diff --git a/py_wake/examples/data/iea34_130rwt/_iea34_130rwt.py b/py_wake/examples/data/iea34_130rwt/_iea34_130rwt.py
index dafb18ca8a3e764bcf9edec362be8f71a4d0a135..aec9f0133884b22202d9cbd7f3b7f9745406aa20 100644
--- a/py_wake/examples/data/iea34_130rwt/_iea34_130rwt.py
+++ b/py_wake/examples/data/iea34_130rwt/_iea34_130rwt.py
@@ -37,7 +37,7 @@ class IEA34_130_PowerCtSurrogate(PowerCtSurrogate):
return power, ct
-class TreeRegionLoadSurrogates(FunctionSurrogates):
+class ThreeRegionLoadSurrogates(FunctionSurrogates):
def __init__(self, function_surrogate_lst, input_parser):
FunctionSurrogates.__init__(self, function_surrogate_lst, input_parser)
self.ws_cutin = function_surrogate_lst[0][0].wind_speed_cut_in
@@ -68,35 +68,37 @@ class TreeRegionLoadSurrogates(FunctionSurrogates):
class IEA34_130_Base(WindTurbine):
+ load_sensors = ['del_blade_flap', 'del_blade_edge', 'del_tower_bottom_fa', 'del_tower_bottom_ss',
+ 'del_tower_top_torsion']
+ set_names = ['below_cut_in', 'operating', 'above_cut_out']
+
def __init__(self, powerCtFunction, loadFunction):
WindTurbine.__init__(self, 'IEA 3.4MW', diameter=130, hub_height=110,
powerCtFunction=powerCtFunction,
loadFunction=loadFunction)
+ for sensor, fs_lst in zip(self.load_sensors, self.loadFunction.function_surrogate_lst):
+ for fs in fs_lst:
+ fs.output_channel_name = sensor
class IEA34_130_1WT_Surrogate(IEA34_130_Base):
def __init__(self):
- sensors = ['del_blade_flap', 'del_blade_edge', 'del_tower_bottom_fa', 'del_tower_bottom_ss',
- 'del_tower_top_torsion']
surrogate_path = Path(example_data_path) / 'iea34_130rwt' / 'one_turbine'
- set_names = ['below_cut_in', 'operating', 'above_cut_out']
- loadFunction = TreeRegionLoadSurrogates(
- [[TensorflowSurrogate(surrogate_path / s, n) for n in set_names] for s in sensors],
- input_parser=lambda ws, TI_eff=.1, Alpha=0: [TI_eff, ws, Alpha])
+ loadFunction = ThreeRegionLoadSurrogates(
+ [[TensorflowSurrogate(surrogate_path / s, n) for n in self.set_names] for s in self.load_sensors],
+ input_parser=lambda ws, TI_eff=.1, Alpha=0: [ws, TI_eff, Alpha])
powerCtFunction = IEA34_130_PowerCtSurrogate(
'one_turbine',
- input_parser=lambda ws, TI_eff, Alpha=0: [TI_eff, ws, Alpha])
+ input_parser=lambda ws, TI_eff, Alpha=0: [ws, TI_eff, Alpha])
IEA34_130_Base.__init__(self, powerCtFunction=powerCtFunction, loadFunction=loadFunction)
class IEA34_130_2WT_Surrogate(IEA34_130_Base):
def __init__(self):
- sensors = ['del_blade_flap']
surrogate_path = Path(example_data_path) / 'iea34_130rwt' / 'two_turbines'
- set_names = ['below_cut_in', 'operating', 'above_cut_out']
- loadFunction = TreeRegionLoadSurrogates(
- [[TensorflowSurrogate(surrogate_path / s, n) for n in set_names] for s in sensors],
+ loadFunction = ThreeRegionLoadSurrogates(
+ [[TensorflowSurrogate(surrogate_path / s, n) for n in self.set_names] for s in self.load_sensors],
input_parser=self.get_input)
self.max_dist = loadFunction.function_surrogate_lst[0][0].input_scaler.data_max_[4]
self.max_angle = loadFunction.function_surrogate_lst[0][0].input_scaler.data_max_[3]
@@ -125,6 +127,7 @@ def main():
import matplotlib.pyplot as plt
u = np.arange(3, 28, .1)
+
# ===============================================================================================================
# IEA34_130_1WT_Surrogate
# ===============================================================================================================
@@ -170,7 +173,7 @@ def main():
plt.figure()
site = Hornsrev1Site()
x, y = [0, 1000], [0, 0]
- sim_res = NOJ(site, wt, turbulenceModel=STF2017TurbulenceModel())(x, y, ws=np.arange(6, 25), Alpha=.12)
+ sim_res = NOJ(site, wt, turbulenceModel=STF2017TurbulenceModel())(x, y, ws=np.arange(3, 28), Alpha=.12)
load_wd_averaged = sim_res.loads(normalize_probabilities=True, method='OneWT_WDAvg')
loads = sim_res.loads(normalize_probabilities=True, method='OneWT')
loads.DEL.isel(sensor=0, wt=0).plot()
diff --git a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/extra_data.json b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/extra_data.json
index f1efc2623c3298b49fce9035bbbcf4c7ac05969a..44318afd5f6e14b990eb00ef55755f53ae965a2c 100644
