def test_fuga_table_edges(): wts = HornsrevV80() path = tfp + 'fuga/2MW/Z0=0.03000000Zi=00401Zeta0=0.00E+00/' site = hornsrev1.Hornsrev1Site() fuga = FugaBlockage(path, site, wts) D = 80 flow_map_dw = fuga([0], [0], wd=270, ws=10).flow_map( HorizontalGrid(np.arange(-200 * D, 450 * D), y=[0])) flow_map_cw = fuga([0], [0], wd=270, ws=10).flow_map( HorizontalGrid([0], np.arange(-20 * D, 20 * D))) flow_map = fuga([0], [0], wd=270, ws=10).flow_map( HorizontalGrid(np.arange(-150, 400) * D, np.arange(-20, 21) * D)) if 0: plt.plot(flow_map_dw.x / D, flow_map_dw.WS_eff.squeeze()) plt.grid() plt.ylim([9.9, 10.1]) plt.figure() plt.plot(flow_map_cw.y / D, flow_map_cw.WS_eff.squeeze()) plt.grid() plt.ylim([9.9, 10.1]) plt.figure() flow_map.WS_eff.plot() plt.show() npt.assert_array_equal(flow_map.WS_eff.squeeze()[[0, -1], :], 10) npt.assert_array_equal(flow_map.WS_eff.squeeze()[:, [0, -1]], 10)
def test_fuga_wriggles(): wts = HornsrevV80() path = tfp + 'fuga/2MW/Z0=0.03000000Zi=00401Zeta0=0.00E+00/' site = hornsrev1.Hornsrev1Site() fuga = PropagateDownwind(site, wts, FugaDeficit(path, remove_wriggles=True)) D = 80 flow_map_cw = fuga([0], [0], wd=270, ws=10).flow_map( HorizontalGrid([0], np.arange(-20 * D, 20 * D))) y = np.linspace(-5 * D, 5 * D, 100) dw_lst = range(10) flow_map_cw_lst = np.array([ fuga([0], [0], wd=270, ws=10).flow_map(HorizontalGrid([dw * D], y)).WS_eff.squeeze() for dw in dw_lst ]) if 0: for flow_map_cw, dw in zip(flow_map_cw_lst, dw_lst): plt.plot(y, flow_map_cw, label="%dD" % dw) plt.xlabel('y [m]') plt.ylabel('ws [m/s') plt.ylim([9.9, 10.1]) plt.grid() plt.legend(loc=1) plt.show() assert np.all(flow_map_cw_lst > 0)
def test_fuga_new_format(): # move turbine 1 600 300 wt_x = [-250, 600, -500, 0, 500, -250, 250] wt_y = [433, 300, 0, 0, 0, -433, -433] wts = HornsrevV80() path = tfp + 'fuga/2MW/Z0=0.00408599Zi=00400Zeta0=0.00E+00/' site = UniformSite([1, 0, 0, 0], ti=0.075) wake_model = Fuga(path, site, wts) res = wake_model(x=wt_x, y=wt_y, wd=[30], ws=[10]) npt.assert_array_almost_equal(res.WS_eff_ilk.flatten(), [ 10.00647891, 10., 8.21713928, 10.03038884, 9.36889964, 8.23084088, 7.80662141 ], 8) npt.assert_array_almost_equal(res.ct_ilk.flatten(), [ 0.79265014, 0.793, 0.80621714, 0.791359, 0.80183541, 0.80623084, 0.80580662 ], 8) x_j = np.linspace(-1500, 1500, 500) y_j = np.linspace(-1500, 1500, 300) wake_model = Fuga(path, site, wts) sim_res = wake_model(wt_x, wt_y, wd=[30], ws=[10]) flow_map70 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=70)) flow_map73 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=73)) X, Y = flow_map70.XY Z70 = flow_map70.WS_eff_xylk[:, :, 0, 0] Z73 = flow_map73.WS_eff_xylk[:, :, 0, 0] if 0: flow_map70.plot_wake_map(levels=np.arange(6, 10.5, .1)) plt.plot(X[0], Y[140]) plt.figure() plt.plot(X[0], Z70[140, :], label="Z=70m") plt.plot(X[0], Z73[140, :], label="Z=73m") plt.plot(X[0, 100:400:10], Z70[140, 100:400:10], '.') print(list(np.round(Z70.values[140, 100:400:10], 4))) print(list(np.round(Z73.values[140, 100:400:10], 4))) plt.legend() plt.show() npt.assert_array_almost_equal(Z70[140, 100:400:10], [ 10.0384, 10.042, 10.044, 10.0253, 9.7194, 7.7561, 6.7421, 9.2308, 9.9894, 10.0413, 10.0499, 10.0579, 10.0437, 9.1626, 7.2334, 9.1208, 10.0396, 10.0322, 10.0276, 9.9504, 9.2861, 7.8375, 6.6608, 8.3343, 9.9756, 10.0229, 10.0136, 10.0142, 10.0118, 10.0094 ], 4) npt.assert_array_almost_equal(Z73[140, 100:400:10], [ 10.0384, 10.042, 10.044, 10.0253, 9.7194, 7.7561, 6.7421, 9.2308, 9.9894, 10.0413, 10.0499, 10.0579, 10.0437, 9.1626, 7.2334, 9.1208, 10.0396, 10.0322, 10.0276, 9.9504, 9.2861, 7.8375, 6.6608, 8.3343, 9.9756, 10.0229, 10.0136, 10.0142, 10.0118, 10.0094 ], 4)
def test_fuga_new_format(): # move turbine 1 600 300 wt_x = [-250, 600, -500, 0, 500, -250, 250] wt_y = [433, 300, 0, 0, 0, -433, -433] wts = HornsrevV80() path = tfp + 'fuga/2MW/Z0=0.00014617Zi=00399Zeta0=0.00E+0/' site = UniformSite([1, 0, 0, 0], ti=0.075) wake_model = Fuga(path, site, wts) res = wake_model(x=wt_x, y=wt_y, wd=[30], ws=[10]) npt.assert_array_almost_equal(res.WS_eff_ilk.flatten(), [ 10.00725165, 10., 7.92176401, 10.02054952, 9.40501317, 7.92609363, 7.52384558 ], 8) npt.assert_array_almost_equal(res.ct_ilk.flatten(), [ 0.79260841, 0.793, 0.80592176, 0.79189033, 0.80132982, 0.80592609, 0.80552385 ], 8) x_j = np.linspace(-1500, 1500, 500) y_j = np.linspace(-1500, 1500, 300) wake_model = Fuga(path, site, wts) sim_res = wake_model(wt_x, wt_y, wd=[30], ws=[10]) flow_map70 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=70)) flow_map73 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=73)) X, Y = flow_map70.XY Z70 = flow_map70.WS_eff_xylk[:, :, 0, 0] Z73 = flow_map73.WS_eff_xylk[:, :, 0, 0] if 0: flow_map70.plot_wake_map(levels=np.arange(6, 