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{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Chapter 8: Variable-Reluctance Machines and Stepping Motors"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 8.1, Page number: 411"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"%pylab inline\n",
"from numpy import *\n",
"from math import *\n",
"\n",
"#Variable declaration:\n",
"R=0.038 #m\n",
"a=b=pi/3 #rad\n",
"g=2.54*10**-4 #m\n",
"D=0.13 #m\n",
"N=100 #turns in both poles\n",
"uo=4*pi*10**-7 #permeability of free space()\n",
"i1=5 #coil current (A)\n",
"\n",
"\n",
"#Calculation:\n",
"Lm=N**2*uo*a*R*D/(2*g)\n",
"#x=symbols('x')\n",
"subplot(2,1,1)\n",
"x=linspace(-180,-120,100)\n",
"L=-(Lm/60)*x-2*Lm\n",
"plot(x,L,'b')\n",
"#grid()\n",
"\n",
"x=linspace(-60,0,100)\n",
"L=(Lm/60)*x+Lm\n",
"plot(x,L,'b')\n",
"grid()\n",
"\n",
"x=linspace(0,60,100)\n",
"L=-(Lm/60)*x+Lm\n",
"plot(x,L,'b')\n",
"grid()\n",
"\n",
"\n",
"x=linspace(120,180,100)\n",
"L=(Lm/60)*x-2*Lm\n",
"plot(x,L)\n",
"annotate('Lm=0.128 H',xy=(-150,0.10))\n",
"annotate('Lmax',xy=(0,Lm+0.005))\n",
"ylabel('L11(theta)')\n",
"xlabel('theta')\n",
"grid()\n",
"\n",
"#part(b)\n",
"subplot(2,1,2)\n",
"x1=linspace(-180,-120,100)\n",
"x2=linspace(-150,-90,100)\n",
"i1=5\n",
"i2=4\n",
"Tm1=(Lm/(2*pi/3))*i1**2\n",
"Tm2=(Lm/(2*pi/3))*i2**2\n",
"dll=np.ones(100)\n",
"plot(x1,-Tm1*np.array(dll),'g')\n",
"plot(x2,Tm2*np.array(dll),'b--')\n",
"\n",
"x1=linspace(-60,0,100)\n",
"x2=linspace(-90,-30,100)\n",
"Tm1=(Lm/(2*pi/3))*i1**2\n",
"Tm2=(Lm/(2*pi/3))*i2**2\n",
"dll=np.ones(100)\n",
"plot(x1,Tm1*np.array(dll),'g')\n",
"plot(x2,-Tm2*np.array(dll),'b--')\n",
"\n",
"x1=linspace(0,60,100)\n",
"x2=linspace(30,90,100)\n",
"Tm1=(Lm/(2*pi/3))*i1**2\n",
"Tm2=(Lm/(2*pi/3))*i2**2\n",
"dll=np.ones(100)\n",
"plot(x1,-Tm1*np.array(dll),'g')\n",
"plot(x2,Tm2*np.array(dll),'b--')\n",
"\n",
"x1=linspace(120,180,100)\n",
"x2=linspace(90,150,100)\n",
"Tm1=(Lm/(2*pi/3))*i1**2\n",
"Tm2=(Lm/(2*pi/3))*i2**2\n",
"dll=np.ones(100)\n",
"plot(x1,Tm1*np.array(dll),'g')\n",
"plot(x2,-Tm2*np.array(dll),'b--')\n",
"grid()\n",
"ylim(-3,3)\n",
"annotate('___ i1=I1, i2=0', xy=(110,2.6))\n",
"annotate('---- i1=0, i2=I2', xy=(110,2.2))\n",
"ylabel('Torque [N.m]')\n",
"xlabel('thetam [degrees]')\n",
"\n",
"#Results:\n",
"print \"Lm =\",Lm,\"H\"\n",
"print \"(c)The peak torque =\",round(Tm1,2),\"N.m\"\n",
"print \"\\t(i) The net torque, (at thetam=0) =\", 0, \"N.m\"\n",
"print \"\\t(ii) The net torque, (at thetam=45 deg.) =\", 0, \"N.m\"\n",
