Basic Floris v4 compatibility

examples_artificial_data/00_setup_floris_model/01_visualize_flowfield.py

@@ -24,7 +24,7 @@
     fi.reinitialize(
         wind_directions=[wind_direction],
         wind_speeds=[wind_speed],
-        turbulence_intensity=turbulence_intensity,
+        turbulence_intensities=[turbulence_intensity],
     )
     plot_floris_layout(fi, plot_terrain=False)
 
@@ -37,10 +37,9 @@
     )
 
     fig, ax = plt.subplots(figsize=(9, 6))
-    im = visualize_cut_plane(horizontal_plane, ax=ax, title=None)
+    im = visualize_cut_plane(horizontal_plane, ax=ax, title=None, color_bar=True)
     ax.set_xlabel("x coordinate (m)")
     ax.set_ylabel("y coordinate (m)")
-    plt.colorbar(im, ax=ax)
     fig.suptitle(
         "Inflow: {:.1f} m/s, {:.1f} deg, {:.1f} % turbulence.".format(
             wind_speed, wind_direction, turbulence_intensity * 100.0

examples_artificial_data/00_setup_floris_model/02_precalculate_floris_solutions.py

@@ -2,7 +2,6 @@
 from time import perf_counter as timerpc
 
 import numpy as np
-from floris.tools import ParallelComputingInterface
 
 from flasc.utilities import floris_tools as ftools
 from flasc.utilities.utilities_examples import load_floris_artificial as load_floris
@@ -22,14 +21,14 @@
         start_time = timerpc()
         print("Precalculating FLORIS table for '{:s}' model...".format(wake_model))
         fi, turbine_weights = load_floris(wake_model=wake_model)
-        fi_pci = ParallelComputingInterface(
-            fi=fi,
-            max_workers=max_workers,
-            n_wind_direction_splits=max_workers,
-            print_timings=True,
-        )
+        # fi_pci = ParallelComputingInterface(
+        #     fi=fi,
+        #     max_workers=max_workers,
+        #     n_wind_direction_splits=max_workers,
+        #     print_timings=True,
+        # )
         df_fi_approx = ftools.calc_floris_approx_table(
-            fi=fi_pci,
+            fi=fi,  # fi=fi_pci,
             wd_array=np.arange(0.0, 360.01, 3.0),
             ws_array=np.arange(1.0, 30.01, 1.0),
             ti_array=[0.03, 0.06, 0.09, 0.12, 0.15],

examples_artificial_data/03_energy_ratio/04_energy_ratio_in_turbine_array.py

@@ -120,7 +120,7 @@ def plot_energy_ratios(turbine_array, results_energy_ratio, ax=None, label=None)
         _, ax = plt.subplots(figsize=(7.0, 3.0))
 
     x = range(len(results_energy_ratio))
-    color = next(ax._get_lines.prop_cycler)["color"]
+    color = ax._get_lines.get_next_color()
     ax.fill_between(
         x,
         results_energy_ratio["baseline_lb"],

examples_artificial_data/03_energy_ratio/06_wake_steering_example.py

@@ -66,14 +66,14 @@
     # Compute the power of the second turbine for two cases
     # Baseline: The front turbine is aligned to the wind
     # WakeSteering: The front turbine is yawed 25 deg
-    fi.reinitialize(wind_speeds=ws_array, wind_directions=wd_array, time_series=True)
+    fi.reinitialize(wind_speeds=ws_array, wind_directions=wd_array)
     fi.calculate_wake()
     power_baseline_ref = fi.get_turbine_powers().squeeze()[:, 0].flatten()
     power_baseline_control = fi.get_turbine_powers().squeeze()[:, 1].flatten()
     power_baseline_downstream = fi.get_turbine_powers().squeeze()[:, 2].flatten()
 
-    yaw_angles = np.zeros([len(t), 1, 3]) * 25
-    yaw_angles[:, :, 1] = 25  # Set control turbine yaw angles to 25 deg
+    yaw_angles = np.zeros([len(t), 3]) * 25
+    yaw_angles[:, 1] = 25  # Set control turbine yaw angles to 25 deg
     fi.calculate_wake(yaw_angles=yaw_angles)
     power_wakesteering_ref = fi.get_turbine_powers().squeeze()[:, 0].flatten()
     power_wakesteering_control = fi.get_turbine_powers().squeeze()[:, 1].flatten()