10.5, .1)) plt.plot(X[0], Y[140]) plt.figure() plt.plot(X[0], Z70[140, :], label="Z=70m") plt.plot(X[0], Z73[140, :], label="Z=73m") plt.plot(X[0, 100:400:10], Z70[140, 100:400:10], '.') print(list(np.round(Z70.values[140, 100:400:10], 4))) print(list(np.round(Z73.values[140, 100:400:10], 4))) plt.legend() plt.show() npt.assert_array_almost_equal(Z70[140, 100:400:10], [ 10.0458, 10.0309, 10.065, 10.0374, 9.7865, 7.7119, 6.4956, 9.2753, 10.0047, 10.0689, 10.0444, 10.0752, 10.0699, 9.1852, 6.9783, 9.152, 10.0707, 10.0477, 10.0365, 9.9884, 9.2867, 7.5714, 6.4451, 8.3276, 9.9976, 10.0251, 10.0264, 10.023, 10.0154, 9.9996 ], 4) npt.assert_array_almost_equal(Z73[140, 100:400:10], [ 10.0458, 10.0309, 10.065, 10.0374, 9.7865, 7.7119, 6.4956, 9.2753, 10.0047, 10.0689, 10.0444, 10.0752, 10.0699, 9.1852, 6.9783, 9.152, 10.0707, 10.0477, 10.0365, 9.9884, 9.2867, 7.5714, 6.4451, 8.3276, 9.9976, 10.0251, 10.0264, 10.023, 10.0154, 9.9996 ], 4)
def test_wec(): # move turbine 1 600 300 wt_x = [-250, 600, -500, 0, 500, -250, 250] wt_y = [433, 300, 0, 0, 0, -433, -433] wts = HornsrevV80() site = UniformSite([1, 0, 0, 0], ti=0.075) wake_model = Fuga(LUT_path_2MW_z0_0_03, site, wts) x_j = np.linspace(-1500, 1500, 500) y_j = np.linspace(-1500, 1500, 300) flow_map_wec1 = wake_model(wt_x, wt_y, 70, wd=[30], ws=[10]).flow_map(HorizontalGrid(x_j, y_j)) Z_wec1 = flow_map_wec1.WS_eff_xylk[:, :, 0, 0] wake_model.wec = 2 flow_map_wec2 = wake_model(wt_x, wt_y, 70, wd=[30], ws=[10]).flow_map(HorizontalGrid(x_j, y_j)) X, Y = flow_map_wec1.XY Z_wec2 = flow_map_wec2.WS_eff_xylk[:, :, 0, 0] if 0: print(list(np.round(Z_wec1[140, 100:400:10].values, 4))) print(list(np.round(Z_wec2[140, 100:400:10].values, 4))) flow_map_wec1.plot_wake_map(levels=np.arange(6, 10.5, .1), plot_colorbar=False) plt.plot(X[0], Y[140]) wts.plot(wt_x, wt_y) plt.figure() c = flow_map_wec2.plot_wake_map(levels=np.arange(6, 10.5, .1), plot_colorbar=False) plt.colorbar(c) plt.plot(X[0], Y[140]) wts.plot(wt_x, wt_y) plt.figure() plt.plot(X[0], Z_wec1[140, :], label="Z=70m") plt.plot(X[0], Z_wec2[140, :], label="Z=70m") plt.plot(X[0, 100:400:10], Z_wec1[140, 100:400:10], '.') plt.plot(X[0, 100:400:10], Z_wec2[140, 100:400:10], '.') plt.legend() plt.show() npt.assert_array_almost_equal(Z_wec1[140, 100:400:10], [ 10.0467, 10.0473, 10.0699, 10.0093, 9.6786, 7.8589, 6.8539, 9.2199, 9.9837, 10.036, 10.0796, 10.0469, 10.0439, 9.1866, 7.2552, 9.1518, 10.0449, 10.0261, 10.0353, 9.9256, 9.319, 8.0062, 6.789, 8.3578, 9.9393, 10.0332, 10.0183, 10.0186, 10.0191, 10.0139 ], 4) npt.assert_array_almost_equal(Z_wec2[140, 100:400:10], [ 10.0297, 9.9626, 9.7579, 9.2434, 8.2318, 7.008, 6.7039, 7.7303, 9.0101, 9.6877, 9.9068, 9.7497, 9.1127, 7.9505, 7.26, 7.9551, 9.2104, 9.7458, 9.6637, 9.1425, 8.2403, 7.1034, 6.5109, 7.2764, 8.7653, 9.7139, 9.9718, 10.01, 10.0252, 10.0357 ], 4)
def test_wec(): # move turbine 1 600 300 wt_x = [-250, 600, -500, 0, 500, -250, 250] wt_y = [433, 300, 0, 0, 0, -433, -433] wts = HornsrevV80() site = UniformSite([1, 0, 0, 0], ti=0.075) wfm = BastankhahGaussian(site, wts) x_j = np.linspace(-1500, 1500, 500) y_j = np.linspace(-1500, 1500, 300) flow_map_wec1 = wfm(wt_x, wt_y, 70, wd=[30], ws=[10]).flow_map(HorizontalGrid(x_j, y_j)) Z_wec1 = flow_map_wec1.WS_eff_xylk[:, :, 0, 0] wfm.wec = 2 flow_map_wec2 = wfm(wt_x, wt_y, 70, wd=[30], ws=[10]).flow_map(HorizontalGrid(x_j, y_j)) X, Y = flow_map_wec1.XY Z_wec2 = flow_map_wec2.WS_eff_xylk[:, :, 0, 0] if 0: print(list(np.round(Z_wec1[140, 100:400:10].values, 2))) print(list(np.round(Z_wec2[140, 100:400:10].values, 2))) flow_map_wec1.plot_wake_map(levels=np.arange(6, 10.5, .1), plot_colorbar=False) plt.plot(X[0], Y[140]) wts.plot(wt_x, wt_y) plt.figure() c = flow_map_wec2.plot_wake_map(levels=np.arange(6, 10.5, .1), plot_colorbar=False) plt.colorbar(c) plt.plot(X[0], Y[140]) wts.plot(wt_x, wt_y) plt.figure() plt.plot(X[0], Z_wec1[140, :], label="Z=70m") plt.plot(X[0], Z_wec2[140, :], label="Z=70m") plt.plot(X[0, 100:400:10], Z_wec1[140, 100:400:10], '.') plt.plot(X[0, 100:400:10], Z_wec2[140, 100:400:10], '.') plt.legend() plt.show() npt.assert_array_almost_equal(Z_wec1[140, 100:400:10], [ 10.0, 10.0, 10.0, 9.99, 9.8, 6.52, 1.47, 9.44, 9.98, 10.0, 10.0, 10.0, 10.0, 9.05, 0.03, 9.11, 10.0, 10.0, 10.0, 9.97, 9.25, 7.03, 2.35, 6.51, 9.99, 10.0, 10.0, 10.0, 10.0, 10.0 ], 2) npt.assert_array_almost_equal(Z_wec2[140, 100:400:10], [ 9.99, 9.96, 9.84, 9.47, 7.82, 2.24, 0.21, 6.21, 9.22, 9.82, 9.98, 9.92, 9.05, 4.45, 0.01, 4.53, 9.35, 9.95, 9.75, 9.13, 7.92, 5.14, 0.32, 2.2, 8.38, 9.94, 10.0, 10.0, 10.0, 10.0 ], 2)
