diff --git a/Total Coupling Variation Plot.ipynb b/Total Coupling Variation Plot.ipynb new file mode 100644 index 0000000..21504b3 --- /dev/null +++ b/Total Coupling Variation Plot.ipynb @@ -0,0 +1,136 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 35, + "id": "d310d005-ed50-4fcb-a54a-87ef0de71553", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "import matplotlib.cm\n", + "import numpy as np\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "9f43c5e5-031c-40c5-b7b0-61ce0ff679e4", + "metadata": {}, + "outputs": [], + "source": [ + "# https://iamkate.com/data/12-bit-rainbow/\n", + "rainbow_12bit = ['#817', '#a35', '#c66', '#e94', '#ed0', '#9d5', '#4d8', '#2cb', '#0bc', '#09c', '#36b', '#639']" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5e46f8e1-f3ef-492c-a3cb-27a837ced3b2", + "metadata": {}, + "outputs": [], + "source": [ + "tcv_data = np.array([\n", + "[3\t,0\t,None\t,4.10],\n", + "[3\t,1\t,None\t,2.37],\n", + "[3\t,4\t,None\t,1.01],\n", + "[3\t,4\t,None ,None],\n", + "[5\t,0\t,0.89\t,2.60],\n", + "[5\t,1\t,0.59\t,1.34],\n", + "[5\t,3\t,0.64\t,1.09],\n", + "[5\t,7\t,0.48\t,0.65],\n", + "[10\t,0\t,0.73\t,1.74],\n", + "[10\t,1\t,0.48\t,0.64],\n", + "[10\t,3\t,0.46\t,0.87],\n", + "[10\t,7\t,0.31\t,0.58],\n", + "[25\t,0\t,0.66\t,2.18],\n", + "[25\t,1\t,0.60\t,1.45],\n", + "[25\t,3\t,0.61\t,1.17],\n", + "[25\t,13\t,0.60\t,1.60],\n", + "])\n", + "tcv_1mm = tcv_data[:, (0,1,2)]\n", + "tcv_1mm = tcv_1mm[tcv_1mm[:,2] != None]\n", + "tcv_4mm = tcv_data[:, (0,1,3)]" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "eca7fb6a-0ca5-48f6-8f22-493242e11ce5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(4, 3.5))\n", + "n_colors = {}\n", + "handles = []\n", + "for r, marker, tcv in [(1, 's', tcv_1mm), (4, 'o', tcv_4mm)]:\n", + " for n in sorted(set(tcv[:,0])):\n", + " sel = tcv[tcv[:,0] == n]\n", + " if n not in n_colors:\n", + " n_colors[n] = matplotlib.cm.tab10(len(n_colors))\n", + " h, = ax.plot(sel[:,1], sel[:,2], label=f'$n={n}, r={r}$', color=n_colors[n], marker=marker, fillstyle='none')\n", + " handles.append(h)\n", + "ax.set_ylabel(r'$V_{ripple,rms} [\\%]$')\n", + "ax.set_xlabel(r'$k$')\n", + "ax.set_xticks([0, 1, 3, 7, 13])\n", + "# hackety hack! https://stackoverflow.com/questions/44575560/centering-matplotlib-legend-entries-within-incomplete-unfilled-rows\n", + "handles.insert(3, ax.plot([],[],color=(0,0,0,0), label=\" \")[0])\n", + "ax.legend(handles=handles, loc='upper right', ncol=2, labelspacing=0.3, columnspacing=0.5)\n", + "ax.grid()\n", + "\n", + "# https://matplotlib.org/stable/gallery/text_labels_and_annotations/annotation_demo.html\n", + "ax.annotate(\n", + " 