diff --git a/doc/COMSOL_WEST_port_index.png b/doc/COMSOL_WEST_port_index.png new file mode 100644 index 0000000..f4a6ce3 Binary files /dev/null and b/doc/COMSOL_WEST_port_index.png differ diff --git a/doc/automatic_matching_control_loop.png b/doc/automatic_matching_control_loop.png new file mode 100644 index 0000000..05a8b51 Binary files /dev/null and b/doc/automatic_matching_control_loop.png differ diff --git a/doc/chart_manual_matching.ipynb b/doc/chart_manual_matching.ipynb index 3821c30..7d6af4f 100644 --- a/doc/chart_manual_matching.ipynb +++ b/doc/chart_manual_matching.ipynb @@ -114,7 +114,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.7" }, "toc": { "base_numbering": 1, diff --git a/doc/coupling_to_plasma_from_COMSOL.ipynb b/doc/coupling_to_plasma_from_COMSOL.ipynb new file mode 100644 index 0000000..5412e4b --- /dev/null +++ b/doc/coupling_to_plasma_from_COMSOL.ipynb @@ -0,0 +1,274 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plasma Coupling Using COMSOL Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we use a COMSOL front-face coupling calculation provided by ORNL, exported as a standard Touchstone file.\n", + "\n", + "The Touchstone file is first import as a scikit-rf Network, which is then modified to fit the WEST ICRH antenna electrical model requirements." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import skrf as rf\n", + "\n", + "# WEST ICRH Antenna package\n", + "import sys; sys.path.append('..')\n", + "from west_ic_antenna import WestIcrhAntenna" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4-Port Network: 'ORNL_front_face_conventional', 55000000.0-55000000.0 Hz, 1 pts, z0=[50.+0.j 50.+0.j 50.+0.j 50.+0.j]\n" + ] + } + ], + "source": [ + "front_face_conventional = rf.Network(\n", + " '../west_ic_antenna/data/Sparameters/front_faces/COMSOL/ORNL_front_face_conventional.s4p')\n", + "print(front_face_conventional) # 50 Ohm S-param component at a single frequency of 55 MHz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ports have been defined as:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So before to use the S-parameters directly to feed the electrical model, we need to:\n", + "- deembed the ports by 0.3m.\n", + "- renomalize port reference impedance to the front-face coax characteristic impedances. \n", + "- reverse ports 2 and 3." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# creating a 50 Ohm dummy coax line to be removed from the front face \n", + "media_coax = rf.DefinedGammaZ0(frequency=front_face_conventional.frequency) # 50 Ohm TEM media\n", + "extra_line = media_coax.line(d=0.3, unit='m')\n", + "# deembedding all the 4 pourts\n", + "for port_idx in range(4):\n", + " front_face_conventional = rf.connect(front_face_conventional, port_idx, extra_line.inv, 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We expect the port to have a characteristic impedance of about 46.64 ohm, so we renormalize the Network to fit this need:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "front_face_conventional.renormalize(46.64) # done inplace" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And finally, for historical reasons (may change in a near future ;), the S-matrix port ordering should be ajusted:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "front_face_conventional.renumber([1, 2], [2, 1]) # done inplace" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OK, so now we can create the WEST antenna object:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "ant = WestIcrhAntenna(front_face=front_face_conventional,\n", + " frequency=front_face_conventional.frequency) # restrict to single frequ" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's match the antenna for this coupling:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking for individual solutions separately for 1st guess...\n", + "Wrong solution (out of range capacitor) ! Re-doing...\n", + "False solution #1: [150. 150.]\n", + "True solution #1: [52.57986227 45.88696785]\n", + "True solution #1: [52.30894839 46.0700872 ]\n", + "Searching for the active match point solution...