Flow model chain

examples/cases/KUL_LES/wind_energy_system/analysis_US.yaml

@@ -64,7 +64,7 @@ HPC_config:
   mesh_node_number: 2
   mesh_ntasks_per_node: 48
   mesh_wall_time_hours: 1
-  run_partition: ""
+  mesh_partition: ""
   #
   wckey: ""
 

pyproject.toml

@@ -38,7 +38,7 @@ classifiers = [
 ]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "windIO @ git+https://github.com/EUFlow/windIO.git",
+    "windIO @ git+https://github.com/bjarketol/windIO.git@flow-model-chain",
     "xarray",
     "scipy",
     "pyyaml",
@@ -70,6 +70,7 @@ dev = [
     "ncplot",
     "nctoolkit",
     "cartopy",
+    "pre-commit",
 ]
 docs = [
     "sphinx>=7.0",

tests/conftest.py

@@ -149,6 +149,179 @@ def cleanup_test_outputs():
 }
 
 
+def make_mixed_type_timeseries_system_dict(flow_model_name):
+    """Build a system dict with TWO turbine types at TWO hub heights.
+
+    3x3 layout (9 turbines), per-turbine timeseries inflow with dims
+    ["time", "wind_turbine"] and NO vertical ``height`` profile (the wind
+    speeds are already at each turbine's own hub height, as a microscale
+    terrain-speedup model would produce). This exercises the mixed
+    hub-height + per-turbine path in the pyWake API.
+
+    Type 0 is the DTU 10MW at 119 m; type 1 is a smaller turbine (half the
+    power, 120 m rotor, 90 m hub) so both the type assignment and the two
+    hub heights are genuinely distinct. Wind speeds are kept in 8-11 m/s,
+    away from cut-in/out, so the comparison is unaffected by curve edges.
+    """
+    n_times = 5
+    n_turbines = 9
+
+    # 3x3 grid, ~5D spacing of the larger rotor (5 * 178.3)
+    spacing = 5 * _TURBINE["rotor_diameter"]
+    axis = [0.0, spacing, 2 * spacing]
+    grid_x = [axis[i] for _ in range(3) for i in range(3)]
+    grid_y = [axis[j] for j in range(3) for _ in range(3)]
+
+    # Alternating type assignment -> [0, 1, 0, 1, 0, 1, 0, 1, 0]
+    turbine_type_idx = [k % 2 for k in range(n_turbines)]
+
+    perf = _TURBINE["performance"]
+    pc_ws = perf["power_curve"]["power_wind_speeds"]
+    pc_p = perf["power_curve"]["power_values"]
+    ct_ws = perf["Ct_curve"]["Ct_wind_speeds"]
+    ct_v = perf["Ct_curve"]["Ct_values"]
+    type1_power = [0.5 * p for p in pc_p]
+
+    # Deterministic per-turbine timeseries (no RNG), wind from ~270 deg so the
+    # rows along x interact.
+    base_ws = [8.0, 9.0, 10.0, 8.5, 11.0]
+    base_wd = [270.0, 268.0, 272.0, 270.5, 269.0]
+    ws_data, wd_data, ti_data = [], [], []
+    for t in range(n_times):
+        ws_data.append([base_ws[t] + 0.1 * i for i in range(n_turbines)])
+        wd_data.append([base_wd[t] + 0.2 * (i - 4) for i in range(n_turbines)])
+        ti_data.append([0.06 + 0.002 * i for i in range(n_turbines)])
+
+    return {
+        "name": "Dict test: mixed turbine types and hub heights",
+        "site": {
+            "name": "Test site",
+            "boundaries": {
+                "polygons": [
+                    {
+                        "x": [-spacing, 3 * spacing, 3 * spacing, -spacing],
+                        "y": [3 * spacing, 3 * spacing, -spacing, -spacing],
+                    }
+                ]
+            },
+            "energy_resource": {
+                "name": "Test resource",
+                "wind_resource": {
+                    "time": list(range(n_times)),
+                    "wind_turbine": list(range(n_turbines)),
+                    "wind_speed": {"data": ws_data, "dims": ["time", "wind_turbine"]},
+                    "wind_direction": {
+                        "data": wd_data,
+                        "dims": ["time", "wind_turbine"],
+                    },
+                    "turbulence_intensity": {
+                        "data": ti_data,
+                        "dims": ["time", "wind_turbine"],
+                    },
+                },
+            },
+        },
+        "wind_farm": {
+            "name": "Test farm",
+            "layouts": [
+                {
+                    "coordinates": {"x": grid_x, "y": grid_y},
