diff --git a/examples/foswec_capytaine.ipynb b/examples/foswec_capytaine.ipynb new file mode 100644 index 00000000..0b18b18d --- /dev/null +++ b/examples/foswec_capytaine.ipynb @@ -0,0 +1,2688 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# FOSWEC BEM and impedance\n", + "\n", + " - SAND report: https://doi.org/10.2172/1717884\n", + " - Journal paper: https://doi.org/10.1016/j.energy.2021.122485\n", + " - YouTube video: https://youtu.be/OUxbaEC2K6Y" + ] + }, + { + "cell_type": "code", + "execution_count": 372, + "metadata": {}, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "import capytaine as cpy\n", + "import wecopttool as wot\n", + "wot.set_loglevel('INFO')\n", + "import gmsh\n", + "import pygmsh\n", + "import xarray as xr\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "from scipy.linalg import block_diag\n", + "import scipy.io as sio" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# BEM\n", + "## Set up geometry and Capytaine" + ] + }, + { + "cell_type": "code", + "execution_count": 373, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Warning: STL can only write triangle cells. Discarding vertex, tetra, line.\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;33mWarning:\u001b[0m\u001b[33m STL can only write triangle cells. Discarding vertex, tetra, line.\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:capytaine.bodies.bodies:Stored 804 triangle faces as quadrilaterals\n", + "INFO:capytaine.bodies.bodies:New floating body: fb_20230327214318897695.\n", + "INFO:capytaine.bodies.bodies:Clipping fb_20230327214318897695 with respect to Plane(normal=[0. 0. 1.], point=[0. 0. 0.])\n", + "INFO:capytaine.bodies.bodies:New floating body: 0_0+1_0.\n" + ] + } + ], + "source": [ + "def flap(thickness=0.1, width=1, draft=1.15, xloc=0, ofst=0.1, mesh_size_factor=1):\n", + "\n", + " with pygmsh.occ.Geometry() as geom:\n", + " gmsh.option.setNumber('Mesh.MeshSizeFactor', mesh_size_factor)\n", + " flap = geom.add_box([-1*thickness/2, 0, 0],\n", + " [1*thickness, width, -draft])\n", + " geom.translate(flap, [xloc, 0, 0])\n", + " geom.translate(flap, [0, 0, ofst])\n", + " mesh = geom.generate_mesh()\n", + " return mesh\n", + "\n", + "draft = 1.15\n", + "spacing = 1.44\n", + "\n", + "mesh = flap(mesh_size_factor=0.65)\n", + "mesh.write('foswec_capytaine_one_flap.stl')\n", + "fb = cpy.FloatingBody.from_meshio(mesh)\n", + "fb.keep_immersed_part()\n", + "my_axis = cpy.meshes.geometry.Axis((0, 1, 0), \n", + " point=(0, 0, -1*draft))\n", + "fb.add_rotation_dof(axis=my_axis, name='rot_about_shaft')\n", + "array = fb.assemble_regular_array(distance=spacing, nb_bodies=(2, 1))\n", + "\n", + "array.dofs['bow'] = array.dofs.pop('0_0__rot_about_shaft')\n", + "array.dofs['aft'] = array.dofs.pop('1_0__rot_about_shaft')\n", + "# array.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Animation to check DOFs" + ] + }, + { + "cell_type": "code", + "execution_count": 374, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:capytaine.ui.vtk.animation:Precompute motions of line_of_fb_20230327214318897695_mesh before animation.\n" + ] + } + ], + "source": [ + "animation = array.animate(motion={ i : 0.2 for i in array.dofs.keys() }, loop_duration=1.0)\n", + "animation.save('foswec_capytaine.ogv', camera_position=[-5, -5, 5], resolution=(int(10e2),int(10e2)))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run Capytaine" + ] + }, + { + "cell_type": "code", + "execution_count": 375, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:              (radiating_dof: 2, influenced_dof: 2, omega: 60,\n",
+       "                          wave_direction: 1)\n",
+       "Coordinates:\n",
+       "  * radiating_dof        (radiating_dof) object 'bow' 'aft'\n",
+       "  * influenced_dof       (influenced_dof) object 'bow' 'aft'\n",
+       "  * omega                (omega) float64 0.07854 0.1571 0.2356 ... 4.634 4.712\n",
+       "  * wave_direction       (wave_direction) float64 0.0\n",
+       "    g                    float64 9.81\n",
+       "    rho                  float64 1.025e+03\n",
+       "    body_name            <U41 'array_of_fb_20230327210526733813_immersed'\n",
+       "    water_depth          float64 inf\n",
+       "Data variables:\n",
+       "    impedance            (radiating_dof, influenced_dof, omega) complex128 (1...\n",
+       "    added_mass           (omega, radiating_dof, influenced_dof) float64 365.4...\n",
+       "    radiation_damping    (omega, radiating_dof, influenced_dof) float64 1.383...\n",
+       "    diffraction_force    (omega, wave_direction, influenced_dof) complex128 (...\n",
+       "    Froude_Krylov_force  (omega, wave_direction, influenced_dof) complex128 0...