\n", + "Reducing search range to +/- 5pF around individual solutions\n", + "True solution #1: [53.67807308 46.12207788 53.62800637 46.30935881]\n" + ] + } + ], + "source": [ + "Cs = ant.match_both_sides(f_match=55e6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The coupling resistance of the antenna for this coupling in a nominal dipole excitation is:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.70081638, 0.6878438 ])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power = [1, 1]\n", + "phase = [0, np.pi]\n", + "\n", + "# Coupling resistance\n", + "ant.Rc(power, phase)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The voltage and currents are:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[22.16126386, 23.75535708, 20.56902439, 25.21223565]])" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power = [1, 1] # MW, to adjust to fit with experiment\n", + "phase = [0, np.pi] # rad\n", + "\n", + "abs(ant.voltages(power, phase)) # results in kV" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.70248687, 0.75556107, 0.65298786, 0.80405425]])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abs(ant.currents(power, phase)) # results in kA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/digital_twin.ipynb b/doc/digital_twin.ipynb index 761e0d9..f5c9a07 100644 --- a/doc/digital_twin.ipynb +++ b/doc/digital_twin.ipynb @@ -5,12 +5,14 @@ "metadata": {}, "source": [ "# WEST Digital Twin Antenna\n", - "This notebook can be used in live in the control room in order to understand the RF behaviour of a WEST ICRH antenna, thanks to the Jupyter notebook interactive tools." + "This notebook can be used in live in the control room in order to understand the RF behaviour of a WEST ICRH antenna, thanks to the Jupyter notebook interactive tools.\n", + "\n", + "This notebook generally works better within the JupyterLab environement." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -19,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -29,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -39,52 +41,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { + "scrolled": true, "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d7c6c703cbaa4e46b38156a70417dea2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "DigitalTwin(children=(Tab(children=(HBox(children=(VBox(children=(Box(children=(Label(value='C1: '), FloatSlid…" - ] - }, - "execution_count": 6, - 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", - "text/html": [ - "\n", - "
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In particular, they have been designed to operate with an impedance at the T-junction $Z_T$ close to 3 Ohm. An impedance transformer connects the T-junction to the feeding transmission line (30 Ohm line). Hence, matching the antenna is similar to having a 30 Ohm load connected to the feeding transmission line, such as no power is reflected (VSWR$\\to 1$), which should be equivalent of having an impedance of roughtly 3 Ohm at the T-junction.\n", "\n", - "However, due to real-life design and manufacturing constraint, the optimal impedance at the T-junction is not necessarely 3 Ohm, but can be slightly different in both real and imaginary parts. \n", + "However, due to real-life design and manufacturing constraints, the optimal impedance at the T-junction is not necessarely 3 Ohm, but can be slightly different in both real and imaginary parts. \n", "\n", - "So let's evaluate the impact of the impedance at the T-junction to the 30 Ohm feeder line (the one which really matter for the generator point-of-view).\n", + "So let's evaluate the impact of the realistic geometries (simulated from full-wave tools) on the impedance at the T-junction to the 30 Ohm feeder line (the one which really matter for the generator point-of-view).\n", "\n", "For that, let's take the impedance transformer/vacuum window/service stub network assembly of an antenna:" ] @@ -520,7 +518,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.7" }, "toc": { "base_numbering": 1, diff --git a/doc/tutorial_matching_automatic.ipynb b/doc/tutorial_matching_automatic.ipynb index 77719f7..d81d2c7 100644 --- a/doc/tutorial_matching_automatic.ipynb +++ b/doc/tutorial_matching_automatic.ipynb @@ -738,7 +738,7 @@ "metadata": {}, "outputs": [], "source": [ - "C_opt_2 = antenna.matching_both_sides_iterative(f_match=55e6, power=power, phase=phase)" + "C_opt_2 = antenna.match_both_sides_iterative(f_match=55e6, power=power, phase=phase, Cs=[50, 50, 50, 50])" ] }, { @@ -896,7 +896,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/doc/tutorial_matching_manual.ipynb b/doc/tutorial_matching_manual.ipynb index 97f39aa..933dd80 100644 --- a/doc/tutorial_matching_manual.ipynb +++ b/doc/tutorial_matching_manual.ipynb @@ -385,7 +385,7 @@ "metadata": {}, "outputs": [], "source": [ - "np.array(C_match_plasma) - np.array(C_vacuum)" + "np.array(C_match_plasma) - np.array(C_opt_vacuum_dipole)" ] }, { @@ -447,7 +447,7 @@ "metadata": {}, "outputs": [], "source": [ - "diff_C = np.array(C_matchs) - np.array(C_vacuum)" + "diff_C = np.array(C_matchs) - np.array(C_opt_vacuum_dipole)" ] }, { @@ -466,21 +466,18 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "Cs=list(np.array(C_opt_vacuum_dipole) + np.array([+3, -3, +3, -3]))" + "# Automatic Matching\n", + "Here is just a glimpse of the automatic matching capabilities.