--- a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/extra_data.json
+++ b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/extra_data.json
@@ -1,7 +1,7 @@
{
"input_channel_names": [
- "ti",
"ws",
+ "ti",
"shear"
],
"output_channel_name": "MomentMy Mbdy:blade1 nodenr: 1 coo: blade1 blade root moment blade1",
@@ -11,103 +11,103 @@
"input_scalers": {
"below_cut_in": {
"feature_range": [
- -3.0,
- 3.0
+ -4.0,
+ 4.0
],
"copy": true,
"n_features_in_": 3,
- "n_samples_seen_": 290,
+ "n_samples_seen_": 308,
"scale_": [
- 4.093931847815023,
- 1.5527559984108794,
- 10.034296914116865
+ 2.077667186797471,
+ 5.45739665679305,
+ 13.37906255215582
],
"min_": [
- -3.00460564098673,
- -3.206397077344649,
- -2.0024497794364287
+ -4.273422763436555,
+ -4.0044113992691726,
+ -2.669933039248572
],
"data_min_": [
- 0.0011249921,
- 0.1329230591,
+ 0.1316008479,
+ 0.000808334,
-0.0994140625
],
"data_max_": [
+ 3.9820731713,
1.4667087446,
- 3.997020191,
0.4985351562
],
"data_range_": [
- 1.4655837525,
- 3.8640971319,
+ 3.8504723234,
+ 1.4659004106,
0.5979492187
]
},
"above_cut_out": {
"feature_range": [
- -3.0,
- 3.0
+ -4.0,
+ 4.0
],
"copy": true,
"n_features_in_": 3,
- "n_samples_seen_": 138,
+ "n_samples_seen_": 127,
"scale_": [
- 17.108732300045514,
- 1.2235952583871186,
- 10.058939096267192
+ 1.7038105156133243,
+ 22.784779731253494,
+ 13.411918795022922
],
"min_": [
- -3.028362517654116,
- -33.59896481247119,
- -2.0029469543104126
+ -46.75667134943951,
+ -4.037787449843989,
+ -2.67059593908055
],
"data_min_": [
- 0.0016577802,
- 25.0074234946,
+ 25.0947338085,
+ 0.0016584514,
-0.0991210938
],
"data_max_": [
- 0.3523558854,
- 29.9110057526,
+ 29.7900916119,
+ 0.3527700309,
0.4973632812
],
"data_range_": [
- 0.3506981052,
- 4.903582258,
+ 4.6953578034,
+ 0.3511115795,
0.596484375
]
},
"operating": {
"feature_range": [
- -3.0,
- 3.0
+ -4.0,
+ 4.0
],
"copy": true,
"n_features_in_": 3,
- "n_samples_seen_": 1572,
+ "n_samples_seen_": 1565,
"scale_": [
- 11.79559835553993,
- 0.28710421679370607,
- 10.014669925814587
+ 0.38140425942903444,
+ 15.512396901553764,
+ 13.352893234419449
],
"min_": [
- -3.000817363012889,
- -4.148878985987874,
- -2.0014669922476362
+ -5.527767728773947,
+ -4.001077368540846,
+ -2.668622656330182
],
"data_min_": [
- 6.92939e-05,
- 4.0016095856,
+ 4.0056388753,
+ 6.94521e-05,
-0.0997070313
],
"data_max_": [
- 0.5087336125,
- 24.8999442287,
+ 24.9807585868,
+ 0.5157860142,
0.4994140625
],
"data_range_": [
- 0.5086643186,
- 20.8983346431,
+ 20.9751197115,
+ 0.5157165621,
0.5991210938
]
}
@@ -120,21 +120,21 @@
],
"copy": true,
"n_features_in_": 1,
- "n_samples_seen_": 290,
+ "n_samples_seen_": 309,
"scale_": [
- 0.003217829437758253
+ 0.0009160949060733422
],
"min_": [
- -2.241711711671581
+ -1.0
],
"data_min_": [
- 385.8848754073917
+ 0.0
],
"data_max_": [
- 1007.4218582355832
+ 2183.1799159026004
],
"data_range_": [
- 621.5369828281914
+ 2183.1799159026004
]
},
"above_cut_out": {
@@ -144,21 +144,21 @@
],
"copy": true,
"n_features_in_": 1,
- "n_samples_seen_": 138,
+ "n_samples_seen_": 128,
"scale_": [
- 0.006414867921372538
+ 0.0015917827414332383
],
"min_": [
- -5.543090761343728
+ -1.0
],
"data_min_": [
- 708.2126735933917
+ 0.0
],
"data_max_": [
- 1019.9883834777
+ 1256.4528738383
],
"data_range_": [
- 311.7757098843083
+ 1256.4528738383
]
},
"operating": {
@@ -168,21 +168,21 @@
],
"copy": true,
"n_features_in_": 1,
- "n_samples_seen_": 1572,
+ "n_samples_seen_": 1566,
"scale_": [
- 0.0021491953622476507
+ 0.00032844455911796894
],
"min_": [
- -11.971056356553449
+ -1.0
],
"data_min_": [
- 5104.727354836558
+ 0.0
],
"data_max_": [
- 6035.308182960242
+ 6089.3077521849
],
"data_range_": [
- 930.5808281236841
+ 6089.3077521849
]
}
}
diff --git a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/model_set_above_cut_out.h5 b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/model_set_above_cut_out.h5
index c36267d7dfe121e60026408f82242fb87d1c3423..866f5b9c2f3f7a77b4e98480e2c43a96afb1f98a 100644
Binary files a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/model_set_above_cut_out.h5 and b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/model_set_above_cut_out.h5 differ