" + ], + "text/plain": [ + "\n", + "Dimensions: (radiating_dof: 2, influenced_dof: 2, omega: 60,\n", + " wave_direction: 1)\n", + "Coordinates:\n", + " * radiating_dof (radiating_dof) object 'bow' 'aft'\n", + " * influenced_dof (influenced_dof) object 'bow' 'aft'\n", + " * omega (omega) float64 0.07854 0.1571 0.2356 ... 4.634 4.712\n", + " * wave_direction (wave_direction) float64 0.0\n", + " g float64 9.81\n", + " rho float64 1.025e+03\n", + " body_name no zero frequency\n", + "file_name = 'foswec_capytaine.nc'\n", + "if not os.path.isfile(file_name):\n", + " bem_data = wot.run_bem(array, freq)\n", + "else:\n", + " bem_data = wot.read_netcdf(file_name)\n", + "bem_data" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate impedance" + ] + }, + { + "cell_type": "code", + "execution_count": 376, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:wecopttool.hydrostatics:Using the geometric centroid as the center of gravity (COG).\n", + "INFO:wecopttool.hydrostatics:Using the center of gravity (COG) as the rotation center for hydrostatics.\n", + "INFO:capytaine.bodies.bodies:Clipping array_of_fb_20230327214318897695_immersed with respect to Plane(normal=[0. 0. 1.], point=[0. 0. 0.])\n", + "INFO:capytaine.meshes.clipper:Clipping fb_20230327214318897695_mesh by Plane(normal=[0. 0. 1.], point=[0. 0. 0.]): no action.\n", + "INFO:capytaine.meshes.clipper:Clipping repetition_1_of_fb_20230327214318897695_mesh by Plane(normal=[0. 0. 1.], point=[0. 0. 0.]): no action.\n" + ] + } + ], + "source": [ + "stiffness = wot.hydrostatics.stiffness_matrix(array).values\n", + "\n", + "rho = 1e3\n", + "m = fb.volume * rho * 0.9 # just less than neutrally buoyant\n", + "Ixx_center = 1/12 * m * draft\n", + "Ixx_shaft = Ixx_center + m*draft/2\n", + "inertia = Ixx_shaft * np.eye(2)\n", + "\n", + "hydro = wot.linear_hydrodynamics(bem_data=bem_data, \n", + " inertia_matrix=inertia, \n", + " hydrostatic_stiffness=stiffness)\n", + "\n", + "Zi = wot.hydrodynamic_impedance(hydro)\n", + "Zi.name = 'impedance'" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save dataset to netCDF" + ] + }, + { + "cell_type": "code", + "execution_count": 377, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'impedance' (radiating_dof: 2, influenced_dof: 2, omega: 60)>\n",
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+       "...\n",
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+       "          2.71555540e+02+1.74320825e+03j,\n",
+       "          3.32798455e+02+1.82957919e+03j,\n",
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+       "          1.13533755e+03+1.69554956e+03j,\n",
+       "          1.09006251e+03+1.76629778e+03j]]])\n",
+       "Coordinates:\n",
+       "  * radiating_dof   (radiating_dof) object 'bow' 'aft'\n",
+       "  * influenced_dof  (influenced_dof) object 'bow' 'aft'\n",
+       "    g               float64 9.81\n",
+       "    rho             float64 1.025e+03\n",
+       "    body_name       <U41 'array_of_fb_20230327210526733813_immersed'\n",
+       "    water_depth     float64 inf\n",
+       "  * omega           (omega) float64 0.07854 0.1571 0.2356 ... 4.555 4.634 4.712