def test_fuga(): # move turbine 1 600 300 wt_x = [-250, 600, -500, 0, 500, -250, 250] wt_y = [433, 300, 0, 0, 0, -433, -433] wts = HornsrevV80() path = tfp + 'fuga/2MW/Z0=0.03000000Zi=00401Zeta0=0.00E+0/' site = UniformSite([1, 0, 0, 0], ti=0.075) wake_model = Fuga(path, site, wts) res, _ = timeit(wake_model.__call__, verbose=0, line_profile=0, profile_funcs=[FugaDeficit.interpolate, LUTInterpolator.__call__, GridInterpolator.__call__])(x=wt_x, y=wt_y, wd=[30], ws=[10]) npt.assert_array_almost_equal(res.WS_eff_ilk.flatten(), [10.002683492812844, 10.0, 8.413483643142389, 10.036952526815286, 9.371203842245153, 8.437429367715435, 8.012759083790058], 8) npt.assert_array_almost_equal(res.ct_ilk.flatten(), [0.79285509, 0.793, 0.80641348, 0.79100456, 0.80180315, 0.80643743, 0.80601276], 8) x_j = np.linspace(-1500, 1500, 500) y_j = np.linspace(-1500, 1500, 300) wake_model = Fuga(path, site, wts) sim_res = wake_model(wt_x, wt_y, wd=[30], ws=[10]) flow_map70 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=70)) flow_map73 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=73)) X, Y = flow_map70.XY Z70 = flow_map70.WS_eff_xylk[:, :, 0, 0] Z73 = flow_map73.WS_eff_xylk[:, :, 0, 0] if 0: flow_map70.plot_wake_map(levels=np.arange(6, 10.5, .1)) plt.plot(X[0], Y[140]) plt.figure() plt.plot(X[0], Z70[140, :], label="Z=70m") plt.plot(X[0], Z73[140, :], label="Z=73m") plt.plot(X[0, 100:400:10], Z70[140, 100:400:10], '.') print(list(np.round(Z70.data[140, 100:400:10], 4))) print(list(np.round(Z73.data[140, 100:400:10], 4))) plt.legend() plt.show() npt.assert_array_almost_equal( Z70[140, 100:400:10], [10.0467, 10.0473, 10.0699, 10.0093, 9.6786, 7.8589, 6.8539, 9.2199, 9.9837, 10.036, 10.0796, 10.0469, 10.0439, 9.1866, 7.2552, 9.1518, 10.0449, 10.0261, 10.0353, 9.9256, 9.319, 8.0062, 6.789, 8.3578, 9.9393, 10.0332, 10.0183, 10.0186, 10.0191, 10.0139], 4) npt.assert_array_almost_equal( Z73[140, 100:400:10], [10.0463, 10.0468, 10.0688, 10.0075, 9.6778, 7.9006, 6.9218, 9.228, 9.9808, 10.0354, 10.0786, 10.0464, 10.0414, 9.1973, 7.3099, 9.1629, 10.0432, 10.0257, 10.0344, 9.9236, 9.3274, 8.0502, 6.8512, 8.3813, 9.9379, 10.0325, 10.018, 10.0183, 10.019, 10.0138], 4)
def test_deficitModel_wake_map_convection(deficitModel, ref): site = IEA37Site(16) x, y = site.initial_position.T windTurbines = IEA37_WindTurbines() wf_model = PropagateDownwind(site, windTurbines, wake_deficitModel=deficitModel, superpositionModel=WeightedSum(), turbulenceModel=GCLTurbulence()) x_j = np.linspace(-1500, 1500, 200) y_j = np.linspace(-1500, 1500, 100) flow_map = wf_model(x, y, wd=0, ws=9).flow_map(HorizontalGrid(x_j, y_j)) X, Y = flow_map.X, flow_map.Y Z = flow_map.WS_eff_xylk[:, :, 0, 0] mean_ref = [3.2, 4.9, 8., 8.2, 7.9, 7.4, 7., 7., 7.4, 7.9, 8.1, 8.1, 8., 7.8, 7.9, 8.1, 8.4] if 0: flow_map.plot_wake_map() plt.plot(X[49, 100:133:2], Y[49, 100:133:2], '.-') windTurbines.plot(x, y) plt.figure() plt.plot(Z[49, 100:133:2], label='Actual') plt.plot(ref, label='Reference') plt.plot(mean_ref, label='Mean ref') plt.legend() plt.show() # check that ref is reasonable npt.assert_allclose(ref[2:], mean_ref[2:], atol=2.6) npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
def flow_map(self, grid=None, wd=None, ws=None): """Return a FlowMap object with WS_eff and TI_eff of all grid points Parameters ---------- grid : Grid or tuple(X, Y, x, y, h) Grid, e.g. HorizontalGrid or\n tuple(X, Y, x, y, h) where X, Y is the meshgrid for visualizing data\n and x, y, h are the flattened grid points See Also -------- pywake.wind_farm_models.flow_map.FlowMap """ if grid is None: grid = HorizontalGrid() if isinstance(grid, HorizontalGrid): grid = grid(self.x_i, self.y_i, self.h_i) if wd is None: wd = self.wd else: assert np.all(np.isin( wd, self.wd)), "All wd=%s not in simulation result" % wd if ws is None: ws = self.ws else: assert np.all(np.isin( ws, self.ws)), "All ws=%s not in simulation result (ws=%s)" % ( ws, self.ws) wd, ws = np.atleast_1d(wd), np.atleast_1d(ws) l_indices = np.argwhere(wd[:, None] == self.wd)[:, 1] # wd对应函数参数3, self.wd是对应classSimulationResult初始化后的结果 k_indices = np.argwhere(ws[:, None] == self.ws)[:, 1] X, Y, x_j, y_j, h_j = grid # 为了搞清楚中间变量进行了修改 lWD = self.localWind.WD_ilk[:, l_indices][:, :, :] # 源代码lWD = self.localWind.WD_ilk[:, l_indices][:, :, k_indices] lWS = self.localWind.WS_ilk[:, l_indices][:, :, k_indices] lTI = self.localWind.TI_ilk[:, l_indices][:, :, k_indices] WSe = self.WS_eff_ilk[:, l_indices][:, :, k_indices] TIe = self.TI_eff_ilk[:, l_indices][:, :, k_indices] cti = self.ct_ilk[:, l_indices][:, :, k_indices] lw_j, WS_eff_jlk, TI_eff_jlk = self.windFarmModel._flow_map( x_j, y_j, h_j, self.x_i, self.y_i, self.h_i, self.type_i, self.yaw_ilk, lWD, lWS, lTI, WSe, TIe, cti, wd, ws) if self.yaw_ilk is not None: yaw_ilk = self.yaw_ilk[:, l_indices][:, :, k_indices] else: yaw_ilk = None return FlowMap(self, X, Y, lw_j, WS_eff_jlk, TI_eff_jlk, wd, ws, yaw_ilk=yaw_ilk)