diff --git a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/model_set_below_cut_in.h5 b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/model_set_below_cut_in.h5
index b42ae553b7bdfc10433bbdad1aa43afaa600fcf1..e14dc38c27f1c3d53ca481fd29f39f1eab60358d 100644
Binary files a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/model_set_below_cut_in.h5 and b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/model_set_below_cut_in.h5 differ
diff --git a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/model_set_operating.h5 b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/model_set_operating.h5
index 8a0cef51f4f89fc97739607f1b48a3053af2e00a..0a490a386f8a50852059298f08923020457c3e4d 100644
Binary files a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/model_set_operating.h5 and b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_edge/model_set_operating.h5 differ
diff --git a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/extra_data.json b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/extra_data.json
index 3afac2691a91b6daf80b13e3347c594cef6953b3..397c98af7e485b7a51214bb753e5ccb54b4a803e 100644
--- a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/extra_data.json
+++ b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/extra_data.json
@@ -1,7 +1,7 @@
{
"input_channel_names": [
- "ti",
"ws",
+ "ti",
"shear"
],
"output_channel_name": "MomentMx Mbdy:blade1 nodenr: 1 coo: blade1 blade root moment blade1",
@@ -11,103 +11,103 @@
"input_scalers": {
"below_cut_in": {
"feature_range": [
- -3.0,
- 3.0
+ -4.0,
+ 4.0
],
"copy": true,
"n_features_in_": 3,
- "n_samples_seen_": 290,
+ "n_samples_seen_": 308,
"scale_": [
- 4.093931847815023,
- 1.5527559984108794,
- 10.034296914116865
+ 2.077667186797471,
+ 5.45739665679305,
+ 13.37906255215582
],
"min_": [
- -3.00460564098673,
- -3.206397077344649,
- -2.0024497794364287
+ -4.273422763436555,
+ -4.0044113992691726,
+ -2.669933039248572
],
"data_min_": [
- 0.0011249921,
- 0.1329230591,
+ 0.1316008479,
+ 0.000808334,
-0.0994140625
],
"data_max_": [
+ 3.9820731713,
1.4667087446,
- 3.997020191,
0.4985351562
],
"data_range_": [
- 1.4655837525,
- 3.8640971319,
+ 3.8504723234,
+ 1.4659004106,
0.5979492187
]
},
"above_cut_out": {
"feature_range": [
- -3.0,
- 3.0
+ -4.0,
+ 4.0
],
"copy": true,
"n_features_in_": 3,
- "n_samples_seen_": 138,
+ "n_samples_seen_": 127,
"scale_": [
- 17.108732300045514,
- 1.2235952583871186,
- 10.058939096267192
+ 1.7038105156133243,
+ 22.784779731253494,
+ 13.411918795022922
],
"min_": [
- -3.028362517654116,
- -33.59896481247119,
- -2.0029469543104126
+ -46.75667134943951,
+ -4.037787449843989,
+ -2.67059593908055
],
"data_min_": [
- 0.0016577802,
- 25.0074234946,
+ 25.0947338085,
+ 0.0016584514,
-0.0991210938
],
"data_max_": [
- 0.3523558854,
- 29.9110057526,
+ 29.7900916119,
+ 0.3527700309,
0.4973632812
],
"data_range_": [
- 0.3506981052,
- 4.903582258,
+ 4.6953578034,
+ 0.3511115795,
0.596484375
]
},
"operating": {
"feature_range": [
- -3.0,
- 3.0
+ -4.0,
+ 4.0
],
"copy": true,
"n_features_in_": 3,
- "n_samples_seen_": 1572,
+ "n_samples_seen_": 1565,
"scale_": [
- 11.79559835553993,
- 0.28710421679370607,
- 10.014669925814587
+ 0.38140425942903444,
+ 15.512396901553764,
+ 13.352893234419449
],
"min_": [
- -3.000817363012889,
- -4.148878985987874,
- -2.0014669922476362
+ -5.527767728773947,
+ -4.001077368540846,
+ -2.668622656330182
],
"data_min_": [
- 6.92939e-05,
- 4.0016095856,
+ 4.0056388753,
+ 6.94521e-05,
-0.0997070313
],
"data_max_": [
- 0.5087336125,
- 24.8999442287,
+ 24.9807585868,
+ 0.5157860142,
0.4994140625
],
"data_range_": [
- 0.5086643186,
- 20.8983346431,
+ 20.9751197115,
+ 0.5157165621,
0.5991210938
]
}
@@ -120,21 +120,21 @@
],
"copy": true,
"n_features_in_": 1,
- "n_samples_seen_": 290,
+ "n_samples_seen_": 309,
"scale_": [
- 0.0044128736952490375
+ 0.0004304624728914839
],
"min_": [
- -16.790881035417325
+ -1.0
],
"data_min_": [
- 3578.366870644408
+ 0.0
],
"data_max_": [
- 4031.586277796992
+ 4646.165754161301
],
"data_range_": [
- 453.2194071525837
+ 4646.165754161301
]
},
"above_cut_out": {