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4.04709668e+02+1.91419172e+03j,\n", + " 4.94982112e+02+1.99532844e+03j,\n", + " 6.06331215e+02+2.06473363e+03j,\n", + " 7.33191874e+02+2.11350569e+03j,\n", + " 8.68129561e+02+2.14097999e+03j,\n", + " 1.00990376e+03+2.13537946e+03j,\n", + " 1.14118805e+03+2.08768330e+03j,\n", + " 1.24733552e+03+2.01420755e+03j,\n", + " 1.32108723e+03+1.92844995e+03j,\n", + " 1.35524215e+03+1.83458486e+03j,\n", + " 1.35285503e+03+1.74831234e+03j,\n", + " 1.33071934e+03+1.68648299e+03j,\n", + " 1.29071091e+03+1.65109758e+03j,\n", + " 1.23607718e+03+1.63906578e+03j,\n", + " 1.18278323e+03+1.65279351e+03j,\n", + " 1.13533755e+03+1.69554956e+03j,\n", + " 1.09006251e+03+1.76629778e+03j]]])\n", + "Coordinates:\n", + " * radiating_dof (radiating_dof) object 'bow' 'aft'\n", + " * influenced_dof (influenced_dof) object 'bow' 'aft'\n", + " g float64 9.81\n", + " rho float64 1.025e+03\n", + " body_name " + ] + }, + "execution_count": 381, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(np.real(Zi.squeeze())).plot(col='radiating_dof', row='influenced_dof')" + ] + }, + { + "cell_type": "code", + "execution_count": 382, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 382, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(20*np.log10(np.abs(Zi.squeeze()*1j*Zi.omega))).plot(col='radiating_dof', row='influenced_dof')" + ] + }, + { + "cell_type": "code", + "execution_count": 383, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Excitation')" + ] + }, + "execution_count": 383, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(nrows=2,\n", + " sharex=True)\n", + "exc = (hydro['diffraction_force'] + hydro['Froude_Krylov_force']).squeeze()\n", + "np.abs(exc).plot(hue='influenced_dof', ax=ax[0])\n", + "np.arctan(np.real(exc)/np.imag(exc)).plot(hue='influenced_dof', ax=ax[1], add_legend=False)\n", + "\n", + "for axi in ax:\n", + " axi.set_title('')\n", + " axi.label_outer()\n", + " axi.autoscale(enable=True, axis='x', tight=True)\n", + "\n", + "ax[0].set_title('Excitation')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PTO" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Losses" + ] + }, + { + "cell_type": "code", + "execution_count": 384, + "metadata": {}, + "outputs": [], + "source": [ + "winding_resistance = 1.082*1e-4\n", + "torque_coefficient = 0.943\n", + "\n", + "def power_loss(speed, torque):\n", + " return winding_resistance * (torque / torque_coefficient)**2 " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Controller" + ] + }, + { + "cell_type": "code", + "execution_count": 385, + "metadata": {}, + "outputs": [], + "source": [ + "def my_controller(pto, wec, x_wec, x_opt, waves=None, nsubsteps=1):\n", + " ndof = pto.ndof\n", + " vel_td = pto.velocity(wec, x_wec, x_opt, waves, nsubsteps)\n", + " pos_td = pto.position(wec, x_wec, x_opt, waves, nsubsteps)\n", + " Kp = np.eye(2) * x_opt[0] + np.rot90(np.eye(2)) * x_opt[1]\n", + " Ki = np.eye(2) * x_opt[2] + np.rot90(np.eye(2)) * x_opt[3]\n", + " # Kp = np.reshape(x_opt[:4],(2,2))\n", + " # Ki = np.reshape(x_opt[4:],(2,2))\n", + " force_td = np.dot(vel_td,Kp) + np.dot(pos_td,Ki)\n", + " return force_td" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create object" + ] + }, + { + "cell_type": "code", + "execution_count": 386, + "metadata": {}, + "outputs": [], + "source": [ + "ndof_pto = 2\n", + "pto = wot.pto.PTO(ndof=ndof_pto, \n", + " kinematics=np.eye(ndof_pto),\n", + " controller=my_controller, \n", + " impedance=None, #TODO\n", + " loss=power_loss, \n", + " names=Zi.radiating_dof.values,\n", + " )" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# WEC" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constaints\n", + "Maybe constrain the mean power from each flap to be negative?" + ] + }, + { + "cell_type": "code", + "execution_count": 387, + "metadata": {}, + "outputs": [], + "source": [ + "nsubsteps=2\n", + "\n", + "def mean_power_pto(wec, x_wec, x_opt, waves):\n", + " power_mech = (\n", + " pto.velocity(wec, x_wec, x_opt, waves, nsubsteps) *\n", + " pto.force(wec, x_wec, x_opt, waves, nsubsteps)\n", + " )\n", + " return -1*np.mean(power_mech, axis=1)\n", + "\n", + "max_pos = 30*np.pi/180\n", + "def pto_pos(wec, x_wec, x_opt, waves):\n", + " abs_pos = np.abs(pto.position(wec, x_wec, x_opt, waves, nsubsteps).flatten())\n", + " return max_pos - abs_pos\n", + "\n", + "contraints = [\n", + " # {'type': 'ineq', 'fun': mean_power_pto,},\n", + " # {'type': 'ineq', 'fun': pto_pos,},\n", + " ]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create object" + ] + }, + { + "cell_type": "code", + "execution_count": 388, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:wecopttool.core:Real part of impedance for 0 has negative or close to zero terms. Shifting up by 0.00\n", + "WARNING:wecopttool.core:Real part of impedance for 1 has negative or close to zero terms. Shifting up by 0.00\n" + ] + } + ], + "source": [ + "# wec = wot.WEC.from_bem(bem_data=bem_data,\n", + "# inertia_matrix=inertia,\n", + "# hydrostatic_stiffness=stiffness,\n", + "# f_add={'PTO': pto.force_on_wec},\n", + "# # constraints=[{'type': 'ineq', 'fun': power_pto,},], #TODO\n", + "# )\n", + "\n", + "wec = wot.WEC.from_impedance(freqs=hydro.omega.values/np.pi/2,\n", + " impedance=Zi.values,\n", + " exc_coeff=hydro['Froude_Krylov_force'] + hydro['diffraction_force'],\n", + " hydrostatic_stiffness=hydro['hydrostatic_stiffness'],\n", + " f_add={'PTO': pto.force_on_wec},\n", + " constraints=contraints,\n", + " dof_names=Zi.radiating_dof.values,\n", + " )" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Waves" + ] + }, + { + "cell_type": "code", + "execution_count": 389, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 389, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "amplitude = 0.05/2\n", + "wavefreq = 1/4\n", + "phase = 0\n", + "wavedir = 0\n", + "\n", + "# waves = wot.waves.regular_wave(f1, nfreq, wavefreq, amplitude, phase, wavedir)\n", + "\n", + "spectrum = lambda f: wot.waves.jonswap_spectrum(freq=f, fp=wavefreq, hs=2*amplitude)\n", + "efth = wot.waves.omnidirectional_spectrum(f1, nfreq, spectrum, \"JONSWAP\")\n", + "waves = wot.waves.long_crested_wave(efth)\n", + "efth.plot(marker='.')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Solve" + ] + }, + { + "cell_type": "code", + "execution_count": 390, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:wecopttool.core:Solving pseudo-spectral control problem.\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.80e+00, 8.31e-01, -1.77e+01]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [2.20e+00, 1.70e-01, 5.19e+00]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.09e+00, 1.01e-01, 5.05e-01]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [9.06e-01, 7.85e-02, 5.66e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [8.45e-01, 5.58e-02, 1.01e-03]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [8.42e-01, 5.20e-02, -3.05e-04]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [8.45e-01, 5.21e-02, -3.29e-04]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [8.58e-01, 5.23e-02, -5.36e-04]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [9.19e-01, 5.33e-02, -1.55e-03]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.05e+00, 5.45e-02, -2.79e-03]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.06e+00, 5.39e-02, -1.92e-03]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.06e+00, 5.38e-02, -1.95e-03]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.06e+00, 5.32e-02, -1.99e-03]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.06e+00, 6.42e-02, -2.16e-03]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.04e+00, 1.36e-01, -2.97e-03]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [9.33e-01, 