ld/add Jensen and TurbOPark wake models

examples/MITWindfarm_quickstart.ipynb

@@ -223,24 +223,31 @@
    "source": [
     "### Wake Model\n",
     "\n",
-    "Within the package, there is a `WakeModel` abstract class with two concrete types: the `GaussianWakeModel` and the `VariableKwGaussianWakeModel`. \n",
+    "Within the package, there is a `WakeModel` abstract class with four concrete types: `GaussianWakeModel`, `VariableKwGaussianWakeModel`, `JensenWakeModel`, and `TurbOParkWakeModel`. \n",
     "\n",
     "The `GaussianWakeModel` has the following optional arguments (and default values): `sigma` (0.25), `kw` (0.07), `WATI_sigma_multiplier` (1.0), and `xmax` (100.0).\n",
     "\n",
-    "On the other hand, the `VariableKwGaussianWakeModel` adjusts the wake spreading rate based on the\n",
+    "The `VariableKwGaussianWakeModel` adjusts the wake spreading rate based on the\n",
     "`Ctprime` and the `TI` experienced by the wake-generating turbine based on the linear relationship: $kw = a * TI + b * C_T' + c$. Therefore, `VariableKwGaussianWakeModel` has the following optional arguments (and default values): `a`, `b`, `c`, `sigma` ($\\frac{1}{\\sqrt{8}}$), `WATI_sigma_multiplier`(1.0), and `xmax` (100.0).\n",
     "\n",
-    "If neither of these is explicitly provided to the `WindFarm` model, the `GaussianWakeModel` is used."
+    "The `JensenWakeModel` has one optional argument `kw` (0.07).\n",
+    "\n",
+    "The `TurbOParkWakeModel` has two versions: the newer Gaussian TurbOPark model proposed in Pedersen 2022 (`TurbOParkWake`) and the older top-hat TurbOPark model proposed in Nygaard 2020 (`TopHatTurbOParkWake`). The Gaussian TurbOPark model is used by default. Both models have the following optional arguments and default values: `A` (0.04), `TIamb` (0.06), `c_1` (1.5), `c_2` (0.8), and `xmax` (100.0).\n",
+    "\n",
+    "If no wake model is explicitly provided to the `WindFarm` model, the `GaussianWakeModel` is used."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "gaussian_wake_model = GaussianWakeModel()\n",
-    "variable_gaussian_wake_model = VariableKwGaussianWakeModel(a = 1, b = 1, c = 1)"
+    "variable_gaussian_wake_model = VariableKwGaussianWakeModel(a = 1, b = 1, c = 1)\n",
+    "jensen_wake_model = JensenWakeModel()\n",
+    "gaussian_turbopark_wake_model = TurbOParkWakeModel()\n",
+    "tophat_turbopark_wake_model = TurbOParkWakeModel(gaussian_profile = False, A = 0.6)"
    ]
   },
   {

mitwindfarm/Superposition.py

@@ -25,3 +25,7 @@ def __call__(self, base_windfield: Windfield, wakes: list[Wake]) -> Windfield:
 class Dominant(Superposition):
     def __call__(self, base_windfield: Windfield, wakes: list[Wake]) -> Windfield:
         return Superimposed(base_windfield, wakes, method="dominant")
+
+class NQuadratic(Superposition):
+    def __call__(self, base_windfield: Windfield, wakes: list[Wake]) -> Windfield:
+        return Superimposed(base_windfield, wakes, method="nquadratic")