'Conventional Single-layer Planar Inductor',\n", + " xy=(0, 1), xycoords=('data', 'axes fraction'),\n", + " xytext=(30, 40), textcoords='offset points',\n", + " arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"angle,angleA=0,angleB=90\"))\n", + "ax.annotate(\n", + " 'Conventional Two-layer Planar Inductor',\n", + " xy=(1, 1), xycoords=('data', 'axes fraction'),\n", + " xytext=(30, 25), textcoords='offset points',\n", + " arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"angle,angleA=0,angleB=90\"))\n", + "ax.annotate(\n", + " r'$-$ Our contribution $\\longrightarrow$',\n", + " xy=(2, 1.05), xycoords=('data', 'axes fraction'),)\n", + "ax.axvspan(-1000, 1, color='black', alpha=0.1)\n", + "ax.set_xlim([-0.3, 13.3])\n", + "fig.subplots_adjust(top=0.7)\n", + "\n", + "fig.savefig('paper/figures/k_ripple_plot.pdf')" + ] + } + ], + "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": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/paper/figures/k_ripple_plot.pdf b/paper/figures/k_ripple_plot.pdf new file mode 100644 index 0000000..f07a012 Binary files /dev/null and b/paper/figures/k_ripple_plot.pdf differ diff --git a/paper/paper.tex b/paper/paper.tex index 9035e4f..02c3a18 100644 --- a/paper/paper.tex +++ b/paper/paper.tex @@ -597,22 +597,22 @@ performance parameters. $1$& $0$& $0.03$& $-86.2$& $0.0076$& $-86.8$& $0.038$& $-42.1$& $0.008$& $-77.5$& $0.054$& $457.585$&$0.0142$\\ $1$& $3$& $0.03$& $-93.1$& $0.0095$& $-49.9$& $0.039$& $-43.6$& $0.008$& $-78.8$& $0.056$& $465.07$& $0.0143$\\ $1$& $4$& $0.03$& $-103.4$& $0.0108$& $-38.6$& $0.040$& $-47.5$& $0.008$& $-87.5$& $0.059$& $460.08$& $0.015$\\ -$1$& $5$& $0.03$& $-89.7$& $0.0123$& $-35.3$& $0.041$& $-34.1$& $0.009$& $-84.4$& $0.055$& $460.08$& $0.0166$\\ +$1$& $5$& $0.03$& $-89.7$& $0.0123$& $-35.3$& $0.041$& $-34.1$& $0.009$& $-84.4$& $0.055$& $460.08$& $0.0166$\\\hline $2$& $0$& $0.16$& $10.0$& $0.0252$& $-26.7$& $0.126$& $-14.3$& $0.026$& $-22.7$& $0.144$& $266.24$& $0.0319$\\ $2$& $1$& $0.12$& $-28.4$& $0.0253$& $-12.1$& $0.127$& $-17.3$& $0.024$& $-18.3$& $0.149$& $245.51$& $0.0284$\\ $2$& $3$& $0.12$& $-31.0$& $0.0270$& $-7.9$& $0.128$& $-18.8$& $0.025$& $-16.4$& $0.152$& $240.52$& $0.0291$\\ -$2$& $5$& $0.12$& $-26.7$& $0.0299$& $-0.2$& $0.130$& $-13.1$& $0.027$& $-11.1$& $0.147$& $225.5$& $0.03$\\ +$2$& $5$& $0.12$& $-26.7$& $0.0299$& $-0.2$& $0.130$& $-13.1$& $0.027$& $-11.1$& $0.147$& $225.5$& $0.03$\\\hline $3$& $0$& $0.26$& $-19.6$& $0.0755$& $-5.0$& $0.285$& $-9.1$& $0.077$& $-2.9$& $0.311$& $192.95$& $0.0792$\\ $3$& $1$& $0.26$& $-10.0$& $0.0454$& $-1.6$& $0.262$& $-9.5$& $0.044$& $-4.8$& $0.287$& $145.71$& $0.0461$\\ -$3$& $4$& $0.26$& $-9.6$& $0.0479$& $5.0$& $0.265$& $-7.9$& $0.046$& $1.1$& $0.286$& $145.71$& $0.0455$\\ +$3$& $4$& $0.26$& $-9.6$& $0.0479$& $5.0$& $0.265$& $-7.9$& $0.046$& $1.1$& $0.286$& $145.71$& $0.0455$\\\hline $5$& $0$& $0.73$& $-9.6$& $0.2357$& $-0.4$& $0.760$& $-5.3$& $0.240$& $1.4$& $0.8$& $125.415$&$0.2366$\\ $5$& $1$& $0.73$& $4.5$& $0.0755$& $-3.1$& $0.670$& $-3.4$& $0.074$& $-5.1$& $0.693$& $61.345$& $0.0778$\\ $5$& $3$& $0.73$& $4.3$& $0.0763$& $4.7$& $0.671$& $-3.4$& $0.074$& $1.8$& $0.694$& $70.285$& $0.0727$\\ -$5$& $7$& $0.73$& $4.4$& $0.0802$& $16.2$& $0.675$& $-2.8$& $0.077$& $12.7$& $0.694$& $68.05$& $0.0672$\\ +$5$& $7$& $0.73$& $4.4$& $0.0802$& $16.2$& $0.675$& $-2.8$& $0.077$& $12.7$& $0.694$& $68.05$& $0.0672$\\\hline $10$& $0$& $2.90$& $-2.4$& $0.7539$& $-2.3$& $2.900$& $-2.4$& $0.761$& $-1.4$& $2.97$& $62.835$& $0.7713$\\ $10$& $1$& $2.90$& $6.3$& $0.2513$& $7.6$& $2.700$& $-0.7$& $0.250$& $7.1$& $2.718$& $24.076$& $0.2322$\\ $10$& $3$& $2.90$& $6.4$& $0.2520$& $10.5$& $2.700$& $-0.5$& $0.250$& $9.8$& $2.714$& $28.571$& $0.2255$\\ -$10$& $7$& $2.90$& $6.4$& $0.2554$& $16.9$& $2.700$& $-0.5$& $0.252$& $15.8$& $2.713$& $28.072$& $0.2122$\\ +$10$& $7$& $2.90$& $6.4$& $0.2554$& $16.9$& $2.700$& $-0.5$& $0.252$& $15.8$& $2.713$& $28.072$& $0.2122$\\\hline $25$& $0$& $18.15$& $1.1$& $3.7693$& $-3.9$& $18.000$& $0.3$& $3.800$& $-3.0$& $17.955$& $24.84$& $3.9156$\\ $25$& $1$& $18.15$& $6.7$& $1.8843$& $9.7$& $16.900$& $-0.2$& $1.900$& $10.4$& $16.938$& $8.84$& $1.7024$\\ $25$& $3$& $18.15$& $6.8$& $1.8851$& $13.2$& N/A& N/A& N/A& N/A& $16.919$& $8.595$& $1.636$\\ @@ -621,7 +621,8 @@ $25$& $37$& $18.15$& $6.0$& $2.0197$& $15.9$& $17.100$& $0.2$& $2.000$& \end{tabular} \caption{Inductor sample design parameters and measured characteristics. All inductors have outer diameter - \qty{35}{\milli\meter} and inner diameter \qty{15}{\milli\meter}.} + \qty{35}{\milli\meter} and inner diameter \qty{15}{\milli\meter}. The missing values in the simulation results + columns result from the solver failing to converge.} \end{table*} \subsection{Inductance and Frequency Behavior of Larger Coils} @@ -740,6 +741,14 @@ for $k=7$ inversions. \todo{concrete coupling factor measurements} +\begin{figure} + \begin{center} + \includegraphics[width=.85\linewidth]{figures/k_ripple_plot.pdf} + \end{center} + \caption{} + \label{fig_test_schematic} +\end{figure} + \begin{figure} \begin{center} \includegraphics[width=.6\linewidth]{figures/field_plot_3d_n5_k0.pdf}