examples_artificial_data/03_energy_ratio/07_wake_steering_total_uplift.py

@@ -67,14 +67,14 @@
     # Compute the power of the second turbine for two cases
     # Baseline: The front turbine is aligned to the wind
     # WakeSteering: The front turbine is yawed 25 deg
-    fi.reinitialize(wind_speeds=ws_array, wind_directions=wd_array, time_series=True)
+    fi.reinitialize(wind_speeds=ws_array, wind_directions=wd_array)
     fi.calculate_wake()
     power_baseline_ref = fi.get_turbine_powers().squeeze()[:, 0].flatten()
     power_baseline_control = fi.get_turbine_powers().squeeze()[:, 1].flatten()
     power_baseline_downstream = fi.get_turbine_powers().squeeze()[:, 2].flatten()
 
-    yaw_angles = np.zeros([len(t), 1, 3]) * 25
-    yaw_angles[:, :, 1] = 25  # Set control turbine yaw angles to 25 deg
+    yaw_angles = np.zeros([len(t), 3]) * 25
+    yaw_angles[:, 1] = 25  # Set control turbine yaw angles to 25 deg
     fi.calculate_wake(yaw_angles=yaw_angles)
     power_wakesteering_ref = fi.get_turbine_powers().squeeze()[:, 0].flatten()
     power_wakesteering_control = fi.get_turbine_powers().squeeze()[:, 1].flatten()

examples_artificial_data/floris_input_artificial/cc.yaml

@@ -30,7 +30,8 @@ farm:
 flow_field:
   air_density: 1.225
   reference_wind_height: -1 # -1 is code for use the hub height
-  turbulence_intensity: 0.06
+  turbulence_intensities:
+  - 0.06
   wind_directions:
   - 270.0
   wind_shear: 0.12

examples_artificial_data/floris_input_artificial/emgauss.yaml

@@ -112,7 +112,8 @@ flow_field:
 
   ###
   # The level of turbulence intensity level in the wind.
-  turbulence_intensity: 0.06
+  turbulence_intensities:
+  - 0.06
 
   ###
   # The wind directions to include in the simulation.

examples_artificial_data/floris_input_artificial/gch.yaml

@@ -112,7 +112,8 @@ flow_field:
 
   ###
   # The level of turbulence intensity level in the wind.
-  turbulence_intensity: 0.06
+  turbulence_intensities:
+  - 0.06
 
   ###
   # The wind directions to include in the simulation.

examples_artificial_data/floris_input_artificial/jensen.yaml

@@ -30,7 +30,8 @@ farm:
 flow_field:
   air_density: 1.225
   reference_wind_height: -1 # -1 is code for use the hub height
-  turbulence_intensity: 0.06
+  turbulence_intensities:
+  - 0.06
   wind_directions:
   - 270.0
   wind_shear: 0.12

examples_artificial_data/floris_input_artificial/turbopark.yaml

@@ -30,7 +30,8 @@ farm:
 flow_field:
   air_density: 1.225
   reference_wind_height: 90.0
-  turbulence_intensity: 0.06
+  turbulence_intensities:
+  - 0.06
   wind_directions:
   - 270.0
   wind_shear: 0.12