def test_interpolation_speed(): import xarray as xr da = xr.DataArray(np.sin(0.3 * np.arange(20).reshape(5, 4)), [('x', np.arange(5)), ('y', [0.1, 0.2, 0.3, 0.4])]) x = xr.DataArray([0.5, 1.5, 2.5], dims='z') y = xr.DataArray([0.15, 0.25, 0.35], dims='z') da.interp(x=x, y=y) site = ParqueFicticioSite() x, y = site.initial_position.T X, Y, x_j, y_j, h_j = HorizontalGrid()(x, y, 70) wd = [270] # site.default_wd ws = site.default_ws res1, t_lst = timeit(site.interp_funcs['A'])( (x_j, y_j, h_j, x_j * 0 + 270)) print(res1.shape) res2, t_lst = timeit(lambda x, y, z, sec: site._ds.A.interp( x=xr.DataArray(x, dims='z'), y=xr.DataArray(y, dims='z'), z=xr.DataArray(z, dims='z'), sec=xr.DataArray(sec, dims='z')).data)(x_j, y_j, h_j, x_j * 0 + 10) npt.assert_array_almost_equal(res1, res2) if 0: c = plt.contourf(X, Y, res1.reshape(X.shape)) plt.colorbar(c) plt.figure() c = plt.contourf(X, Y, res2.reshape(X.shape)) plt.colorbar(c) plt.show()
def test_distance_over_rectangle(): x, y = [-100, 50], [200, -100] windTurbines = IEA37_WindTurbines() site = Rectangle(height=200, width=100, distance_resolution=100) wf_model = NOJ(site, windTurbines) sim_res = wf_model(x, y, wd=[270], ws=[9]) x_j = np.linspace(-100, 500, 100) y_j = np.linspace(-200, 300, 100) flow_map = sim_res.flow_map(HorizontalGrid(x_j, y_j)) Z = flow_map.WS_eff_xylk[:, :, 0, 0] X, Y = flow_map.X, flow_map.Y my = np.argmin(np.abs(Y[:, 0] - 200)) my2 = np.argmin(np.abs(Y[:, 0] + 100)) if 0: import matplotlib.pyplot as plt flow_map.plot_wake_map() H = site.elevation(X, Y) plt.plot(X[my], Z[my] * 10, label='wsp*10') plt.plot(X[my2], Z[my2] * 10, label='wsp*10') plt.contour(X, Y, H) plt.plot(X[my, :50:4], Z[my, :50:4] * 10, '.') plt.plot(x_j, site.elevation(x_j, x_j * 0), label='terrain level') plt.legend() plt.show() ref = [ 9., 3.42, 3.8, 6.02, 6.17, 6.31, 6.43, 7.29, 7.35, 7.41, 7.47, 7.53, 7.58 ] npt.assert_array_almost_equal(Z[my, :50:4], ref, 2)
def test_wake_radius(deficitModel, wake_radius_ref): mean_ref = [105, 68, 135, 93, 123] # check that ref is reasonable npt.assert_allclose(wake_radius_ref, mean_ref, rtol=.5) npt.assert_array_almost_equal(deficitModel.wake_radius( D_src_il=np.reshape([100, 50, 100, 100, 100], (5, 1)), dw_ijlk=np.reshape([500, 500, 1000, 500, 500], (5, 1, 1, 1)), ct_ilk=np.reshape([.8, .8, .8, .4, .8], (5, 1, 1)), TI_ilk=np.reshape([.1, .1, .1, .1, .05], (5, 1, 1)), TI_eff_ilk=np.reshape([.1, .1, .1, .1, .05], (5, 1, 1)))[:, 0, 0, 0], wake_radius_ref) # Check that it works when called from WindFarmModel site = IEA37Site(16) windTurbines = IEA37_WindTurbines() wfm = PropagateDownwind(site, windTurbines, wake_deficitModel=deficitModel, turbulenceModel=GCLTurbulence()) wfm(x=[0, 500], y=[0, 0], wd=[30], ws=[10]) if 0: sim_res = wfm([0], [0], wd=[270], ws=10) sim_res.flow_map(HorizontalGrid(x=np.arange(-100, 1500, 10))).WS_eff.plot() x = np.arange(0, 1500, 10) wr = deficitModel.wake_radius( D_src_il=np.reshape([130], (1, 1)), dw_ijlk=np.reshape(x, (1, len(x), 1, 1)), ct_ilk=sim_res.CT.values, TI_ilk=np.reshape(sim_res.TI.values, (1, 1, 1)), TI_eff_ilk=sim_res.TI_eff.values)[0, :, 0, 0] plt.title(deficitModel.__class__.__name__) plt.plot(x, wr) plt.plot(x, -wr) plt.axis('equal') plt.show()
def aep4smart_start(X, Y, wt_x, wt_y, type=0): sim_res = self.windFarmModel(wt_x, wt_y, type=type, wd=wd, ws=ws) x = np.sort(np.unique(X)) y = np.sort(np.unique(Y)) aep_map = sim_res.flow_map(HorizontalGrid(x, y)).aep_xy() if isinstance(aep_map, xr.DataArray): aep_map = aep_map.values[:, :, 0] return RegularGridInterpolator((y, x), aep_map)(np.array([Y, X]).T)
def test_power_xylk(): site = IEA37Site(16) x, y = site.initial_position.T windTurbines = IEA37_WindTurbines() # NOJ wake model wind_farm_model = IEA37SimpleBastankhahGaussian(site, windTurbines) simulation_result = wind_farm_model(x, y) fm = simulation_result.flow_map(grid=HorizontalGrid(resolution=3)) npt.assert_array_almost_equal(fm.power_xylk(with_wake_loss=False)[:, :, 0, 0] * 1e-6, 3.35)
def test_huge_flow_map(wake_deficitModel, deflectionModel, superpositionModel): site = IEA37Site(16) windTurbines = IEA37_WindTurbines() wake_model = PropagateDownwind(site, windTurbines, wake_deficitModel=wake_deficitModel, superpositionModel=superpositionModel, deflectionModel=deflectionModel, turbulenceModel=STF2005TurbulenceModel()) n_wt = 2 flow_map = wake_model(*site.initial_position[:n_wt].T, wd=0).flow_map(HorizontalGrid(resolution=1000)) # check that deficit matrix > 10MB (i.e. it enters the memory saving loop) assert (np.prod(flow_map.WS_eff_xylk.shape) * n_wt * 8 / 1024**2) > 10 assert flow_map.WS_eff_xylk.shape == (1000, 1000, 1, 1)
def test_NOJ_Nibe_result_wake_map(): # Replicate result from: Jensen, Niels Otto. "A note on wind generator interaction." (1983). def ct_func(_): return 8 / 9 def power_func(*_): return 0 windTurbines = NibeA0 site = UniformSite([1], 0.1) wake_model = NOJ(site, windTurbines) sim_res = wake_model(x=[0], y=[0], wd=[0], ws=[8.1]) WS_eff_xy = sim_res.flow_map(HorizontalGrid( x=[0], y=[0, -40, -100], h=50)).WS_eff_xylk.mean(['wd', 'ws']) npt.assert_array_almost_equal(WS_eff_xy[:, 0], [8.1, 4.35, 5.7])