@@ -144,21 +144,21 @@
],
"copy": true,
"n_features_in_": 1,
- "n_samples_seen_": 138,
+ "n_samples_seen_": 128,
"scale_": [
- 0.004608273201192155
+ 0.0004876000920352097
],
"min_": [
- -15.907513088691113
+ -1.0
],
"data_min_": [
- 3234.945594118542
+ 0.0
],
"data_max_": [
- 3668.947640586317
+ 4101.721949337901
],
"data_range_": [
- 434.002046467775
+ 4101.721949337901
]
},
"operating": {
@@ -168,21 +168,21 @@
],
"copy": true,
"n_features_in_": 1,
- "n_samples_seen_": 1572,
+ "n_samples_seen_": 1566,
"scale_": [
- 0.00032756328073293837
+ 0.0002660350733914166
],
"min_": [
- -1.359284416662768
+ -1.0
],
"data_min_": [
- 1096.839718599875
+ 0.0
],
"data_max_": [
- 7202.530184041866
+ 7517.8057332967
],
"data_range_": [
- 6105.690465441991
+ 7517.8057332967
]
}
}
diff --git a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/model_set_above_cut_out.h5 b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/model_set_above_cut_out.h5
index cafc0f33ab0bac29561340bc0189c09fb20933bb..9c9b37a9f4ffbf8552b60af3070180be957fc79f 100644
Binary files a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/model_set_above_cut_out.h5 and b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/model_set_above_cut_out.h5 differ
diff --git a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/model_set_below_cut_in.h5 b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/model_set_below_cut_in.h5
index 067dd9a25cfd784a063275019d584893ed629098..4c9338596f8db5972260fd3160e95d4c61bf4d09 100644
Binary files a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/model_set_below_cut_in.h5 and b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/model_set_below_cut_in.h5 differ
diff --git a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/model_set_operating.h5 b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/model_set_operating.h5
index 6196ded97c593712c5c007f476bc60d6df0d9ea8..2970b3e08966e701c464b621a326b87d82cfe7b9 100644
Binary files a/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/model_set_operating.h5 and b/py_wake/examples/data/iea34_130rwt/one_turbine/del_blade_flap/model_set_operating.h5 differ
diff --git a/py_wake/examples/data/iea34_130rwt/one_turbine/shaft_torsion/extra_data.json b/py_wake/examples/data/iea34_130rwt/one_turbine/del_shaft_torsion/extra_data.json
similarity index 62%
rename from py_wake/examples/data/iea34_130rwt/one_turbine/shaft_torsion/extra_data.json
rename to py_wake/examples/data/iea34_130rwt/one_turbine/del_shaft_torsion/extra_data.json
index 1b64008a5fc6fb282f41ce5513d5fcdf5e60cc15..664bc862765fe4a30f25c949d8905196104e60e4 100644
--- a/py_wake/examples/data/iea34_130rwt/one_turbine/shaft_torsion/extra_data.json
+++ b/py_wake/examples/data/iea34_130rwt/one_turbine/del_shaft_torsion/extra_data.json
@@ -1,7 +1,7 @@
{
"input_channel_names": [
- "ti",
"ws",
+ "ti",
"shear"
],
"output_channel_name": "MomentMz Mbdy:shaft nodenr: 3 coo: shaft main bearing moment",
@@ -11,35 +11,35 @@
"input_scalers": {
"operating": {
"feature_range": [
- -3.0,
- 3.0
+ -4.0,
+ 4.0
],
"copy": true,
"n_features_in_": 3,
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similarity index 55%
rename from py_wake/examples/data/iea34_130rwt/one_turbine/shaft_torsion/model_set_operating.h5
rename to py_wake/examples/data/iea34_130rwt/one_turbine/del_shaft_torsion/model_set_operating.h5
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index 38e5d2c90095067fb8cdad67e5da79215bb35da6..9eca7e07304878d9a88d94658f01df4d220b5cfb 100644
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@@ -1,7 +1,7 @@
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index 247c729095eb82c35a75154cca8c0c4526050b78..041a46e87e25e5d233d2a08010a3c8418ccd8891 100644
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@@ -1,7 +1,7 @@
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diff --git a/py_wake/examples/data/iea34_130rwt/one_turbine/del_tower_top_torsion/extra_data.json b/py_wake/examples/data/iea34_130rwt/one_turbine/del_tower_top_torsion/extra_data.json
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},
"above_cut_out": {
"feature_range": [
- -3.0,
- 3.0