+                    "turbine_types": turbine_type_idx,
+                }
+            ],
+            "turbine_types": {
+                0: {
+                    "name": "type_0_DTU10MW",
+                    "hub_height": 119.0,
+                    "rotor_diameter": 178.3,
+                    "performance": {
+                        "power_curve": {
+                            "power_wind_speeds": pc_ws,
+                            "power_values": pc_p,
+                        },
+                        "Ct_curve": {"Ct_wind_speeds": ct_ws, "Ct_values": ct_v},
+                    },
+                },
+                1: {
+                    "name": "type_1_small",
+                    "hub_height": 90.0,
+                    "rotor_diameter": 120.0,
+                    "performance": {
+                        "power_curve": {
+                            "power_wind_speeds": pc_ws,
+                            "power_values": type1_power,
+                        },
+                        "Ct_curve": {"Ct_wind_speeds": ct_ws, "Ct_values": ct_v},
+                    },
+                },
+            },
+        },
+        "attributes": {
+            "flow_model": {"name": flow_model_name},
+            "analysis": {
+                "wind_deficit_model": {
+                    "name": "Bastankhah2014",
+                    "wake_expansion_coefficient": {
+                        "k_a": 0.0,
+                        "k_b": 0.04,
+                        "free_stream_ti": False,
+                    },
+                    "ceps": 0.2,
+                    "use_effective_ws": True,
+                },
+                "axial_induction_model": "Madsen",
+                "deflection_model": {"name": "None"},
+                "turbulence_model": {"name": "CrespoHernandez"},
+                "superposition_model": {
+                    "ws_superposition": "Linear",
+                    "ti_superposition": "Squared",
+                },
+                "rotor_averaging": {"name": "Center"},
+                "blockage_model": {"name": "None"},
+            },
+            "model_outputs_specification": {
+                "turbine_outputs": {
+                    "turbine_nc_filename": "turbine_data.nc",
+                    "output_variables": ["power"],
+                },
+            },
+        },
+    }
+
+
+def make_mixed_type_profile_system_dict(flow_model_name):
+    """Mixed types/hub heights with a (time, height) vertical wind profile.
+
+    Same farm as :func:`make_mixed_type_timeseries_system_dict` (two types at
+    119 m and 90 m), but the inflow is given as a vertical profile over a
+    ``height`` dimension that brackets both hub heights, so WIFA must
+    interpolate the profile to each turbine's hub height. The wind direction
+    veers around 360 deg to exercise circular interpolation.
+    """
+    system = make_mixed_type_timeseries_system_dict(flow_model_name)
+
+    n_times = 5
+    heights = [60.0, 90.0, 120.0, 150.0]
+    href, alpha = 119.0, 0.14  # power-law shear reference + exponent
+    ws_base = [8.0, 9.0, 10.0, 8.5, 11.0]
+    wd_base = [358.0, 359.0, 357.0, 0.5, 359.5]  # straddles 360 deg
+
+    ws_data, wd_data, ti_data = [], [], []
+    for t in range(n_times):
+        ws_data.append([ws_base[t] * (h / href) ** alpha for h in heights])
+        wd_data.append([(wd_base[t] + 0.05 * (h - href)) % 360 for h in heights])
+        ti_data.append([max(0.08 - 0.0001 * (h - 60.0), 0.02) for h in heights])
+
+    system["site"]["energy_resource"]["wind_resource"] = {
+        "time": list(range(n_times)),
+        "height": heights,
+        "wind_speed": {"data": ws_data, "dims": ["time", "height"]},
+        "wind_direction": {"data": wd_data, "dims": ["time", "height"]},
+        "turbulence_intensity": {"data": ti_data, "dims": ["time", "height"]},
+    }
+    return system
+
+
 def make_timeseries_per_turbine_system_dict(flow_model_name):
     """Build a complete system dict with per-turbine timeseries data including density.