examples_smarteole/01_precalculate_floris_solutions.ipynb

@@ -1,45 +1,61 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Precalculate FLORIS Solutions\n",
     "\n",
     "Use FLASC tools to precalculate the FLORIS solutions across wind speeds and directions without wake steering to enable faster comparisons in later steps. The FLORIS solutions based on four different wake models are then saved in the \"/precalculated_floris_solutions\" folder. "
-   ],
-   "metadata": {},
-   "attachments": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "from time import perf_counter as timerpc\n",
     "\n",
     "import numpy as np\n",
-    "from floris.tools import ParallelComputingInterface\n",
     "\n",
+    "# from floris.tools import ParallelComputingInterface\n",
     "from flasc.utilities import floris_tools as ftools\n",
     "from flasc.utilities.utilities_examples import load_floris_smarteole as load_floris"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# User settings\n",
     "max_workers = 16\n",
     "wake_models = [\"jensen\", \"turbopark\", \"gch\", \"cc\"]"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precalculating FLORIS table for 'jensen' model...\n",
+      "Generating a df_approx table of FLORIS solutions covering a total of 18150 cases.\n",
+      "Finished calculating the FLORIS solutions for the dataframe.\n",
+      "Computation time: 5.62 s\n",
+      "FLORIS table for 'turbopark' model exists. Skipping...\n",
+      "FLORIS table for 'gch' model exists. Skipping...\n",
+      "FLORIS table for 'cc' model exists. Skipping...\n"
+     ]
+    }
+   ],
    "source": [
     "# Precalculate FLORIS solutions\n",
     "root_path = Path.cwd() / \"precalculated_floris_solutions\"\n",
@@ -54,69 +70,31 @@
     "    start_time = timerpc()\n",
     "    print(\"Precalculating FLORIS table for '{:s}' model...\".format(wake_model))\n",
     "    fi, _ = load_floris(wake_model=wake_model)\n",
-    "    fi_pci = ParallelComputingInterface(\n",
-    "        fi=fi,\n",
-    "        max_workers=max_workers,\n",
-    "        n_wind_direction_splits=max_workers,\n",
-    "        print_timings=True,\n",
-    "    )\n",
+    "    # fi_pci = ParallelComputingInterface(\n",
+    "    #     fi=fi,\n",
+    "    #     max_workers=max_workers,\n",
+    "    #     n_wind_direction_splits=max_workers,\n",
+    "    #     print_timings=True,\n",
+    "    # )\n",
     "    df_fi_approx = ftools.calc_floris_approx_table(\n",
-    "        fi=fi_pci,\n",
+    "        fi=fi,  # fi_pci,\n",
     "        wd_array=np.arange(0.0, 360.01, 3.0),\n",
     "        ws_array=np.arange(1.0, 30.01, 1.0),\n",
     "        ti_array=[0.03, 0.06, 0.09, 0.12, 0.15],\n",
     "    )\n",
     "    end_time = timerpc()\n",
     "    print(\"Computation time: {:.2f} s\".format(end_time - start_time))\n",
     "    df_fi_approx.to_feather(fn)"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Precalculating FLORIS table for 'jensen' model...\n",
-      "Generating a df_approx table of FLORIS solutions covering a total of 18150 cases.\n",
-      "===============================================================================\n",
-      "Total time spent for parallel calculation (16 workers): 6.492 s\n",
-      "  Time spent in parallel preprocessing: 0.005 s\n",
-      "  Time spent in parallel loop execution: 6.486 s.\n",
-      "  Time spent in parallel postprocessing: 0.000 s\n",
-      "===============================================================================\n",
-      "Total time spent for parallel calculation (16 workers): 6.612 s\n",
-      "  Time spent in parallel preprocessing: 0.005 s\n",
-      "  Time spent in parallel loop execution: 6.607 s.\n",
-      "  Time spent in parallel postprocessing: 0.000 s\n",
-      "===============================================================================\n",
-      "Total time spent for parallel calculation (16 workers): 7.267 s\n",
-      "  Time spent in parallel preprocessing: 0.005 s\n",
-      "  Time spent in parallel loop execution: 7.261 s.\n",
-      "  Time spent in parallel postprocessing: 0.000 s\n",
-      "===============================================================================\n",
-      "Total time spent for parallel calculation (16 workers): 7.577 s\n",
-      "  Time spent in parallel preprocessing: 0.005 s\n",
-      "  Time spent in parallel loop execution: 7.572 s.\n",
-      "  Time spent in parallel postprocessing: 0.000 s\n",
-      "===============================================================================\n",
-      "Total time spent for parallel calculation (16 workers): 8.281 s\n",
-      "  Time spent in parallel preprocessing: 0.005 s\n",
-      "  Time spent in parallel loop execution: 8.275 s.\n",
-      "  Time spent in parallel postprocessing: 0.000 s\n",
-      "Finished calculating the FLORIS solutions for the dataframe.\n",
-      "Computation time: 36.74 s\n",
-      "FLORIS table for 'turbopark' model exists. Skipping...\n",
-      "FLORIS table for 'gch' model exists. Skipping...\n",
-      "FLORIS table for 'cc' model exists. Skipping...\n"
-     ]
-    }
-   ],
-   "metadata": {}
+   ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "96c53852a1e56d9fbc8381f88ff3256056a2f574c5e86cd3dfe6ce1bc9d68e6a"
+  },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.10.4 64-bit ('flasc-reqs': conda)"
+   "display_name": "Python 3.10.4 64-bit ('flasc-reqs': conda)",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -142,9 +120,6 @@
    "toc_position": {},
    "toc_section_display": true,
    "toc_window_display": false
-  },
-  "interpreter": {
-   "hash": "96c53852a1e56d9fbc8381f88ff3256056a2f574c5e86cd3dfe6ce1bc9d68e6a"
   }
  },
  "nbformat": 4,