+ -4.0,
+ 4.0
],
"copy": true,
"n_features_in_": 5,
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+ "n_samples_seen_": 347,
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},
"operating": {
"feature_range": [
- -3.0,
- 3.0
+ -4.0,
+ 4.0
],
"copy": true,
"n_features_in_": 5,
- "n_samples_seen_": 4053,
+ "n_samples_seen_": 3837,
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+ -5.524981689358597,
+ -4.0008155118248405,
+ -2.6677115744870976,
+ 3.337518002854978e-05,
+ -4.025685906955127
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"data_min_": [
- 4.0040835652,
- 2.26553e-05,
- -0.099990563,
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+ -0.0998900071,
+ -44.9955844061,
+ 3.0002621835
],
"data_max_": [
- 24.9953188258,
- 0.5280123849,
- 0.4998349361,
- 44.992446093,
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+ 24.9927689514,
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"data_range_": [
- 20.9912352606,
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+ 20.9913423597,
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+ 0.5998101023,
+ 89.9904179506,
+ 934.4461735351999
]
}
},
@@ -152,21 +152,21 @@
],
"copy": true,
"n_features_in_": 1,
- "n_samples_seen_": 554,
+ "n_samples_seen_": 809,
"scale_": [
- 0.14176690898241076
+ 0.13221478579422918
],
"min_": [
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+ 1.0064180449185827
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+ -15.175443751361108
],
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- -0.1917482794374999
+ -0.0485425656444444
],
"data_range_": [
- 14.107664576704167
+ 15.126901185716664
]
},
"above_cut_out": {
@@ -176,21 +176,21 @@
],
"copy": true,
"n_features_in_": 1,
- "n_samples_seen_": 381,
+ "n_samples_seen_": 347,
"scale_": [
- 0.39849074525006134
+ 0.2816830645016472
],
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+ -4.133983310352102
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+ 11.125920246205554
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+ 18.226098609922214
],
"data_range_": [
- 5.018937136783334
+ 7.10017836371666
]
},
"operating": {
@@ -200,21 +200,21 @@
],
"copy": true,
"n_features_in_": 1,
- "n_samples_seen_": 4053,
+ "n_samples_seen_": 3837,
"scale_": [
- 0.00417517689902569
+ 0.0038557564725860386
],
"min_": [
- -1.2550647952593086
+ -1.1764234935677385
],
"data_min_": [
- 61.09077565523751
+ 45.7558704296
],
"data_max_": [
- 540.1123951863083
+ 564.4608286446112
],
"data_range_": [
- 479.02161953107077
+ 518.7049582150112
]
}
}
diff --git a/py_wake/examples/data/iea34_130rwt/two_turbines/thrust/model_set_above_cut_out.h5 b/py_wake/examples/data/iea34_130rwt/two_turbines/thrust/model_set_above_cut_out.h5
index f2bfaf408ef4afc4023ff6f59844d86ca1f49ae3..983029e3548756575501dd745ca542aa135a2de0 100644
Binary files a/py_wake/examples/data/iea34_130rwt/two_turbines/thrust/model_set_above_cut_out.h5 and b/py_wake/examples/data/iea34_130rwt/two_turbines/thrust/model_set_above_cut_out.h5 differ
diff --git a/py_wake/examples/data/iea34_130rwt/two_turbines/thrust/model_set_below_cut_in.h5 b/py_wake/examples/data/iea34_130rwt/two_turbines/thrust/model_set_below_cut_in.h5
index 914123edc3fa40f1cca70a459fd62b8c6ab2c202..0a782eea259cf086e8deab55613484ce10991f48 100644
Binary files a/py_wake/examples/data/iea34_130rwt/two_turbines/thrust/model_set_below_cut_in.h5 and b/py_wake/examples/data/iea34_130rwt/two_turbines/thrust/model_set_below_cut_in.h5 differ
diff --git a/py_wake/examples/data/iea34_130rwt/two_turbines/thrust/model_set_operating.h5 b/py_wake/examples/data/iea34_130rwt/two_turbines/thrust/model_set_operating.h5
index e4e062eaa0e1e3aae38bf995e01e59b7ab0d041b..5767dafa10ab48664f017c6d95ecd443b56e6c56 100644
Binary files a/py_wake/examples/data/iea34_130rwt/two_turbines/thrust/model_set_operating.h5 and b/py_wake/examples/data/iea34_130rwt/two_turbines/thrust/model_set_operating.h5 differ
diff --git a/py_wake/tests/test_load_surrogates/test_iea34_surrogates.py b/py_wake/tests/test_load_surrogates/test_iea34_surrogates.py
index 8fd5060f839b9337bb4e960f73ab9edbedc16499..9953a31c0b84957aab8da6d80a1a37484f1f3bc3 100644
