Merge pull request #945 from NREL/develop

FLORIS v4.1.1

README.md

@@ -3,7 +3,7 @@
 FLORIS is a controls-focused wind farm simulation software incorporating
 steady-state engineering wake models into a performance-focused Python
 framework. It has been in active development at NREL since 2013 and the latest
-release is [FLORIS v4.1](https://github.com/NREL/floris/releases/latest).
+release is [FLORIS v4.1.1](https://github.com/NREL/floris/releases/latest).
 Online documentation is available at https://nrel.github.io/floris.
 
 The software is in active development and engagement with the development team
@@ -79,7 +79,7 @@ PACKAGE CONTENTS
     wind_data
 
 VERSION
-    4.1
+    4.1.1
 
 FILE
     ~/floris/floris/__init__.py

docs/_config.yml

@@ -3,7 +3,7 @@
 
 title: FLORIS
 author: National Renewable Energy Laboratory
-logo: gch.gif
+logo: docs_image.png
 copyright: '2023'
 only_build_toc_files: false
 

docs/heterogeneous_map.ipynb

@@ -4,7 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# HeterogeneousMap"
+    "(heterogeneous_map)=\n",
+    "\n",
+    "# Heterogeneous Map"
    ]
   },
   {

docs/layout_optimization.md

@@ -1,6 +1,6 @@
 
 (layout_optimization)=
-# Layout optimization
+# Layout Optimization
 
 The FLORIS package provides layout optimization tools to place turbines within a specified
 boundary area to optimize annual energy production (AEP) or wind plant value. Layout
@@ -59,8 +59,8 @@ turbine placement
 - Set up to run cheap constraint checks prior to more expensive objective function evaluations
 to accelerate optimization
 
-The algorithm, described in full in an upcoming paper that will be linked here when it is
-publicly available, moves a random turbine and random distance in a random direction; checks
+The algorithm, described in full in {cite:t}`SinnerFleming2024grs`,
+moves a random turbine and random distance in a random direction; checks
 that constraints are satisfied; evaluates the objective function (AEP or value); and then
 commits to the move if there is an objective function improvement. The main tuning parameter
 is the probability mass function for the random movement distance, which is a dictionary to be

docs/references.bib

@@ -272,3 +272,17 @@ @Article{bay_2022
 URL = {https://wes.copernicus.org/preprints/wes-2022-17/},
 DOI = {10.5194/wes-2022-17}
 }
+
+@article{SinnerFleming2024grs,
+doi = {10.1088/1742-6596/2767/3/032036},
+url = {https://dx.doi.org/10.1088/1742-6596/2767/3/032036},
+year = {2024},
+month = {jun},
+publisher = {IOP Publishing},
+volume = {2767},
+number = {3},
+pages = {032036},
+author = {Michael Sinner and Paul Fleming},
+title = {Robust wind farm layout optimization},
+journal = {Journal of Physics: Conference Series},
+}

examples/003_wind_data_objects.py

@@ -149,15 +149,15 @@
 # Aggregating and Resampling the Wind Rose
 ##################################################
 
-# The aggregate function allows for aggregation of the wind rose data into
+# The downsample function allows for aggregation of the wind rose data into
 # fewer wind direction and wind speed bins.
 # Note it will throw an error if the step sizes passed in are smaller than the
 # step sizes of the original data.
-wind_rose_aggregate = wind_rose.aggregate(wd_step=10, ws_step=2)
+wind_rose_aggregate = wind_rose.downsample(wd_step=10, ws_step=2)
 
-# For upsampling, the resample_by_interpolation function can be used to interpolate
+# For upsampling, the upsample function can be used to interpolate
 # the wind rose data to a finer grid.  It can use either linear or nearest neighbor
-wind_rose_resample = wind_rose.resample_by_interpolation(wd_step=0.5, ws_step=0.25)
+wind_rose_resample = wind_rose.upsample(wd_step=0.5, ws_step=0.25)
 
 ##################################################
 # Setting turbulence intensity
@@ -224,7 +224,7 @@
 # bins for which frequency is zero are not simulated.  This can be changed by setting the
 # compute_zero_freq_occurrence parameter to True.
 wind_directions = np.array([200.0, 300.0])
-wind_speeds = np.array([5.0, 1.00])
+wind_speeds = np.array([5.0, 10.0])
 freq_table = np.array([[0.5, 0], [0.5, 0]])
 wind_rose = WindRose(
     wind_directions=wind_directions, wind_speeds=wind_speeds, ti_table=0.06, freq_table=freq_table

examples/examples_layout_optimization/002_optimize_layout_with_heterogeneity.py

@@ -26,10 +26,10 @@
 
 # Setup 2 wind directions (due east and due west)
 # and 1 wind speed with uniform probability
-wind_directions = np.array([270.0, 90.0])
+wind_directions = np.array([90.0, 270.0])
 n_wds = len(wind_directions)
-wind_speeds = [8.0] * np.ones_like(wind_directions)
-turbulence_intensities = 0.06 * np.ones_like(wind_directions)
+wind_speeds = np.array([8.0])
+
 # Shape frequency distribution to match number of wind directions and wind speeds
 freq_table = np.ones((len(wind_directions), len(wind_speeds)))
 freq_table = freq_table / freq_table.sum()
@@ -106,7 +106,7 @@
 fig = plt.gcf()
 sm = ax.tricontourf(x_locs, y_locs, speed_multipliers[0], cmap="coolwarm")
 fig.colorbar(sm, ax=ax, label="Speed multiplier")
-ax.legend(["Initial layout", "Optimized layout", "Optimization boundary"])
+ax.legend(["_Optimization boundary", "Initial layout", "Optimized layout" ])
 ax.set_title("Geometric yaw disabled")
 
 
@@ -144,7 +144,7 @@
 fig = plt.gcf()
 sm = ax.tricontourf(x_locs, y_locs, speed_multipliers[0], cmap="coolwarm")
 fig.colorbar(sm, ax=ax, label="Speed multiplier")
-ax.legend(["Initial layout", "Optimized layout", "Optimization boundary"])
+ax.legend(["_Optimization boundary", "Initial layout", "Optimized layout"])
 ax.set_title("Geometric yaw enabled")
 
 print(