def test_RotorGridAvg_deficit(): site = IEA37Site(16) x, y = site.initial_position.T windTurbines = IEA37_WindTurbines() wfm = IEA37SimpleBastankhahGaussian(site, windTurbines) flow_map = wfm([0, 500], [0, 0], wd=270, ws=10).flow_map(HorizontalGrid(x=[500], y=np.arange(-100, 100))) plt.plot(flow_map.Y[:, 0], flow_map.WS_eff_xylk[:, 0, 0, 0]) R = windTurbines.diameter() / 2 for name, rotorAvgModel, ref1 in [ ('RotorCenter', RotorCenter(), 7.172723970425709), ('RotorGrid2', EqGridRotorAvg(2), 7.495889360682771), ('RotorGrid3', EqGridRotorAvg(3), 7.633415167369133), ('RotorGrid4', EqGridRotorAvg(4), 7.710215921858325), ('RotorGrid100', EqGridRotorAvg(100), 7.820762402628349), ('RotorGQGrid_4,3', GQGridRotorAvg(4, 3), 7.826105012683896), ('RotorCGI4', CGIRotorAvg(4), 7.848406907726826), ('RotorCGI4', CGIRotorAvg(7), 7.819900693605533), ('RotorCGI4', CGIRotorAvg(9), 7.82149363932618), ('RotorCGI4', CGIRotorAvg(21), 7.821558905416136)]: # test with PropagateDownwind wfm = IEA37SimpleBastankhahGaussian(site, windTurbines, rotorAvgModel=rotorAvgModel) sim_res = wfm([0, 500], [0, 0], wd=270, ws=10) npt.assert_almost_equal(sim_res.WS_eff_ilk[1, 0, 0], ref1) # test with All2AllIterative wfm = All2AllIterative(site, windTurbines, IEA37SimpleBastankhahGaussianDeficit(), rotorAvgModel=rotorAvgModel, superpositionModel=SquaredSum()) sim_res = wfm([0, 500], [0, 0], wd=270, ws=10) npt.assert_almost_equal(sim_res.WS_eff_ilk[1, 0, 0], ref1) plt.plot([-R, R], [sim_res.WS_eff_ilk[1, 0, 0]] * 2, label=name) if 0: plt.legend() plt.show() plt.close('all')
def test_RotorGridAvg_ti(): site = IEA37Site(16) x, y = site.initial_position.T windTurbines = IEA37_WindTurbines() wfm = IEA37SimpleBastankhahGaussian(site, windTurbines, turbulenceModel=STF2017TurbulenceModel()) flow_map = wfm([0, 500], [0, 0], wd=270, ws=10).flow_map(HorizontalGrid(x=[500], y=np.arange(-100, 100))) plt.plot(flow_map.Y[:, 0], flow_map.TI_eff_xylk[:, 0, 0, 0]) R = windTurbines.diameter() / 2 for name, rotorAvgModel, ref1 in [ ('RotorCenter', RotorCenter(), 0.22292190804089568), ('RotorGrid2', EqGridRotorAvg(2), 0.2111162769995657), ('RotorGrid3', EqGridRotorAvg(3), 0.2058616982653193), ('RotorGrid4', EqGridRotorAvg(4), 0.2028701990648858), ('RotorGrid100', EqGridRotorAvg(100), 0.1985255601976247), ('RotorGQGrid_4,3', GQGridRotorAvg(4, 3), 0.1982984399750206)]: # test with PropagateDownwind wfm = IEA37SimpleBastankhahGaussian(site, windTurbines, rotorAvgModel=rotorAvgModel, turbulenceModel=STF2017TurbulenceModel()) sim_res = wfm([0, 500], [0, 0], wd=270, ws=10) npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1) # test with All2AllIterative wfm = All2AllIterative(site, windTurbines, IEA37SimpleBastankhahGaussianDeficit(), rotorAvgModel=rotorAvgModel, superpositionModel=SquaredSum(), turbulenceModel=STF2017TurbulenceModel()) sim_res = wfm([0, 500], [0, 0], wd=270, ws=10) npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1) plt.plot([-R, R], [sim_res.TI_eff_ilk[1, 0, 0]] * 2, label=name) if 0: plt.legend() plt.show() plt.close('all')
def test_superposition_model_indices(): class WTSite(UniformSite): def local_wind(self, x_i=None, y_i=None, h_i=None, wd=None, ws=None, time=False, wd_bin_size=None, ws_bins=None): lw = UniformSite.local_wind(self, x_i=x_i, y_i=y_i, h_i=h_i, wd=wd, ws=ws, wd_bin_size=wd_bin_size, ws_bins=ws_bins) lw['TI'] = xr.DataArray(lw.TI_ilk + np.arange(len(x_i))[:, np.newaxis, np.newaxis] * .1, [('wt', [0, 1, 2]), ('wd', np.atleast_1d(wd)), ('ws', np.atleast_1d(ws))]) return lw site = WTSite([1], 0.1) x_i = [0, 0, 0] y_i = [0, -40, -100] h_i = [50, 50, 50] # WS_ilk different at each wt position TI_ilk = site.local_wind(x_i, y_i, h_i, wd=0, ws=8.1).TI_ilk npt.assert_array_almost_equal(TI_ilk, np.reshape([0.1, 0.2, 0.3], (3, 1, 1)), 10) def get_wf_model(cls): return cls(site, NibeA0, wake_deficitModel=NoWakeDeficit(), superpositionModel=LinearSum(), turbulenceModel=STF2017TurbulenceModel()) for wake_model in [get_wf_model(PropagateDownwind), get_wf_model(All2AllIterative)]: # No wake (ct = 0), i.e. WS_eff == WS TI_eff_ilk = wake_model.calc_wt_interaction(x_i, y_i, h_i, [1, 1, 1], 0.0, 8.1)[1] npt.assert_array_equal(TI_eff_ilk, TI_ilk) # full wake (CT=8/9) ref_TI_eff_ilk = TI_ilk + np.reshape([0, 0.33738364, np.sum([0.19369135, 0.21239116])], (3, 1, 1)) TI_eff_ilk = wake_model.calc_wt_interaction(x_i, y_i, h_i, [0, 0, 0], 0.0, 8.1)[1] npt.assert_array_almost_equal(TI_eff_ilk, ref_TI_eff_ilk) sim_res = wake_model(x_i, y_i, h_i, [0, 0, 0], 0.0, 8.1) TI_eff_ilk = sim_res.flow_map(HorizontalGrid(x=[0], y=y_i, h=50)).TI_eff_xylk[:, 0] npt.assert_array_almost_equal(TI_eff_ilk, ref_TI_eff_ilk)