"print \"\\t(iii)The net torque, (at thetam=75 deg) =\", round(Tm1,2), \"N.m\"\n",
"show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n",
"Lm ="
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0.127968099059 H\n",
"(c)The peak torque = 1.53 N.m\n",
"\t(i) The net torque, (at thetam=0) = 0 N.m\n",
"\t(ii) The net torque, (at thetam=45 deg.) = 0 N.m\n",
"\t(iii)The net torque, (at thetam=75 deg) = 1.53 N.m\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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HDh3Qs2dPkxlKm700dPkq+DoKkTA9e8YMYbq2JSzJOXKkcOX5kSNAixa25ywNKc6ntZSQ\nEShdzosXgZ9+As6csU8mezDbcIwaNarYHatVq1bsNu7u7khLSzMup6WlFfjgLI41+4pVVr2k5U8/\nBby8dGjWDOjXz7r9AeGaiOIe9/T0xH/+8x/jG+/HH3+ETqcr8EYs7vk2bNhgXM47HzqdDgsXLjQ+\nR0REBCIiIgrsr9PpsG/fPgDAokWLCpy/2NhY9OnTB0uXLi2wfeHn//HHH4vkuX//vqjnv/D5EvN4\nlixv2wb8/bfuabE5y/ZPSkqy6PiffRaOCROAzz/XQaVSfhnwZ3k5KSnJ6v2//TYcY8YAp07ZL5/O\n0WXVMzMzsWzZMpw9exY5T+elqlQq/FDCVKPHjx/Dx8cHhw8fRu3atdGiRQssW7YMwcHBRbaNiopC\n1apVjSXVLd3XHrWqijN9OvDnn8LtG9mzIycH8PcXvm127Cj+8XNzhdLZM2cCr78u/vGZfB0/DnTr\nJky2sMfkG3PsXqvqzTffxN27d/Hbb78hPDwc6enpFl0VbEtlXHP7Sm3cOECnAxISpE7CHOmHH4Rp\n2faaW1+unHAF+qRJ9rtmiMlPXvfn1KmObTREUdIVgn5+fkREFBAQQEREOTk5FBoaatNVh2KxIL7o\nvvmGqF076/aJiYmxSxaxcc6iMjOJ6tUjiouzfl9rchoMRK++SvTdd9Y/j62U8LorISORdTn37CHy\n9ibKzrZfHnNs/ews8RvH80+bwsqVK+PUqVO4ffu2pDOcpPbOO0BKCvB0WICVcQsXAq1bAyEh9n2e\nvGuGoqKAhw/t+1xMerm5/9zHpbxdb+BtHyWOcSxfvhx9+/bFsWPH8Pbbb+PJkyeYPn06Rsig3q+j\nxzjybNok9EfHx/N9FcqyGzcAX1/g6FFhZp0j9O4NBAdzWf+ybvVqoXLA4cOWleQXm62fnSU2HJcu\nXcJLhSYXm1onBakaDiLglVeA0aMBK0pKMYUZPVq4w9+SJY57zvPngdBQYRJGzZqOe17mOI8fCxd8\nrl4tfJuVgt0Hx3v16mXRumeJSiUUqLP0vgqFpz3KFef8x6VLwNq1wr3CS6s0Ob28gDfeEL7ROooS\nXnclZAQsy/nNN0KhS6kaDTGY7V07c+YMTp8+jbt372LLli0gIqhUKjx48AAZGRmOzChLr74KqNXC\n180xY6ROw8T2ySfCN47atR3/3NOmCbctHjUKEGHKPZORu3eFGXQKaQfNMttVtX37dmzduhU7duzA\n6/kml1euXBl9+vTBq6++6rCQ5kjVVZUnORn417+EOdjVq0sWg4ksPl643/P589JNk5w2TbiiuJiq\nPkyBJk0SShd9/720OWz+7Cxp2tXhw4dLPWVrz5495O/vT76+vjR79myT24wcOZL8/PxIq9VSQkKC\ncf3UqVPJy8uLvL29qWfPnvTgwYMi+1oQ3+4iI4k+/ljqFExMbdsK066ldP8+kasrUWKitDmYeNLS\niGrUINLrpU5i+2dniXsnJydTy5Ytydvbm4iITp06RVFRUSUe+PHjx+Tp6Ul6vZ6ys7MpJCSkQMNA\nRLRp0ybq1q0bERElJCRQYGAgERGdP3+eGjZsSFlZWURE9MYbb9D3339fNLwMGo7UVOHNkJ5ufpuy\nOAddSvbM+X//R+TlJdyJzVa25lyyhKhDB9tzlEQJr7sSMhIVn3PwYOHOkXJg62dniYPjgwcPxoIF\nC1C5cmUAws19NmzYUOI3mfyl0cuXL28sjZ5f/jvXabVa5OTkID09HTVq1MBzzz2HBw8eICcnBw8f\nPkSDBg2s/TLlEB4e/9xXgSmbwfDP3PqnleUl9d57wIULwG+/SZ2E2So5GdixQ3h/lQUlNhyPHz9G\n8+bNjcsqlQrlypUr8cCWlEY3t02NGjUwbtw41K9fH/Xq1cMLL7yAtm3bWvQLSWHyZGDbNvPVLfOK\njsnds55z3TqgYkUg3/2obGJrzgoVgFmzhLIUBoM4mUxRwuuuhIyA+ZyTJwvjG2VlLLTEaxZr1KiB\nCxcuGJd37tyJmhZMMLe0rDaZGKC5ePEiFi1ahJSUFFSvXh29e/fG2rVr0b9//yLbOqo6bknLEyYA\n776rw4wZ8qrWycuWLWdlAR99pMPkyYBKJX2evGUXF6BcuXCsXw/UrSt9Hl62ftnJKRwnTwIjR+qg\n00mTRydyddwSO7rOnj1LLVq0oEqVKpGHhwcFBwfT+fPnS+wDi42Npc6dOxuX586dSzNmzCiwzeDB\ng2njxo3GZbVaTXq9ntatW0dDhgwxrv/vf/9L77//fpHnsCC+wzx6RFS/PtGhQ0UfKwv9s3Jij5xf\nfkmU7+0qCrFy7t9P9NJLRE+H/ESnhNddCRmJiuY0GIheeYXov/+VJo85tn52lthV5e3tjcOHD0Ov\n1yMhIQHx8fEW3QWuadOmSE5ORnp6OrKzs7FhwwZ06tSpwDYRERFYu3YtACAhIQHlypWDm5sbGjVq\nhKNHj+LRo0cgIvz666+yv/NcpUrCjeYnTBCuLGfKkTe3fvZsqZOY1qaNcIvZb7+VOgmz1rZtwKNH\ngInOEkUrseTIgwcPsHHjRqSlpcHwtKNVpVJh6tSpJR58z549GD9+PAwGAwYOHIjJkycbS6oPHToU\ngHD3upiYGFSsWBHff/+9sXx6VFQU1q5dCycnJ2i1WqxcuRKVCt16TerrOArLzQW0WqEB6d5d6jTM\nUh9/DFy7JpRPl6sTJ4B27YRrS6pVkzoNs0R2NqDR2O8+Lrawe62q8PBwuLq64uWXXy4wKJ530yUp\nya3hAIDdu4X7dpw8qcyql8+a9HSh/ENSkjBDTs7efhuoX1+4PzWTv2+/FQqi7tsnTSHD4tj9AkC1\nWm1TX5g9WRDf4QwGovBwouXL/1mn1P5ZuRIz5zvvEE2cKNrhChD7fF65IlwzdPWqqIdVxOuuhIxE\n/+TMzCSqW5fo+HFp85hj62dniWMcrVq1QnJyculbpmdM/vsqPHggdRpWnNOnge3blTO3vn59YNAg\n4RbGTN6+/FKoZ/fyy1InsQ+zXVUajQYAkJubi/Pnz6Nhw4aoWLGisJNKhRMnTjgupRly7KrK88Yb\nwn2kP/5Y6iTMnG7dhAqlH30kdRLL3b4tDJQfOiT8l8nP9etCkcpjx4BGjaROY5rdxjhSUlLMPoFK\npZLFldxybjguXBDu2XH2LFCrltRpWGGHDgkzXf78U5gRpyRz5wo3l9qyReokzJRRo4Seh8WLpU5i\nnt3HOAYMGGDROilYEF9Sw4cTjRmjvP5ZubM1p8FA1KIF0cqV4uQxx17n8+FDIg8PIhvqjxaghNdd\nCRmJiNasiaEaNYiuX5c6SfFs/ewscYyj8PhGbm4ujh07ZlGjFB0dDY1GAz8/P8yZM8fkNqNGjYJa\nrUZwcDASExON6+/evYvevXsjMDAQvr6++P333y16TjmZOhX473+Bv/6SOgnLb/t2ICMDGDBA6iSl\nU7myMM4xcSJfMyQ3K1YI9+dxcZE6iZ2Za1FmzpxJzs7OVK5cOXJ2djb+VK9enUaPHl1ii2RLdVwi\nol69etG6deuIiCg3N5fu3btX5DmKiS8b06YR9esndQqWJzubyMeHaPduqZPYJieHSK0m+uUXqZOw\nPMePCzOpMjOlTlIyWz87S9x7YinnKh44cKBAyZF58+bR559/XmCbwYMH06ZNm4zLeSVHbt68SY0b\nNy7xOZTQcNy/T1SnDlGhNpNJZNkyojZthO4qpduxg8jPT2gMmbQMBqLXXiP69lupk1jG1s9Os11V\nly5dAgDMLqYOw8WLF80+VtrquGlpaTh//jxcXFzwxhtvwN/fH2+99RYyMzOL/+okU1WrAn366BQx\n5TOvKJrclTbngwdCF8+cOY65IMve57NzZ2HixapVth1HCa+73DP+3/8JF5M2bqyTOopDmG04Jk+e\njC5dumD58uVISEjAX3/9hatXryI+Ph7Lli1D586dMWXKFLMHLm11XJVKBYPBgLi4OIwfPx7Jycmo\nUaMGPlfw5bJdugCXLwtXkDLpLFoEtGoFNG0qdRJxqFTCDKtp04CHD6VO8+wyGISS6bNmARbccaJM\nMFsUY/369bhw4QJ+/vlnTJkyBVeuXAEANGjQAK1atcKSJUvw0ksvmT1w3reHPGlpaQW+XeTfJu9+\nH3q9Hu7u7jAYDHBzc0PTp/+H9+rVy2zDIZey6sUtt20bjlmzgOHDdVi2DHjtNXnly1vOWyeXPGIu\n37wJzJ2rwzffAIBjnj9vnb1/v1deCcfixUBoqH1/HymX80qDyyVP/uW0tHBUrgy8+KKwnEcu+fLO\nnUPLqpfWo0ePqEGDBqTX6+nJkycUEhJC8fHxBbbZtGkTde/enYiI4uPjKSAgwPjYyy+/TH/++ScR\nEU2bNs3kgLwd44vOYCBq1ozo6Xg/c7DRo4lGjJA6hX38+SdRzZpEN29KneTZ8/gxUYMGRLGxUiex\njq2fnaXae+/evRZtt3v3blKr1eTr60uzZs0iIqJvv/2Wvs03gjRixAjy8/MjrVZboGFJSkqikJAQ\n8vPzo06dOtHt27eLhldIw5E3Bz0mhqhhQ+HNJkdKmStvbc5Ll4QaT9eu2SePOY48n++/TzR2bOn2\nVcLrLteMX35J1LXrP8tyzVmYrZ+dparfOnjw4ALdUOZ06tSpyD048sqp5/nqq69M7hsYGIi4uLjS\nxJOt8HDA11eomjl6tNRpnh2ffCKcb1dXqZPYz7RpgFotXLUsg6IOz4S7d4X708fESJ3E8cyWHOna\ntavZnX777Tc8lMFonJxLjphz8iTQti1w7lzZuf+wnCUkCJMTzp0DnJ2lTmNf06YJkzD++1+pkzwb\nJk8W6lKtWCF1EuvZrVbViy++iNWrV8M53/9teU/2xhtv4Pr166V+UrEoseEAgMhIwN0dmDFD6iRl\nX7t2QI8ewLBhUiexv4wMwMsLiI4WCmwy+9HrgcBA4H//E/5fVhpbPzvNTsdt3rw5qlSpYpzREB4e\njldffRXh4eHw5rKcVsmb3ZDns8+ApUuBq1elyWNO4ZxyZWnOvXuBK1eAd96xbx5zHH0+q1YVuuUm\nTbJuPyW87nLLGBUFvPtu0UZDbjntxWzDER0djddee83kYwcPHrRboGdB/frAkCHCm4/Zh8Eg1HKa\nORN47jmp0zjOe+8JlZl/+03qJGXX6dPAL79Y30CXJSXeOlbOlNpVBQB37gBNmgAHDwI+PlKnKXvW\nrRPKWh89Kr/bdtrb+vXChYFxcYBTiWVMmbVef12Y6DJ2rNRJSs9uXVXFybvJEyu9F18EJkwQBtiY\nuLKyhC6buXOfvUYDAHr3FhqMjRulTlL2HDwInDgBjBghdRJpmW04Nm/eXORny5Yt2Lx5M/6ysE64\nLWXVAaGEu1arLXaGlxKY6/f84AMgPh44csSxecxRSv9sSTmXLhXuwPbqq47JY45U59PJSajH9fHH\nwJMnJW+vhNddDhmJhO7PGTOApzdDLUIOOR3B7HUcffv2Rb9+/eBU6LsuEeHx48clHjgrKwvDhg3D\noUOH4OrqitDQULRv3x5arda4zebNm5GamopTp04hMTERgwYNQlJSkvHxxYsXw8/PDxkZGaX53WSv\ncmVhoHzCBOEvmWfxr2Ox3bsnzK3/9Vepk0jrtdeErtBly4CRI6VOUzZs2yYUyuzXT+okMmDuykCt\nVksnTpww+Zi7u3uJVxaWtqx6WloaERGlpaXRv/71L9q/fz916dLF5HMUE18xcnKI/P2Jtm2TOknZ\nMHkyUWSk1CnkISmJyNWVyMStbJiVnjwh8vYm2rNH6iTisPWz02xX1aJFi1CtWjWTj23durXEBqm0\nZdXT09MBAB9++CHmzZtX5BtPWVOuHDB7tjBDIydH6jTKlp4u/IX92WdSJ5GHwECgQwdg3jypkyjf\nDz8Abm7C+WTFdFWFhYWZ3enQoUMICQkp9sClLatORNi5cydq164NrVZbYp+hEqrj5q0z93hERDjm\nzQMmT9ahc2fp8i5atEiW58/S8zl0qA7t2gEeHvLIK4fz2akTMGJEOIYPB/780/T2eeukPl/FLRfO\n6sjnb9o0HNOnA9Om6XDgQPHbJyUlYcyYMQ7NZ+n5k7w6riVdVbGxsQW6qubOnUszZswosM3gwYNp\n48aNxuWv0Fb5AAAgAElEQVS8rqrJkyeTu7s7eXp6Up06dahKlSo0cODAIs9RyvgOZ0nhs2PHiOrV\nI3rwwP55zFFKgTZTOU+fJqpVi8hELUzJyOV8jhsnFEE0Ry45iyNlxs8/J+rTx7JtlXAuiSSqjmtJ\nw2FrWfU8Op2uTI9x5NerF9HTIsLMSt26Ec2dK3UKebp5Uyi7fvas1EmU5/p14dydPy91EnHZ+tlZ\nquq4lqhUqRKWLl2KDh06wGAwYODAgQgODsayZcsACFVye/bsiZiYGKjValSsWBE//vijyWNZ2u2l\ndLNmAaGhQimDWrWkTqMchw8LxQx//lnqJPJUsyYwfrwwPXfzZqnTKMuMGcCbbwKNG0udRGbMtSjP\nP/88OTs7m/xxcnKyqbUSSzHxZcWar6/DhxONGWO/LMVRytfs/DkNBqIWLYhWrpQujzlyOp8PHxK5\nuxP9/nvRx+SU0xwpMl68KHzb+Ptvy/dRwrkksuM3jszMTMe1Xsxo6lTh4rVRo4CGDaVOI3/btwtV\nYQcMkDqJvFWuDEyfLlwzdOAAXzNkiSlThPu41K4tdRL54VpVMhQVJRSqW7NG6iTylpMDaDTAggVA\nRITUaeQvN1eYovvFF4DCizHYXXy8cI7Onweef17qNOKTpFYVs69x44TqpoUqsLBCVq4U7upX6CaT\nzAy+ZshykyYJN8Yqi42GGLjhcID8c9AtkXdfhYkT7ZPHHGtzSkWn0+HhQ+F/bDkXMpTj+ezcWRgs\nX7Xqn3VyzFmYIzPu3QukpgKDB1u/rxLOpRi44ZCpd98VbgP6rNdcMmfRIqBlS6BZM6mTKItKJTS2\nUVHAo0dSp5Efg0EYB5o169m6j4vVRBigL9aePXvI39+ffH19afbs2Sa3GTlyJPn5+ZFWq6WEhAQi\nIkpNTaXWrVuTv78/NWnShObMmVNkPwfEl9T69UTBwUS5uVInkZcbN4TZLufOSZ1EuXr0IDLzv+Mz\nbfVqoubNhdl6ZZmtn512/eR9/PgxeXp6kl6vp+zsbAoJCTE2DHk2bdpE3bp1IyKihIQECgwMJCKi\na9eu0cmTJ4mIKCMjg7y8vCgpKalg+DLecBgMRE2bEq1bJ3USeRk9Wpi2zErv7FnhSvtbt6ROIh+P\nHxN5ehLFxkqdxP5s/ey0a1fVsWPHoFar4ebmhvLly6NPnz7YtWtXgW12796NgQMHAgC0Wi1ycnKg\n1+vh6uoKf39/AICzszMCAgJwVW436bZQafs987oVpkwRbk5kb0ron718GfjhBx2mTpU6ScnkfD69\nvYFevYQuGTnnzOOIjN98I8zSa9269MdQwrkUg10bjtJWyC28TUpKCuLi4tCqVSt7xpWl8HDh1rLf\nfit1Enn45BOgRw9hNhWzzbRpwI8/AteuSZ1EenfvCjPOvvhC6iTKYNeGo7QVcvPvl5mZid69e2Px\n4sWoWrWqqPkcJa9aZWnNmSP8ZXj/vjh5zLE1p70lJgL79wNLloRLHcUicj+fdeoIt0DdvTtc6igl\nsve5nDtXmHGmVtt2HLm/5mKxW60qQPj2kJaWZlxOS0sr8O0i/zbNmzcHIHwDcXd3BwBkZ2ejZ8+e\n6NevH7p3727yOZRQVl2M5Y4dgREjdBgyRB55pFh+910d+vQBqlaVR56ysNy8ObB8eTj+9z/gzh3p\n80ix7OUVjmXLgKVLddDppM9jj2WdHMqqW8qWCrkGg4EGDhxIY4op3GTn+KIRo37NlStENWoQXb1q\nex5z5FxnZ+9eIi8v4U5scs6Zn1JyjhwZQx07Sp2iePY8l0OGEE2YIM6xlPKa2/rZadeuqvwVcgMD\nA9GjRw9jhdy8Krk9e/aEm5sb1Go13nnnHWOF3MOHD2PNmjWIiYmBVquFVqtFdHS0PePKWv36wKBB\nwvz7Z43BIFwMOXMmz623h65dgXPnhG7AZ83p08AvvwCTJ0udRFm4VpWC3L4tzIY5eFAYMH9WrFsH\nLF4MHD0q36vElW79emD+fODYMcDpGbosuFs3ICxMKPPzLOFaVc+QGjX+ua/CsyIrS5hJNWcONxr2\n1Ls3QARs2iR1Esc5dAhIShImCDDrcMPhAHmDVGIYORI4fhw4ckS0QxqJmVMs334rfLvKP1lFjjlN\nUVJOJydhZtHHHwNPnkidqCixzyWRUFrk88+BSpXEO65SXnNbccOhMHn3VZg4UXjzl2X37gnTkGfP\nljrJs+G114Q73T0dfizTtm8HMjOB/v2lTqJMPMahQLm5QFCQMFj8+utSp7GfTz4B9HqhfDpzjKQk\noGNH4T4UCr1sqkQ5OYC/P7Bw4bNbkt/Wz05uOBRq1y7hq/b//geUt+vVONK4elUo/5CYKMwoY44z\ncCDw0kvCN9uyaPly4f70v/327I6b8eC4Atij3zMiAqhVq+B9FWwlp/7ZqChgyBDTjYacchZHqTln\nzAC++kpepUjEOpcPHggNor3u46KU19xWdm04oqOjodFo4Ofnhzlz5pjcZtSoUVCr1QgODkZivlve\nWbKvUiQlJYl+zLwCiNOmAQ8finNMe+QsjbNnga1bzc+tl0vOkig1Z4MGQGSkvL5xiHUuFy0CWrUC\nQkJEOVwRSnnNbWW3hiMrKwvDhg1DdHQ0Tpw4gU2bNhVoGABg8+bNSE1NxalTp7BixQoMGjTI4n2V\n5O7du3Y5bvPmwCuvCNc4iMFeOa01ebLQDffii6Yfl0vOkig558cfAxs3An/+KUEgE8Q4lzduCOMa\nM2eKEMgMpbzmtrJbw2FLSXVL9mWCWbOABQuAmzelTiKOI0eA+Hhh2jGTTs2awEcfCSX9y4oZM4A3\n3xRmjjHb2K3hsKWkenp6eon7KklKSordjt2kCfDGG0IDYit75rSEpXPrpc5pKaXnHD1auJL86FHH\n5jHF1nN56RKwZo0wU8+elPKa28pu83FKW1LdGo0aNbL4eaS2SsxRbDMWLrT9GI7IWZLDh4U+9uLI\nIaclykLO0FAHBimGGOeyTh0RgpRACa95o0aNbNrfbg1HaUuqe3h4IDs7u8R9AeDChQt2Ss8YY8wc\nu3VVNW3aFMnJyUhPT0d2djY2bNiAToWutomIiMDatWsBAAkJCShXrhzc3Nws2pcxxpg07PaNI39J\ndYPBgIEDBxpLqgPA0KFD0bNnT8TExECtVqNixYrGkurm9mWMMSY9RV85zhhjzPEUc+X42LFj4efn\nBz8/P3Tp0gW3bt0yPvbFF1/Az88PGo0Ge/fuNa6Pj4+HVquFWq3G6NGj7Z5x48aNUKvVKFeuHBIS\nEozrU1JSULlyZeMNqYYPHy5ZxuJyAvI5l4VFRUXB3d3deA737NlTYmapyPniVU9PTwQEBECr1aJZ\ns2YAgNu3b6Ndu3YICAhAhw4dJLkWYfDgwXB1dYVGozGuKy6XVK+5qZxye2+mpaUhLCwMGo0G3t7e\nmDt3LgCRz6dN9w90oP3791Nubi4REU2cONF4S9njx49TSEgI5eTkkF6vJ09PT3ry5AkREWk0GkpI\nSCAiom7dutGWLVvsmvHMmTP0559/Unh4eIFb5F6+fJn8/f1N7uPojMXllNO5LCwqKooWLFhQZL2p\nzFlZWQ7Nlt/jx4/J09OT9Ho9ZWdnU0hIiPG8yYGnpyfdunWrwLoPPviAFi5cSERECxcupFGjRjk8\nV2xsLCUkJBT4/8RcLilfc1M55fbevHbtGp08eZKIiDIyMsjLy4uSkpJEPZ+K+cbRpk0bOD29NVnL\nli2Rnp4OANi1axf69u1rHFhXq9U4duwYUlNTYTAYoNVqAQADBgyw+0WEPj4+aNKkicXbS5ERMJ9T\nTufSFDLRq2oq8x9//OHwbHmUcPFq4fOY/0JcqV7b1q1b48VCpQLM5ZLyNTeVE5DXe9PV1RX+/v4A\nAGdnZwQEBCA9PV3U86mYhiO/5cuXo1u3bgCA9PR0uLu7Gx8zdxGhm5ubpBcRpqSkICgoCC1atMD+\npzd3LnwBpNQZ5X4uv/76a/j6+mLAgAG4fft2sZmlYsmFr1JSqVTG7oqvvvoKAHDjxg3UrFkTAFCr\nVi1cv35dyohG5nLJ7TUH5PveTElJQVxcHFq1aiXq+ZRVQe527drhmomSnLNmzULXrl0BADNnzkSF\nChXQX6I7sFiSsbB69eohPT0d1apVQ2JiIrp06YJTp07JLqfUzGWeOXMmRowYgalTpwIQ+pRHjRqF\nNWvWODpiieR+QerRo0dRu3Zt3LhxAx07doTPs3TzejuR63szMzMTvXr1wuLFi1GtWjVRjy2rhmPf\nvn3FPr5q1Srs2rXL+Bc7UPRCw7y/+Eytz9+q2iujKRUqVECFChUACDW5/P39cfbsWXh4eNglY2lz\nOvpcFmZp5qFDh6JNmzYAzGeWiiUXvkqpdu3aAAAXFxf06tULcXFxcHFxwc2bN1GrVi3cuHHDuI3U\nzOWS22teq1Yt47/l8t7Mzs5Gz5490b9/f3Tv3h2AuOdTMV1V0dHRmDt3Ln755RdUylfIKCIiAuvX\nrzcWSExOTkazZs3g4eEBJycnY1XdtWvXIiIiwmF58/d53r59GwaDAYDw1TE5ORmNGzeWPGPhnHI9\nlwAKdJ9s3rwZarW62MxSkfPFqw8fPsTDpzX4Hzx4gOjoaKjVakRERBj/Ql6zZo3DX1tzzOWS22su\nt/cmEWHIkCHw8/PDhx9+aFwv6vm018i+2Bo3bkz169enoKAgCgoKomHDhhkfmzlzJvn6+pJarabo\n6Gjj+uPHj1NQUBD5+fnRyJEj7Z5xy5Yt5O7uTpUqVSJXV1fq2LEjERFt3LiR1Go1aTQa8vf3p02b\nNkmWsbicRPI5l4UNGDCAAgICyMfHhzp06EB6vb7EzFLZvXs3qdVq8vX1pVmzZkkdx+jSpUsUEBBA\ngYGB5OXlRZ9++ikREd26dYvatm1LGo2G2rVrR3fu3HF4tr59+1LdunXpueeeI3d3d/rhhx+KzSXV\na14454oVK2T33jx48CCpVCoKDAw0fl7u2bNH1PPJFwAyxhizimK6qhhjjMkDNxyMMcasIuuG4/Hj\nx2jatCm0Wi2aNGlSYKCHMcaYNGQ/xvHo0SNUrlwZOTk5aNWqFb744gvjdDfGGGOOJ+tvHABQuXJl\nAMCTJ0+Qm5sLV1dXiRMxxtizTfYNh8FgQFBQEFxdXdGmTRv4+flJHYkxxp5psrpy3BQnJyckJSXh\n3r176NChA3Q6HcLDwwEINZOuXr0qbUDGGFOYRo0a2XTrbdl/48hTvXp1dO7cGUePHjWuu3r1KohI\n9j9vv/225Bk4J+dUck5HZ4yIiMC9e/dARBg0aBBq164Nf39/q3LGxMSgS5cuICKcOXMGr7zyCipW\nrIj58+dblOGdd97BmTNn8PDhQ7Rv3x5+fn7GSUIGg8Hi32XlypXGexmtWrUKRISLFy/a9Hks64bj\n1q1byMjIACAMku/bt6/ADVQYY8wedu3aZSwMOGjQIERHR9t0vJo1a2LJkiX46KOPLN7nu+++Mxah\nnDp1Kk6dOoXk5GQcP34cv/zyi0XH+Ouvv/D555/j2LFjOHbsGD777DP8/fffpfod8pN1w3H16lWE\nhYUhKCgIWq0Wbdu2RefOnaWOZTVPT0+pI1iEc4qLc4rH0Rk9PT2N5dHN3YPD3H6muLi4ICQkBM89\n95zFGcLDwxEfH4/KlSujZcuWAIDnnnsOzZo1s7iLft++fejUqROcnZ3h7OyMjh07lqoAamGyHuPQ\naDTGwnpKljcmI3ecU1ycUzyOzmhJefz58+dj7dq1BdZlZmbi7t27WLRokSgZCue4e/cutm7dil9/\n/RUAsG7dOsybN6/Ivl5eXtiwYYPd7gki64aDMcbk6qOPPirS9ZR/8