4.48e-01, -6.68e-03]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [8.24e-01, 1.11e+00, -1.53e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [7.72e-01, 9.10e-01, -1.02e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.36e+00, 3.91e+00, -7.99e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [9.09e-01, 4.76e-01, -6.63e-03]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.64e+00, 3.77e+00, -8.48e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.18e+00, 1.78e+00, 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"INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.24e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.25e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.26e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.27e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.28e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.29e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.30e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.31e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.33e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.34e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.35e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.36e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.37e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.38e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.39e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.40e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.28e+00, 5.41e+00, -1.94e-02]\n", + "INFO:wecopttool.core:[max(x_wec), max(x_opt), obj_fun(x)]: [1.29e+00, 4.05e+00, -1.92e-02]\n", + "WARNING:wecopttool.core:Iteration limit reached (Exit mode 9)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration limit reached (Exit mode 9)\n", + " Current function value: -0.01921888326273043\n", + " Iterations: 100\n", + " Function evaluations: 232\n", + " Gradient evaluations: 100\n", + "Optimal average mechanical power: -1.92 W\n" + ] + } + ], + "source": [ + "# nstate_opt = 2*2*nfreq # two flaps, two components per freq\n", + "# nstate_opt = 2*2*2 # 2x2 mimo with PI controller\n", + "nstate_opt = 2*2 # 2x2 mimo, symmetric and rot90 symmetric with PI controller\n", + "\n", + "results = wec.solve(\n", + " waves,\n", + " obj_fun=pto.average_power,\n", + " nstate_opt=nstate_opt,\n", + " optim_options={'maxiter': 100},\n", + " x_wec_0=np.ones(wec.nstate_wec)*0.1,\n", + " x_opt_0=np.ones(nstate_opt)*0.1,\n", + " scale_x_wec=1e0,\n", + " scale_x_opt=1e-2,\n", + " scale_obj=1e-2,\n", + " )\n", + "\n", + "opt_mechanical_average_power = results.fun\n", + "print(f'Optimal average mechanical power: {opt_mechanical_average_power:.2f} W')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gains" + ] + }, + { + "cell_type": "code", + "execution_count": 391, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Kp\n", + "[[-405.01842309 404.32088638]\n", + " [ 404.32088638 -405.01842309]]\n", + "Kp\n", + "[[ 3.22396867 -15.30281623]\n", + " [-15.30281623 3.22396867]]\n" + ] + } + ], + "source": [ + "Kp = results['x'][-4:][0]*np.eye(2) + results['x'][-4:][1]*np.rot90(np.eye(2))\n", + "print(f'Kp\\n{Kp}')\n", + "\n", + "Ki = results['x'][-4:][2]*np.eye(2) + results['x'][-4:][3]*np.rot90(np.eye(2))\n", + "print(f'Kp\\n{Ki}')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Results" + ] + }, + { + "cell_type": "code", + "execution_count": 392, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:         (influenced_dof: 2, time: 120, type: 3, wave_direction: 1)\n",
+       "Coordinates:\n",
+       "  * influenced_dof  (influenced_dof) <U3 'bow' 'aft'\n",