--- a/py_wake/tests/test_load_surrogates/test_iea34_surrogates.py
+++ b/py_wake/tests/test_load_surrogates/test_iea34_surrogates.py
@@ -1,93 +1,108 @@
import numpy as np
+import pandas as pd
+
from py_wake.examples.data.iea34_130rwt._iea34_130rwt import IEA34_130_1WT_Surrogate, IEA34_130_2WT_Surrogate
from py_wake.tests import npt
from py_wake.deficit_models.noj import NOJ
from py_wake.site.xrsite import UniformSite
from py_wake.turbulence_models.stf import STF2017TurbulenceModel
+from pathlib import Path
def test_one_turbine_case0():
- ti, ws, shear = 0.0592370641, 9.6833182032, 0.2
+ ws, ti, shear = 9.2984459862, 0.0597870198, 0.2
+
+ if 0:
+ f = r'C:\mmpe\programming\python\Topfarm\iea-3_4-130-rwt\turbine_model\res/'
+ print(pd.concat([pd.read_csv(f + 'stats_one_turbine_mean.csv').iloc[0, [10, 14, 322]],
+ pd.read_csv(f + 'stats_one_turbine_std.csv').iloc[0, [10, 14, 322]]],
+ axis=1))
+ # Free wind speed Vy, gl. coo, of gl. pos 0.00... 9.309756e+00 0.401308
+ # Aero rotor thrust 5.408776e+02 11.489005
+ # generator_servo inpvec 2 2: pelec [w] 2.931442e+06 116491.192548
+
+ print(pd.read_csv(f + 'stats_one_turbine_del.csv').iloc[0, [28, 29, 1, 2, 9]])
+ # MomentMx Mbdy:blade1 nodenr: 1 coo: blade1 blade root moment blade1 1822.247387
+ # MomentMy Mbdy:blade1 nodenr: 1 coo: blade1 blade root moment blade1 5795.166623
+ # MomentMx Mbdy:tower nodenr: 1 coo: tower tower bottom moment 4385.405881
+ # MomentMy Mbdy:tower nodenr: 1 coo: tower tower bottom moment 2468.357017
+ # MomentMz Mbdy:tower nodenr: 11 coo: tower tower top/yaw bearing moment 1183.884786
- # for i in [10, 14, 322]:
- # print(sensors[i], mean_values[i], std_values[i])
- # "Free wind speed Vy gl. coo of gl. pos 0.00 0.00-110.00" 9.704401687125 0.7808578675666666
- # Aero rotor thrust 532.846421460325 26.557189388525
- # generator_servo inpvec 2 2: pelec [w] 3197471.4446449564 176585.66727525144
+ ws_ref = 9.309756e+00
+ ws_std_ref = 0.401308
+ power_ref = 2.931442e+06
+ thrust_ref = 5.408776e+02
+ ref_dels = [1822, 5795, 4385, 2468, 1183]
wt = IEA34_130_1WT_Surrogate()
- assert wt.loadFunction.output_keys[1] == 'MomentMy Mbdy:blade1 nodenr: 1 coo: blade1 blade root moment blade1'
+ assert wt.loadFunction.output_keys[1] == 'del_blade_edge'
assert wt.loadFunction.wohler_exponents == [10, 10, 4, 4, 7]
site = UniformSite(p_wd=[1], ti=ti, ws=ws)
sim_res = NOJ(site, wt, turbulenceModel=STF2017TurbulenceModel())([0], [0], wd=0, Alpha=shear)
- npt.assert_allclose(ws, 9.7, atol=.02)
- npt.assert_allclose(ti, 0.78 / 9.7, atol=.022)
- npt.assert_allclose(sim_res.Power, 3197471, atol=230)
- npt.assert_allclose(sim_res.CT, 532 * 1e3 / (1 / 2 * 1.225 * (65**2 * np.pi) * 9.7**2), atol=0.006)
-
- # for i in [28,29,1,2,9]:
- # print(del_sensors[i], del_values[i])
- # MomentMx Mbdy:blade1 nodenr: 1 coo: blade1 blade root moment blade1 2837.2258423768494
- # MomentMy Mbdy:blade1 nodenr: 1 coo: blade1 blade root moment blade1 5870.8557931814585
- # MomentMx Mbdy:tower nodenr: 1 coo: tower tower bottom moment 9154.009617791817
- # MomentMy Mbdy:tower nodenr: 1 coo: tower tower bottom moment 5184.805475839391
- # MomentMz Mbdy:tower nodenr: 11 coo: tower tower top/yaw bearing moment 2204.4440980948084
+ npt.assert_allclose(ws, ws_ref, rtol=.0013)
+ npt.assert_allclose(ti, ws_std_ref / ws_ref, atol=.02)
+ npt.assert_allclose(sim_res.Power, power_ref, rtol=0.003)
+ npt.assert_allclose(sim_res.CT, thrust_ref * 1e3 / (1 / 2 * 1.225 * (65**2 * np.pi) * ws_ref**2), rtol=0.011)
+
loads = sim_res.loads(method='OneWT')
- npt.assert_allclose(loads.DEL.squeeze(), [2837, 5870, 9154, 5184, 2204], rtol=.11)
+ npt.assert_allclose(loads.DEL.squeeze(), ref_dels, rtol=.11)
f = 20 * 365 * 24 * 3600 / 1e7
m = np.array([10, 10, 4, 4, 7])
npt.assert_array_almost_equal(loads.LDEL.squeeze(), (loads.DEL.squeeze()**m * f)**(1 / m))
loads = sim_res.loads(method='OneWT_WDAvg')
- npt.assert_allclose(loads.DEL.squeeze(), [2837, 5870, 9154, 5184, 2204], rtol=.11)
+ npt.assert_allclose(loads.DEL.squeeze(), ref_dels, rtol=.11)
npt.assert_array_almost_equal(loads.LDEL.squeeze(), (loads.DEL.squeeze()**m * f)**(1 / m))
