax.set_ylabel(r"Concentrations ratio $C_{\text{encl1}} / \sqrt{C_{\text{encl2}}}$") +ax.set_ylabel(r"Concentrations ratio $C_{\mathrm{encl1}} / \sqrt{C_{\mathrm{encl2}}}$") ax.legend(loc="best") ax.grid(which='major', color='0.65', linestyle='--', alpha=0.3) RMSE = np.sqrt(np.mean((concentration_ratio_HT_k10[1:]-solubility_ratio)**2)) @@ -159,7 +159,7 @@ ax.axhline(y=2, color='tab:green', linestyle='--', label='TMAP7 Equilibrium Constant') ax.set_xlabel('Time (s)') ax.set_xlim(0,TMAP8_time_k10.max()) -ax.set_ylabel(r"Equilibrium constant $P_{\text{HT}} / \sqrt{P_{\text{H}_2} P_{\text{T}_2}}$") +ax.set_ylabel(r"Equilibrium constant $P_{\mathrm{HT}} / \sqrt{P_{\mathrm{H}_2} P_{\mathrm{T}_2}}$") ax.set_ylim(bottom=0) ax.legend(loc="best") ax.grid(which='major', color='0.65', linestyle='--', alpha=0.3) diff --git a/test/tests/ver-1kd/comparison_ver-1kd.py b/test/tests/ver-1kd/comparison_ver-1kd.py index f8bf99d77..f45845352 100644 --- a/test/tests/ver-1kd/comparison_ver-1kd.py +++ b/test/tests/ver-1kd/comparison_ver-1kd.py @@ -79,7 +79,7 @@ ax.set_yticks(np.arange(0, 21, 10)) ax.set_xlim(0,TMAP8_time_k10.max()) ax.set_xlabel('Time (s)') -ax.set_ylabel(r"Concentrations ratio $C_{\text{encl1}} / \sqrt{C_{\text{encl2}}}$") +ax.set_ylabel(r"Concentrations ratio $C_{\mathrm{encl1}} / \sqrt{C_{\mathrm{encl2}}}$") ax.legend(loc="best") ax.grid(which='major', color='0.65', linestyle='--', alpha=0.3) RMSE = np.sqrt(np.mean((concentration_ratio_H2_k10[1:]-solubility_ratio)**2)) @@ -100,7 +100,7 @@ ax.set_yticks(np.arange(0, 21, 10)) ax.set_xlim(0,TMAP8_time_k10.max()) ax.set_xlabel('Time (s)') -ax.set_ylabel(r"Concentrations ratio $C_{\text{encl1}} / \sqrt{C_{\text{encl2}}}$") +ax.set_ylabel(r"Concentrations ratio $C_{\mathrm{encl1}} / \sqrt{C_{\mathrm{encl2}}}$") ax.legend(loc="best") ax.grid(which='major', color='0.65', linestyle='--', alpha=0.3) RMSE = np.sqrt(np.mean((concentration_ratio_T2_k10[1:]-solubility_ratio)**2)) @@ -121,7 +121,7 @@ ax.set_yticks(np.arange(0, 21, 10)) ax.set_xlim(0,TMAP8_time_k10.max()) ax.set_xlabel('Time (s)') -ax.set_ylabel(r"Concentrations ratio $C_{\text{encl1}} / \sqrt{C_{\text{encl2}}}$") +ax.set_ylabel(r"Concentrations ratio $C_{\mathrm{encl1}} / \sqrt{C_{\mathrm{encl2}}}$") ax.legend(loc="best") ax.grid(which='major', color='0.65', linestyle='--', alpha=0.3) RMSE = np.sqrt(np.mean((concentration_ratio_HT_k10[1:]-solubility_ratio)**2)) @@ -159,7 +159,7 @@ ax.axhline(y=2, color='tab:green', linestyle='--', label='TMAP7 Equilibrium Constant') ax.set_xlabel('Time (s)') ax.set_xlim(0,TMAP8_time_k10.max()) -ax.set_ylabel(r"Equilibrium constant $P_{\text{HT}} / \sqrt{P_{\text{H}_2} P_{\text{T}_2}}$") +ax.set_ylabel(r"Equilibrium constant $P_{\mathrm{HT}} / \sqrt{P_{\mathrm{H}_2} P_{\mathrm{T}_2}}$") ax.set_ylim(bottom=0) ax.legend(loc="best") ax.grid(which='major', color='0.65', linestyle='--', alpha=0.3)