def test_wake_map(): site = IEA37Site(16) x, y = site.initial_position.T windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml') wake_model = NOJ(site, windTurbines) x_j = np.linspace(-1500, 1500, 200) y_j = np.linspace(-1500, 1500, 100) flow_map = wake_model(x, y, wd=[0], ws=[9]).flow_map(HorizontalGrid(x_j, y_j, 110)) Z = flow_map.WS_eff_xylk.mean((2, 3)) if 0: flow_map.plot_wake_map() plt.show() ref = [ 3.27, 3.27, 9.0, 7.46, 7.46, 7.46, 7.46, 7.31, 7.31, 7.31, 7.31, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3 ] npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
def test_IEA37SimpleBastankhahGaussian_wake_map(): site = IEA37Site(16) x, y = site.initial_position.T windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml') wake_model = IEA37SimpleBastankhahGaussian(site, windTurbines) x_j = np.linspace(-1500, 1500, 200) y_j = np.linspace(-1500, 1500, 100) flow_map = wake_model(x, y, wd=0, ws=9).flow_map(HorizontalGrid(x_j, y_j)) X, Y, Z = flow_map.X, flow_map.Y, flow_map.WS_eff_xylk[:, :, 0, 0] # test that the result is equal to last run (no evidens that these number are correct) ref = [ 3.32, 4.86, 7.0, 8.1, 7.8, 7.23, 6.86, 6.9, 7.3, 7.82, 8.11, 8.04, 7.87, 7.79, 7.85, 8.04, 8.28 ] if 0: flow_map.plot_wake_map() plt.plot(X[49, 100:133:2], Y[49, 100:133:2], '.-') plt.show() npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
def flow_map(self, grid=None, wd=None, ws=None): """Return a FlowMap object with WS_eff and TI_eff of all grid points Parameters ---------- grid : Grid or tuple(X, Y, x, y, h) Grid, e.g. HorizontalGrid or\n tuple(X, Y, x, y, h) where X, Y is the meshgrid for visualizing data\n and x, y, h are the flattened grid points See Also -------- pywake.wind_farm_models.flow_map.FlowMap """ if grid is None: grid = HorizontalGrid() if isinstance(grid, Grid): if isinstance(grid, HorizontalGrid): plane = "XY", self.h if isinstance(grid, YZGrid): plane = 'YZ', grid.x if isinstance(grid, Points): plane = 'xyz', None grid = grid(x_i=self.x, y_i=self.y, h_i=self.h, d_i=self.windFarmModel.windTurbines.diameter( self.type)) else: plane = (None, ) wd, ws = self._wd_ws(wd, ws) X, Y, x_j, y_j, h_j = grid lw_j, WS_eff_jlk, TI_eff_jlk = self.windFarmModel._flow_map( x_j, y_j, h_j, self.sel(wd=wd, ws=ws)) return FlowMap(self, X, Y, lw_j, WS_eff_jlk, TI_eff_jlk, plane=plane)
def test_RotorAvg_deficit(): site = IEA37Site(16) windTurbines = IEA37_WindTurbines() wfm = IEA37SimpleBastankhahGaussian(site, windTurbines, turbulenceModel=STF2017TurbulenceModel()) flow_map = wfm([0, 500], [0, 0], wd=270, ws=10).flow_map(HorizontalGrid(x=[500], y=np.arange(-100, 100))) plt.plot(flow_map.Y[:, 0], flow_map.TI_eff_xylk[:, 0, 0, 0]) R = windTurbines.diameter() / 2 for name, rotorAvgModel, ref1 in [ ('None', None, 0.22292190804089568), ('RotorCenter', RotorCenter(), 0.22292190804089568), ('RotorGrid100', EqGridRotorAvg(100), 0.1989725533174574), ('RotorGQGrid_4,3', GQGridRotorAvg(4, 3), 0.19874837617113356), ('RotorCGI4', CGIRotorAvg(4), 0.19822024411411204), ('RotorCGI4', CGIRotorAvg(21), 0.1989414764606653)]: # test with PropagateDownwind wfm = IEA37SimpleBastankhahGaussian(site, windTurbines, turbulenceModel=STF2017TurbulenceModel(rotorAvgModel=rotorAvgModel)) sim_res = wfm([0, 500], [0, 0], wd=270, ws=10) npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1, err_msg=name) # test with All2AllIterative wfm = All2AllIterative(site, windTurbines, IEA37SimpleBastankhahGaussianDeficit(), turbulenceModel=STF2017TurbulenceModel(rotorAvgModel=rotorAvgModel), superpositionModel=SquaredSum()) sim_res = wfm([0, 500], [0, 0], wd=270, ws=10) npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1) plt.plot([-R, R], [sim_res.WS_eff_ilk[1, 0, 0]] * 2, label=name) if 0: plt.legend() plt.show() plt.close()
def main(): if __name__ == '__main__': from py_wake.examples.data.iea37 import IEA37Site, IEA37_WindTurbines from py_wake import IEA37SimpleBastankhahGaussian import matplotlib.pyplot as plt site = IEA37Site(16) x, y = site.initial_position.T windTurbines = IEA37_WindTurbines() wind_farm_model = IEA37SimpleBastankhahGaussian(site, windTurbines) simulation_result = wind_farm_model(x, y) fm = simulation_result.flow_map(wd=30) fm.plot_wake_map() plt.figure() fm.plot(fm.power_xylk().sum(['wd', 'ws']) * 1e-3, "Power [kW]") fm = simulation_result.flow_map(grid=HorizontalGrid(resolution=50)) plt.figure() fm.plot(fm.aep_xy(), "AEP [GWh]") plt.show()