def test_two_turbine_case0():
- ws, ti, shear, wdir, dist = 11.924050697, 0.2580934242, 0.2536493558, 20.5038383383, 3.8603095881
-
-# sensors = 'case,shaft_rot angle,shaft_rot angle speed,pitch1 angle,'.split(
-# ',')
-# mean_values = np.array("0,180.03464337189587,11.752259992475002,".split(","), dtype=np.float)
-# std_values = np.array("0,104.04391803126249,0.1227628373541667,".split(","), dtype=np.float)
-# for i in [10, 14, 322]:
-# print(sensors[i], mean_values[i], std_values[i])
- # "Free wind speed Vy gl. coo of gl. pos 0.00 0.00-110.00" 11.107441047808335 0.8527054147583332
- # Aero rotor thrust 381.01636820417497 37.541737944825
- # generator_servo inpvec 2 2: pelec [w] 3398038.328107514 13065.24538016912
- # ref from simulation statistic
- ws_ref = 11.1
- ws_std_ref = 0.85
- power_ref = 3398038
- thrust_ref = 381
+ if 0:
+ i = 0
+ f = r'C:\mmpe\programming\python\Topfarm\iea-3_4-130-rwt\turbine_model\res/'
+ print(list(pd.DataFrame(eval(Path(f + 'input_two_turbines_dist.json').read_text())).iloc[i]))
+ # [10.9785338191, 0.2623204277, 0.4092031776, -38.4114616871, 5.123719529]
+
+ print(pd.concat([pd.read_csv(f + 'stats_two_turbines_mean.csv').iloc[i, [12, 14, 322]],
+ pd.read_csv(f + 'stats_two_turbines_std.csv').iloc[i, [12, 14, 322]]],
+ axis=1))
+ # Free wind speed Abs_vhor, gl. coo, of gl. pos ... 1.103937e+01 0.914252
+ # Aero rotor thrust 4.211741e+02 41.015962
+ # generator_servo inpvec 2 2: pelec [w] 3.399746e+06 3430.717100
+
+ print(pd.read_csv(f + 'stats_two_turbines_del.csv').iloc[i, [28, 29, 1, 2, 9]])
+ # MomentMx Mbdy:blade1 nodenr: 1 coo: blade1 blade root moment blade1 4546.998501
+ # MomentMy Mbdy:blade1 nodenr: 1 coo: blade1 blade root moment blade1 5931.157693
+ # MomentMx Mbdy:tower nodenr: 1 coo: tower tower bottom moment 11902.153031
+ # MomentMy Mbdy:tower nodenr: 1 coo: tower tower bottom moment 7599.336676
+ # MomentMz Mbdy:tower nodenr: 11 coo: tower tower top/yaw bearing moment 2407.279074
+
+ ws, ti, shear, wdir, dist = [10.9785338191, 0.2623204277, 0.4092031776, -38.4114616871 % 360, 5.123719529]
+
+ # ref from simulation statistic (not updated yet)
+ ws_ref = 1.103937e+01
+ ws_std_ref = 0.914252
+ thrust_ref = 4.211741e+02
+ power_ref = 3.399746e+06
+ ref_dels = [4546, 5931, 11902, 7599, 2407]
wt = IEA34_130_2WT_Surrogate()
site = UniformSite(p_wd=[1], ti=ti, ws=ws)
sim_res = NOJ(site, wt, turbulenceModel=STF2017TurbulenceModel())([0, 0], [0, dist * 130], wd=wdir, Alpha=shear)
- npt.assert_allclose(ws, ws_ref, atol=.9)
- npt.assert_allclose(ti, ws_std_ref / ws_ref, atol=.19)
- npt.assert_allclose(sim_res.Power.sel(wt=0), power_ref, atol=1060)
+ npt.assert_allclose(ws, ws_ref, rtol=.006)
+ # npt.assert_allclose(ti, ws_std_ref / ws_ref, atol=.19)
+ npt.assert_allclose(sim_res.Power.sel(wt=0), power_ref, rtol=0.002)
npt.assert_allclose(sim_res.CT.sel(wt=0), thrust_ref * 1e3 / (1 / 2 * 1.225 * (65**2 * np.pi) * ws_ref**2),
- atol=0.06)
-
- # del_sensors = 'case,MomentMx Mbdy:tower nodenr: 1 coo: tower tower bottom moment,'.split(",")
- # del_values = np.array("0, 11235.755826844477, 7551.985936926388, ".split(","), dtype=float)
- # for i in [28, 29, 1, 2, 9]:
- # print(del_sensors[i], del_values[i])
- # MomentMx Mbdy:blade1 nodenr: 1 coo: blade1 blade root moment blade1 3863.6241845634827
- # MomentMy Mbdy:blade1 nodenr: 1 coo: blade1 blade root moment blade1 5774.294439888724
- # MomentMx Mbdy:tower nodenr: 1 coo: tower tower bottom moment 11235.755826844477
- # MomentMy Mbdy:tower nodenr: 1 coo: tower tower bottom moment 7551.985936926388
- # MomentMz Mbdy:tower nodenr: 11 coo: tower tower top/yaw bearing moment 2384.173023068421
+ rtol=0.03)
+
loads = sim_res.loads(method='TwoWT')
- npt.assert_allclose(loads.DEL.sel(wt=0).squeeze(), [3863], rtol=.12)
+ npt.assert_allclose(loads.DEL.sel(wt=0).squeeze(), ref_dels, rtol=.05)
f = 20 * 365 * 24 * 3600 / 1e7
- m = 10
+ m = loads.m.values
npt.assert_array_almost_equal(loads.LDEL.sel(wt=0).squeeze(), (loads.DEL.sel(wt=0).squeeze()**m * f)**(1 / m))
loads = sim_res.loads(method='TwoWT', softmax_base=100)
- npt.assert_allclose(loads.DEL.sel(wt=0).squeeze(), [3863], rtol=.11)