+       "  * time            (time) float64 0.0 0.6667 1.333 2.0 ... 78.0 78.67 79.33\n",
+       "    omega           float64 4.712\n",
+       "    freq            float64 0.75\n",
+       "    period          float64 1.333\n",
+       "  * type            (type) object 'intrinsic_impedance' 'excitation' 'PTO'\n",
+       "  * wave_direction  (wave_direction) float64 0.0\n",
+       "Data variables:\n",
+       "    pos             (influenced_dof, time) float64 -1.384 -0.9342 ... -0.9117\n",
+       "    vel             (influenced_dof, time) float64 -0.02326 1.294 ... -1.3\n",
+       "    acc             (influenced_dof, time) float64 2.231 1.491 ... -0.1987 1.463\n",
+       "    force           (influenced_dof, type, time) float64 -50.9 -11.65 ... 1.805\n",
+       "    wave_elev       (wave_direction, time) float64 0.0002033 ... -0.008302\n",
+       "Attributes:\n",
+       "    time_created_utc:  2023-03-28 03:44:09.453199
" + ], + "text/plain": [ + "\n", + "Dimensions: (influenced_dof: 2, time: 120, type: 3, wave_direction: 1)\n", + "Coordinates:\n", + " * influenced_dof (influenced_dof) " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(nrows=5,\n", + " sharex=True,\n", + " figsize=(8,12))\n", + "\n", + "# Wave elevation\n", + "wec_tdom.wave_elev.squeeze().plot(ax=ax[0], c='k')\n", + "ax[0].set_ylabel('Wave elev. [m]')\n", + "ax1 = ax[0].twinx()\n", + "wec_tdom.pos.plot(hue='influenced_dof', ax=ax1, add_legend=False)\n", + "ax1.set_ylabel('Flap pos. [rad]')\n", + "\n", + "# Excitation\n", + "wec_tdom.force.sel(type='excitation').plot(hue='influenced_dof', ax=ax[1], add_legend=False)\n", + "ax[1].set_ylabel('Excitation torque [Nm]')\n", + "\n", + "# Flap velocity\n", + "wec_tdom.vel.plot(ax=ax[2], hue='influenced_dof', add_legend=False)\n", + "ax[2].set_ylabel('Flap vel. [rad/s]')\n", + "\n", + "# Torque\n", + "wec_tdom.force.sel(type='PTO').plot(ax=ax[3], hue='influenced_dof', add_legend=False)\n", + "ax[3].set_ylabel('PTO torque [Nm]')\n", + "\n", + "# Power\n", + "pto_tdom.power.plot(ax=ax[4], hue='dof', add_legend=False)\n", + "\n", + "p_mean = pto_tdom.power.mean('time')\n", + "p_mean\n", + "\n", + "leg = []\n", + "for name, pow in zip(p_mean.dof.values, p_mean.values):\n", + " leg.append(f'{name}: ' + '$\\\\bar{P}=$' + f'{pow:.0f}W')\n", + "ax[-1].legend(leg, ncol=2, loc=1)\n", + "\n", + "for axi in ax:\n", + " axi.label_outer()\n", + " axi.set_title('')\n", + " axi.autoscale(enable=True, axis='x', tight=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "wot_dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wecopttool/core.py b/wecopttool/core.py index 37d142ab..104863af 100644 --- a/wecopttool/core.py +++ b/wecopttool/core.py @@ -502,6 +502,7 @@ def from_impedance( f_add: Optional[TIForceDict] = None, constraints: Optional[Iterable[Mapping]] = None, min_damping: Optional[float] = _default_min_damping, + dof_names: Optional[Iterable[str]] = None, ) -> TWEC: """Create a WEC object from the intrinsic impedance and excitation coefficients. @@ -544,6 +545,11 @@ def from_impedance( Minimum damping level to ensure a stable system. See :py:func:`wecopttool.check_impedance` for more details. + dof_names + Names of the different degrees of freedom (e.g. + :python:`'Heave'`). + If :python:`None` the names + :python:`['DOF_0', ..., 'DOF_N']` are used. Raises ------ @@ -583,7 +589,8 @@ def from_impedance( # wec wec = WEC(f1, nfreq, forces, constraints, - inertia_in_forces=True, ndof=shape[0]) + inertia_in_forces=True, ndof=shape[0], + dof_names=dof_names) return wec def _resid_fun(self, x_wec, x_opt, waves):