\n" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "np.argmin(np.abs(ant.frequency.f - 55e6))" + "Let's assume we start from a non-optimal situation, that is we enlarge the capacitance differences from a vacuum matching from a rather arbitrary value: " ] }, { @@ -489,9 +486,14 @@ "metadata": {}, "outputs": [], "source": [ - "C_left, C_right, err = ant.capacitor_predictor(powers, phases, Cs=Cs)\n", - "Cs=[*C_left[500], *C_right[500]]\n", - "print(Cs)" + "Cs=list(np.array(C_opt_vacuum_dipole) + np.array([+3, -3, +3, -3]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, using the capacitor predictor, we calculate the capacitance set to reach:" ] }, { @@ -500,6 +502,13 @@ "metadata": {}, "outputs": [], "source": [ + "# Evaluate this cell a few times to see the convergence to the optimal matching\n", + "C_left, C_right, err = ant.capacitor_predictor(powers, phases, Cs=Cs)\n", + "idx_f = np.argmin(np.abs(ant.frequency.f - 55e6))\n", + "Cs=[*C_left[idx_f], *C_right[idx_f]]\n", + "\n", + "print(Cs)\n", + "\n", "s_act = ant.s_act(powers, phases , Cs=Cs)\n", "\n", "fig, ax = plt.subplots()\n", @@ -561,7 +570,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.7" }, "toc": { "base_numbering": 1, diff --git a/west_ic_antenna/data/Sparameters/front_faces/COMSOL/ORNL_front_face_conventional.s4p b/west_ic_antenna/data/Sparameters/front_faces/COMSOL/ORNL_front_face_conventional.s4p new file mode 100644 index 0000000..fed11bc --- /dev/null +++ b/west_ic_antenna/data/Sparameters/front_faces/COMSOL/ORNL_front_face_conventional.s4p @@ -0,0 +1,5 @@ +# Hz S MA R 50 +5.5E7 0.9833673750492276 80.96377443955839 0.008159649655058979 7.738772614443071 0.0145413437349296 16.766538738668693 0.0010505712330604862 -169.75560162791115 + 0.005398399959278055 22.968885177373394 0.9848074824065548 80.65809524512109 0.002317451028156058 3.2454593187084524 0.013899534074545427 14.553096258345233 + 0.015466057366109193 15.352573092351177 0.0017852444452037635 2.802734655640837 0.9830347719457835 80.74299676938419 0.00812386410086659 7.621156795969611 + 0.0010813303292888372 -61.44581050964589 0.014724345533904457 13.318047129975318 0.005359073489960678 22.934149123418244 0.9850374253641254 80.8905612330192 diff --git a/west_ic_antenna/data/Sparameters/front_faces/COMSOL/ORNL_front_face_plasma_inside.s4p b/west_ic_antenna/data/Sparameters/front_faces/COMSOL/ORNL_front_face_plasma_inside.s4p new file mode 100644 index 0000000..43d6755 --- /dev/null +++ b/west_ic_antenna/data/Sparameters/front_faces/COMSOL/ORNL_front_face_plasma_inside.s4p @@ -0,0 +1,5 @@ +# Hz S MA R 50 +5.5E7 0.9531014698199719 74.68128260423282 0.011727825824109023 22.140672391691247 0.0042213470494665405 11.518713731671811 0.002189631421892682 42.58156998869071 + 9.251508660246946E-4 -70.21624695384136 0.958437677141582 73.97406838963975 1.7232083904810094E-4 -60.95155708607013 0.005781121306569129 11.04470231400275 + 0.003550071050919271 -5.440992958402874 0.0031673180639685814 30.852889750516837 0.9523110682604861 74.3856493165878 0.011744626700427849 22.11148013138784 + 1.7657833450556378E-4 -67.23316376248191 0.005247609440270916 0.5440706526584136 9.356608093953542E-4 -69.87439488938504 0.9590992784229622 74.23536526952307 diff --git a/west_ic_antenna/digital_twin.py b/west_ic_antenna/digital_twin.py index 0ac503b..a25c51d 100644 --- a/west_ic_antenna/digital_twin.py +++ b/west_ic_antenna/digital_twin.py @@ -31,13 +31,13 @@ def __init__(self): #### defining widgets # capacitor widgets - C1 = widgets.FloatSlider(value=49.31, min=30.0, max=120.0, step=1e-4, + C1 = widgets.FloatSlider(value=50.62, min=30.0, max=120.0, step=1e-4, continuous_update=False) - C2 = widgets.FloatSlider(value=47.38, min=30.0, max=120.0, step=1e-4, + C2 = widgets.FloatSlider(value=48.69, min=30.0, max=120.0, step=1e-4, continuous_update=False) - C3 = widgets.FloatSlider(value=49.11, min=30.0, max=120.0, step=1e-4, + C3 = widgets.FloatSlider(value=50.15, min=30.0, max=120.0, step=1e-4, continuous_update=False) - C4 = widgets.FloatSlider(value=47.56, min=30.0, max=120.0, step=1e-4, + C4 = widgets.FloatSlider(value=48.86, min=30.0, max=120.0, step=1e-4, continuous_update=False) def capa_plus(clicked_button):