+ npt.assert_allclose(loads.DEL.sel(wt=0).squeeze(), ref_dels, rtol=.05)
npt.assert_array_almost_equal(loads.LDEL.sel(wt=0).squeeze(), (loads.DEL.sel(wt=0).squeeze()**m * f)**(1 / m))
diff --git a/py_wake/tests/test_load_surrogates/test_tensorflow_surrogate_utils.py b/py_wake/tests/test_load_surrogates/test_tensorflow_surrogate_utils.py
index de7124dbad7c0a4eea4bdd0d84a818f8230000b8..d30e0b7cc58606c6e108626ee043417547ff83c1 100644
--- a/py_wake/tests/test_load_surrogates/test_tensorflow_surrogate_utils.py
+++ b/py_wake/tests/test_load_surrogates/test_tensorflow_surrogate_utils.py
@@ -9,10 +9,10 @@ from py_wake.tests import npt
def test_TensorflowSurrogate():
surrogate = TensorflowSurrogate(example_data_path + "iea34_130rwt/one_turbine/electrical_power", 'operating')
- assert surrogate.input_channel_names == ['ti', 'ws', 'shear']
+ assert surrogate.input_channel_names == ['ws', 'ti', 'shear']
assert surrogate.output_channel_name == "generator_servo inpvec 2 2: pelec [w]"
- assert surrogate.input_space == {'ti': (6.92939e-05, 0.5087336125),
- 'ws': (4.0016095856, 24.8999442287),
+ assert surrogate.input_space == {'ti': (6.94521e-05, 0.5157860142),
+ 'ws': (4.0056388753, 24.9807585868),
'shear': (-0.0997070313, 0.4994140625)}
assert surrogate.wind_speed_cut_in == 4.0
assert surrogate.wind_speed_cut_out == 25.0
@@ -22,10 +22,10 @@ def test_bounds_warning():
surrogate = TensorflowSurrogate(example_data_path + "iea34_130rwt/one_turbine/electrical_power", 'operating')
import warnings
warnings.filterwarnings('error')
- with pytest.raises(UserWarning, match='Input, ws, with value, 3.0 outside range 4.0016095856-24.8999442287'):
- surrogate.predict_output(np.array([.1, 3., .1])[na])
+ with pytest.raises(UserWarning, match='Input, ws, with value, 3.0 outside range 4.0056388753-24.9807585868'):
+ surrogate.predict_output(np.array([3., .1, .1])[na])
- with pytest.raises(UserWarning, match='Input, ws, with value, 25.0 outside range 4.0016095856-24.8999442287'):
- surrogate.predict_output(np.array([.1, 25., .1])[na])
+ with pytest.raises(UserWarning, match='Input, ws, with value, 25.0 outside range 4.0056388753-24.9807585868'):
+ surrogate.predict_output(np.array([25., .1, .1])[na])
- assert surrogate.predict_output(np.array([.1, 25., .1])[na], bounds='ignore') == 3399945.
+ assert surrogate.predict_output(np.array([25., .1, .1])[na], bounds='ignore') == 3399991.2
diff --git a/py_wake/wind_farm_models/engineering_models.py b/py_wake/wind_farm_models/engineering_models.py
index 7326be5517e74fcd3d09047ac1b2eb4846f6fe45..66bad4f2d99e4c98ff7b8bcb04c85a3f87cd1a70 100644
--- a/py_wake/wind_farm_models/engineering_models.py
+++ b/py_wake/wind_farm_models/engineering_models.py
@@ -220,7 +220,7 @@ class EngineeringWindFarmModel(WindFarmModel):
d_ijl_keys = ({k for l in self.windTurbines.function_inputs for k in l} &
{'dw_ijl', 'hcw_ijl', 'dh_ijl', 'cw_ijl'})
if d_ijl_keys:
- d_ijl_dict = {k: lambda: v for k, v in zip(['dw_ijl', 'hcw_ijl', 'dh_ijl'], self.site.distance(wd[na]))}
+ d_ijl_dict = {k: lambda v=v: v for k, v in zip(['dw_ijl', 'hcw_ijl', 'dh_ijl'], self.site.distance(wd[na]))}
d_ijl_dict['cw_ijl'] = lambda d_ijl_dict=d_ijl_dict: np.sqrt(
d_ijl_dict['dw_ijl']**2 + d_ijl_dict['hcw_ijl']**2)
wt_kwargs.update({k: d_ijl_dict[k]() for k in d_ijl_keys})
diff --git a/py_wake/wind_farm_models/wind_farm_model.py b/py_wake/wind_farm_models/wind_farm_model.py
index e7aa78f656eeb414b50353ec62ac91b62db58889..e3b0db92c34e2d72ab248cbf55620461fc9be459 100644
--- a/py_wake/wind_farm_models/wind_farm_model.py
+++ b/py_wake/wind_farm_models/wind_farm_model.py
@@ -307,8 +307,10 @@ class SimulationResult(xr.Dataset):
if softmax_base is None:
loads_silk = loads_siilk.max(1)
else:
- f = loads_siilk.mean((1, 2, 3, 4)) / 10 # factor used to reduce numerical errors in power
- loads_silk = np.log((softmax_base**(loads_siilk / f)).sum(1)) / np.log(softmax_base) * f
+ # factor used to reduce numerical errors in power
+ f = loads_siilk.mean((1, 2, 3, 4)) / 10
+ loads_silk = (np.log((softmax_base**(loads_siilk / f[:, na, na, na, na])).sum(1)) /
+ np.log(softmax_base) * f[:, na, na, na])
ds = xr.DataArray(
loads_silk,