def test_BastankhahGaussian_wake_map(): site = IEA37Site(16) x, y = site.initial_position.T windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml') wake_model = BastankhahGaussian(site, windTurbines) x_j = np.linspace(-1500, 1500, 200) y_j = np.linspace(-1500, 1500, 100) flow_map = wake_model(x, y, wd=0, ws=9).flow_map(HorizontalGrid(x_j, y_j)) X, Y = flow_map.X, flow_map.Y Z = flow_map.WS_eff_xylk[:, :, 0, 0] # test that the result is equal to last run (no evidens that these number are correct) ref = [ 0.18, 3.6, 7.27, 8.32, 7.61, 6.64, 5.96, 6.04, 6.8, 7.69, 8.08, 7.87, 7.59, 7.46, 7.55, 7.84, 8.19 ] if 0: flow_map.plot_wake_map() plt.plot(X[49, 100:133:2], Y[49, 100:133:2], '.-') windTurbines.plot(x, y) plt.show() npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
def test_fuga(): # move turbine 1 600 300 wt_x = [-250, 600, -500, 0, 500, -250, 250] wt_y = [433, 300, 0, 0, 0, -433, -433] wts = HornsrevV80() path = tfp + 'fuga/2MW/Z0=0.03000000Zi=00401Zeta0=0.00E+00/' site = UniformSite([1, 0, 0, 0], ti=0.075) wake_model = Fuga(path, site, wts) res, _ = timeit(wake_model.__call__, verbose=0, line_profile=0, profile_funcs=[ FugaDeficit.interpolate, LUTInterpolator.__call__, GridInterpolator.__call__ ])(x=wt_x, y=wt_y, wd=[30], ws=[10]) npt.assert_array_almost_equal(res.WS_eff_ilk.flatten(), [ 10.00669629, 10., 8.47606501, 10.03143097, 9.37288077, 8.49301941, 8.07462708 ], 8) npt.assert_array_almost_equal(res.ct_ilk.flatten(), [ 0.7926384, 0.793, 0.80647607, 0.79130273, 0.80177967, 0.80649302, 0.80607463 ], 8) x_j = np.linspace(-1500, 1500, 500) y_j = np.linspace(-1500, 1500, 300) wake_model = Fuga(path, site, wts) sim_res = wake_model(wt_x, wt_y, wd=[30], ws=[10]) flow_map70 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=70)) flow_map73 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=73)) X, Y = flow_map70.XY Z70 = flow_map70.WS_eff_xylk[:, :, 0, 0] Z73 = flow_map73.WS_eff_xylk[:, :, 0, 0] if 0: flow_map70.plot_wake_map(levels=np.arange(6, 10.5, .1)) plt.plot(X[0], Y[140]) plt.figure() plt.plot(X[0], Z70[140, :], label="Z=70m") plt.plot(X[0], Z73[140, :], label="Z=73m") plt.plot(X[0, 100:400:10], Z70[140, 100:400:10], '.') print(list(np.round(Z70.data[140, 100:400:10], 4))) print(list(np.round(Z73.data[140, 100:400:10], 4))) plt.legend() plt.show() npt.assert_array_almost_equal(Z70[140, 100:400:10], [ 10.0407, 10.0438, 10.0438, 10.013, 9.6847, 7.8787, 6.9561, 9.2251, 9.9686, 10.0382, 10.0498, 10.0569, 10.0325, 9.1787, 7.4004, 9.1384, 10.0329, 10.0297, 10.0232, 9.9265, 9.3163, 8.0768, 6.8858, 8.3754, 9.9592, 10.0197, 10.0118, 10.0141, 10.0118, 10.0095 ], 4) npt.assert_array_almost_equal(Z73[140, 100:400:10], [ 10.0404, 10.0435, 10.0433, 10.0113, 9.6836, 7.9206, 7.0218, 9.2326, 9.9665, 10.0376, 10.0494, 10.0563, 10.0304, 9.1896, 7.4515, 9.15, 10.0317, 10.0294, 10.0226, 9.9245, 9.3252, 8.1192, 6.9462, 8.3988, 9.9574, 10.0194, 10.0117, 10.014, 10.0117, 10.0094 ], 4)
def test_superposition_model_indices(superpositionModel, sum_func): class WTSite(UniformSite): def local_wind(self, x_i=None, y_i=None, h_i=None, wd=None, ws=None, wd_bin_size=None, ws_bins=None): lw = UniformSite.local_wind(self, x_i=x_i, y_i=y_i, h_i=h_i, wd=wd, ws=ws, wd_bin_size=wd_bin_size, ws_bins=ws_bins) lw['WS'] = xr.DataArray( lw.WS_ilk + np.arange(len(x_i))[:, np.newaxis, np.newaxis], [('wt', [0, 1, 2]), ('wd', np.atleast_1d(wd)), ('ws', np.atleast_1d(ws))]) return lw site = WTSite([1], 0.1) x_i = [0, 0, 0] y_i = [0, -40, -100] h_i = [50, 50, 50] # WS_ilk different at each wt position WS_ilk = site.local_wind(x_i, y_i, h_i, wd=0, ws=8.1).WS_ilk npt.assert_array_equal(WS_ilk, np.reshape([8.1, 9.1, 10.1], (3, 1, 1))) for wake_model in [ PropagateDownwind(site, NibeA0, wake_deficitModel=NOJDeficit(), superpositionModel=superpositionModel), All2AllIterative(site, NibeA0, wake_deficitModel=NOJDeficit(), superpositionModel=superpositionModel) ]: # No wake (ct = 0), i.e. WS_eff == WS WS_eff_ilk = wake_model.calc_wt_interaction(x_i, y_i, h_i, [1, 1, 1], 0.0, 8.1)[0] npt.assert_array_equal(WS_eff_ilk, WS_ilk) ref = WS_ilk - np.reshape( [0, 3.75, sum_func([2.4, 3.58974359])], (3, 1, 1)) # full wake (CT=8/9) WS_eff_ilk = wake_model.calc_wt_interaction(x_i, y_i, h_i, [0, 0, 0], 0.0, 8.1)[0] npt.assert_array_almost_equal(WS_eff_ilk, ref) sim_res = wake_model(x_i, y_i, h_i, [0, 0, 0], 0.0, 8.1) WS_eff_ilk = sim_res.flow_map(HorizontalGrid(x=[0], y=y_i, h=50)).WS_eff_xylk[:, 0] npt.assert_array_almost_equal(WS_eff_ilk, ref)
def wake_map(self, x_j=None, y_j=None, height_level=None, wt_x=[], wt_y=[], wt_type=0, wt_height=None, wd=None, ws=None): """Calculate wake(effective wind speed) map Parameters ---------- x_j : array_like or None, optional X position map points y_j : array_like Y position of map points height_level : int, float or None, optional Height of wake map\n If None, default, the mean hub height is used wt_x : array_like, optional X position of wind turbines wt_y : array_like, optional Y position of wind turbines wt_type : array_like or None, optional Type of the wind turbines wt_height : array_like or None, optional Hub height of the wind turbines\n If None, default, the standard hub height is used wd : int, float, array_like or None Wind directions(s)\n If None, default, the wake is calculated for site.default_wd ws : int, float, array_like or None Wind speed(s)\n If None, default, the wake is calculated for site.default_ws Returns ------- X_j : array_like 2d array of map x positions Y_j : array_like 2d array of map y positions WS_eff_avg : array_like 2d array of average effective local wind speed taking into account the probability of wind direction and speed See Also -------- plot_wake_map """ sim_res = self.wake_model(x=wt_x, y=wt_y, type=wt_type, h=wt_height, wd=wd, ws=ws) flow_map = sim_res.flow_map( HorizontalGrid(x=x_j, y=y_j, h=height_level)) X, Y = flow_map.XY return X, Y, flow_map.WS_eff_xylk.mean((2, 3))
def aep_map(self, x_j=None, y_j=None, type_j=None, wt_x=[], wt_y=[], wt_type=0, wt_height=None, wd=None, ws=None): """Calculate AEP map The map represents the of AEP produced by a new turbine at the specified positions Parameters ---------- x_j : array_like or None, optional X position map points (potential turbine positions) y_j : array_like Y position of map points (potential turbine positions) type_j : int, float or None, optional Type of potential turbine positions\n If None, default, first turbine type(0) is used wt_x : array_like, optional X position of the current wind turbines wt_y : array_like, optional Y position of the current wind turbines wt_type : array_like or None, optional Type of the current wind turbines wt_height : array_like or None, optional Hub height of the current wind turbines\n If None, default, the standard hub height is used wd : int, float, array_like or None Wind directions(s)\n If None, default, the wake is calculated for site.default_wd ws : int, float, array_like or None Wind speed(s)\n If None, default, the wake is calculated for site.default_ws Returns ------- X_j : array_like 2d array of map x positions Y_j : array_like 2d array of map y positions WS_eff_avg : array_like 2d array of average effective local wind speed taking into account the probability of wind direction and speed """ h_j = self.windTurbines.hub_height(type_j) sim_res = self.wake_model(x=wt_x, y=wt_y, type=wt_type, h=wt_height, wd=wd, ws=ws) flow_map = sim_res.flow_map(HorizontalGrid(x=x_j, y=y_j, h=h_j)) X, Y = flow_map.XY aep_xy = flow_map.aep_xy(normalize_probabilities=True) return X, Y, aep_xy
def test_fuga(): # move turbine 1 600 300 wt_x = [-250, 600, -500, 0, 500, -250, 250] wt_y = [433, 300, 0, 0, 0, -433, -433] wts = HornsrevV80() path = tfp + 'fuga/2MW/Z0=0.03000000Zi=00401Zeta0=0.00E+0/' site = UniformSite([1, 0, 0, 0], ti=0.075) wake_model = Fuga(path, site, wts) res = wake_model(x=wt_x, y=wt_y, wd=[30], ws=[10]) npt.assert_array_almost_equal(res.WS_eff_ilk.flatten(), [ 10.002683492812844, 10.0, 8.413483643142389, 10.036952526815286, 9.371203842245153, 8.437429367715435, 8.012759083790058 ], 8) npt.assert_array_almost_equal(res.ct_ilk.flatten(), [ 0.79285509, 0.793, 0.80641348, 0.79100456, 0.80180315, 0.80643743, 0.80601276 ], 8) x_j = np.linspace(-1500, 1500, 500) y_j = np.linspace(-1500, 1500, 300) wake_model = Fuga(path, site, wts) sim_res = wake_model(wt_x, wt_y, wd=[30], ws=[10]) flow_map70 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=70)) flow_map73 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=73)) X, Y = flow_map70.XY Z70 = flow_map70.WS_eff_xylk[:, :, 0, 0] Z73 = flow_map73.WS_eff_xylk[:, :, 0, 0] if 0: import matplotlib.pyplot as plt flow_map70.plot_wake_map(levels=np.arange(6, 10.5, .1)) plt.plot(X[0], Y[140]) plt.figure() plt.plot(X[0], Z70[140, :], label="Z=70m") plt.plot(X[0], Z73[140, :], label="Z=73m") plt.plot(X[0, 100:400:10], Z70[140, 100:400:10], '.') print(list(np.round(Z70[140, 100:400:10], 4))) print(list(np.round(Z73[140, 100:400:10], 4))) plt.legend() plt.show() # npt.assert_array_almost_equal( # Z70[140, 100:400:10], # [10.0547, 10.0519, 10.0741, 10.0099, 9.6774, 7.8538, 6.8484, 9.2134, 9.9749, 10.0232, 10.0658, 10.0189, 10.0187, # 9.1496, 7.2077, 9.1154, 10.0183, 10.0008, 10.0146, 9.8838, 9.2848, 7.9681, 6.7412, 8.3149, 9.9114, 10.0119, # 10.0011, 9.9979, 10.0002, 9.9981], 4) npt.assert_array_almost_equal(Z70[140, 100:400:10], [ 10.0547, 10.0519, 10.0718, 10.0093, 9.6786, 7.8589, 6.8539, 9.2199, 9.9837, 10.036, 10.0796, 10.0469, 10.0439, 9.1866, 7.2552, 9.1518, 10.0449, 10.0261, 10.0353, 9.9256, 9.319, 8.0062, 6.789, 8.3578, 9.9393, 10.0332, 10.0191, 10.0186, 10.0191, 10.0139 ], 4) npt.assert_array_almost_equal(Z73[140, 100:400:10], [ 10.0542, 10.0514, 10.0706, 10.0075, 9.6778, 7.9006, 6.9218, 9.228, 9.9808, 10.0354, 10.0786, 10.0464, 10.0414, 9.1973, 7.3099, 9.1629, 10.0432, 10.0257, 10.0344, 9.9236, 9.3274, 8.0502, 6.8512, 8.3813, 9.9379, 10.0325, 10.0188, 10.0183, 10.019, 10.0138 ], 4)