o7YcnJy0K9fP4wePRoNGzYE\nAPTr1w/9+vWzy/MVR9ZdVYwx5mg7duzAvXv3AACxsbEIDg6Gl5eXcV2eefPmQavVFvh59913MXr0\naADC+MKhQ4eg0WjQt29fZGdnF/u8V69eRe/evQEAe/fuxfHjx9GnTx9oNBr83//9H9577z14eXlh\n1KhRxn3Wrl1bJEOdOnUQGBgIADhw4ADmzJkDPz8/dOnSBefPnxflniCyv3K8OCqVCgqOzxiTqYYN\nGyI+Ph4ZGRm4f/8+oqKiEBcXh9TUVIuP0aJFC2RnZyMuLg5jxoxBgwYNcO/ePVStWhXjxo0zbvev\nf/0La9asQd26dY3rTpw4gWHDhmHJkiWoWLEimjdvjvbt22Pz5s0ldqNNnz4dzs7OGDduHDZt2oSJ\nEyciKSkJ06ZNw4oVK3Du3DnUqVPHps9O7qpijLF8Vq5caRwYb9CgAd58803s2bMH2dnZ8PDwwGef\nfYZBgwYVe4ycnBycPn0aYWFhAICOHTuie/fuqFixIpycnLB48WKcPn0aVapUwcWLF1GjRg2kpKSg\na9euOHnyJAICAox3Da1evToePHiAM2fOIDg4GAAwcuRIDB48uMTfpVevXsjIyEDz5s2RkZEBb29v\nUapvcFeVA+h0OqkjWIRziotziseRGVUqFd566y3UqFEDAPDTTz+hT58++Pnnn5GWloZBgwYhIyOj\nSBdRXjHWs2fP4vr166hbt65x2mxgYCA8PT1x79493LlzB6mpqXB2dsaZM2fQq1cvVKxYsUiOmJgY\nBAcH4+jRo2jbti3OnDmDxMREeHt7Y8mSJUWe29x9zgcNGoTTp08jKCjI2I1mK/7GwRhjVqpatarJ\nGZ86nQ4+Pj4WT5dVq9WYP3++2cdPnz6NSZMmFZhC+/PPP1udd+bMmahQoQL69+9v9b6mcMPhAEqY\n7ghwTrFxTvFYknHevHmYNm0aAOCll17ClStXkJubi0qVKqFWrVrQ6/UgIri6uuLx48e4f/8+AMDb\n2xu//fab8RuGOfnHFjIyMtC6dWuT4w0//fQTGjdujJs3bxrX6fX6AtNiLaHX6/Hvf/8bq1evNs6i\nAoA+ffrg3LlzRbYfN24cBgwYUGT9qlWrsGvXLuzfv9+q5y8ONxyMsTJh/PjxGD9+vM3HMTVonFe+\nI0/VqlWRlJRU7HFeeeUVbNu2Dd27d8eaNWsQEREBAPjjjz/w9ddfY9WqVWb3vXv3Ljp37ozZs2cj\nNDS0wGPr16+3+HeJjo7G3LlzceDAAVSqVMni/Uoi6zGOtLQ0hIWFQaPRwNvbG3PnzpU6UqkooQ8Z\n4Jxi45zicfQYR943ibi4OHh4eGDTpk0YOnRoiSWP8uf8z3/+gzlz5kCj0eDvv//GyJEjAQCpqamo\nUqWK2ecGgK+++goXL17EZ599ZhzDyP8NxpLfARAG0TMzM9GuXTtotVoMHz7c4mMUi2Ts2rVrdPLk\nSSIiysjIIC8vL0pKSjI+LvP4RjExMVJHsAjnFBfnFI8SMhJZlnP8+PHGzzWp2PrZqajrOHr16oUh\nQ4agU6dOAPg6DsYYKw1bPztl3VWVX0pKCuLi4tCqVSupozDG2DNNEYPjmZmZ6N27NxYvXoyqVasW\neCwyMtJYkfKFF15AUFCQcQZGXn+j1Mt56+SSx9zyokWLZHn++HzadzlvnVzymFounFXqPOaWk5KS\nMGbMGNnkyVvW6XRYuXIlAJEqDYvQXWZXT548ofbt29OXX35Z5DEFxCeistU/KwecU1xKyKmEjETK\nyWnrZ6esxzjo6R21atasiYULFxZ5nMc4GGPMerZ+dsq64Th06BDCwsIQEBBgnF72xRdfoGPHjgC4\n4WCMsdKQtOHo2rVridvUqFGj2AtdbKGUhkNnxxr9YuKc4uKc4lFCRkA5OW397LRpcPzs2bP4/vvv\nTQbICzZixAhbnoIxxpjM2PSNY/369ejTp4/N25SWUr5xMMaYnJTpMY6ScMPBGGPWk7SrKs+RI0cw\na9YspKWlwWAwGIOdOHFCjMMrnlL6PeWSUzW9+Duc4TKAhsVvIguXAVop/z9s5PK6F0eMjCW+r8Tg\noPcmTZP2fSVKw9G/f38sXrwY/v7+cHJSzMXoTKZK+p9CCR90gDKKBz5LHPFhq5T3pq1E6aoKCwtD\nbGysGHmswl1VjDFmPVmMcezbtw8bNmzAa6+9ZrxPrkqlQo8ePWw9dLG44WCMMevJosjhypUrkZiY\niOjoaOzcuRM7d+7Ejh07xDh0maCULgvOKS7OKR4lZASUk9NWooxxxMfH48yZMyZvo2iLwYMHY9eu\nXahduzZOnjwp6rEZY4yVjihdVUOGDMH48ePh4+MjRiajgwcPwtnZGW+99ZbJhoO7qhhjzHqyGOPw\n8fHBxYsX0bBhQ1SsWNEYTIzpuCkpKejatSs3HIwxJhJZjHFER0fj/Pnz2Lt3L3bs2IEdO3bgl19+\nEePQZYJS+j05p7g4p3iUkBFQTk5biTLGIcqNQUpJKTdyklMec8tJSUmyysPn0zHLeeSSR8nLSUlJ\nssqTt6xTyo2cIiIiRDnO5cuXyd/f3+RjdowvqmnTiICiP9OmKWt7c48z6cj1vcLvLXmz9bPTbrWq\nrl69inr16tl8HB7jYIwxcclijMMUMRqNN998Ey1atMC5c+fg4eGBH3/8UYRkjle4S0CuOKe4OKd4\nlJARUE5OW9k0xtGmTRuT6/Ou59i/f78th8dPP/1k0/6MMcbEZ1NX1fHjx/850NPG4ujRo5gzZw5q\n165d4HF74K4qxhizniyu4wCEr2gzZszAo0eP8Mknn6BTp05iHLZY3HAwxpj1JB/jiI6ORuvWrfH5\n559jypQpOHz4sEMaDSVRSr8n5xQX5xSPEjICyslpK5vGOJo2bYobN27go48+QmhoKAAgISHB+Hhw\ncLBt6RhjjMmOTV1VeReamCtuGBMTU9pDW4S7qhhjzHqyGeOQAjccjDFmPUnHOPJ3S9myTVmnlH5P\nzikuzikeJWQElJPTVjaNcURGRhZ7oogIQ4YMQWJioi1PwxhjTEZs6qry9PQs8eZNLi4u+OOPP0p1\n/OjoaIwfPx65ubl4++23MXHixAKPc1cVY4xZr8yOcWRlZcHHxweHDh2Cq6srQkNDsXz5cmi1WuM2\n3HAwxpj1JL+Ow16OHTsGtVoNNzc3lC9fHn369MGuXbukjlUqSun35Jzi4pziUUJGQDk5bSXbhkOv\n18PDw8O47O7uDr1eL2EixhhjgEg3crKHksZO8ijhRk5KWc5bJ5c8Sl/OWyeXPEpezrsZkVzyFLec\nRy558s6dmDdyEmWMIycnBytXrkRaWhqmT58OvV6Pq1evolmzZqU+5sGDBzFnzhzs3LkTADBv3jw8\nefIEU6ZM+Sc8j3EwxpjVZDHG8d577yEhIQHr168HAFSrVg3vv/++Tcds2rQpkpOTkZ6ejuzsbGzY\nsEGxNbAK/yUiV5xTXJxTPErICCgnp61E6ao6duwYTp06ZZzxVK1aNRgMBpuOWalSJSxduhQdOnSA\nwWDAwIEDufYVY4zJgChdVYGBgUhISEBISAgSExNx584dtG7dGsnJyWJkNIu7qhhjzHqy6Kr64IMP\n0K1bN1y/fh1Tp05FaGgoxo8fL8ahGWOMyYwoDce7776LmTNn4sMPP0S1atWwfv16vP3222IcukxQ\nSr8n5xQX5xSPEjICyslpK1HGOFJTU/Hiiy+id+/eAISvQampqahfv74Yh2eMMSYjooxx+Pv7G6+7\nePz4MS5fvgxvb2+cOnXK5oDF4TEOxhiznq2fnaJ84yg8CJ6UlISvvvpKjEMzxhiTGbuUHAkKCsLR\no0ftcWhFUkq/J+cUF+cUjxIyAsrJaStRvnEsWLDA+G+DwYCEhATUqlVLjEMzxhiTGVHGOKKiooxj\nHE5OTnB3d8cbb7yB559/3uaAxeExDsYYs16ZvR/Hxo0bERUVhbNnzyIuLs7kVePccDDGmPVkcQFg\n165d8frrr6Nr164m/10aGo0GW7duRVhYmBgRJaWUfk/OKS7OKR4lZASUk9NWooxxNGzYEDdv3sSb\nb74JIsL69evh4uKCf//736U+po+PjxjRGGOMiUyUrqrmzZvj2LFjJa4rjTZt2mDBggXcVcUYYyKR\nRVfV7du3kZKSYly+cuUKbt++XeJ+7dq1g0ajKfKzY8cOi587MjISUVFRiIqKwqJFiwp8VdTpdLws\n4nJkpA4qlQ4qFZ7+CMtRUcrbPipK+vPJy/8sR0X98/rlfz0jI5W1vbnHpV7W6XSIjIw0fl7ajESw\nbds2qlOnDoWFhVFYWBjVqVOHtm/fLsahKTw8nOLj400+JlJ8u4uJiZE6gkU4p7g4p3iUkJFIOTlt\n/ey0eYzDYDAgKysLly5dwsmTJ+Hk5AS1Wo3KlSvb3qo9RdwdxRhjsmG3MQ5bbd26FaNGjcLNmzdR\nvXp1aLVa7Nmzp8A2PMbBGGPWk8V1HJMmTYKrqyt69epV4KK/GjVq2HroYnHDwRhj1pPF4PjPP/+M\n//znPwgLC8PLL7+Ml19+GSEhIWIcukzIP2AlZ5xTXJxTPErICCgnp61EuY4j/4wqxhhjZZsoXVVZ\nWVlYtGgRDh48CJVKhbCwMIwePRoVKlQQI6NZ3FXFGGPWk8UYR//+/VGxYkUMGDAARISffvoJjx49\nwtq1a209dLG44WCMMetJ2nDk5OSgfPnyUKvVRe72Z2qd2MRoOFTTVSKlKcZlAA3t/zQ0zbZzodPp\nEB4eLk4YO1JKTlWkyiGvu80seH/a+t6ylVJec6XklPQOgM2aNUNCQgJUKhVSUlLg6ekJQBjzcHKy\nyz2iROeI/yGU8mZi4oqJjFHE687vT2Ytm75xaLVaJCYmYvfu3Rg8eDB8fHxARDh37hxWrFiBiIgI\nMbMWwV1VjDFmPUm7qtzd3TF27FgQER4+fIhKlSoBEAbLq1SpgrFjx5Y62NixYxEdHQ0AeOmll7Bq\n1SrUrFmzYHhuOBhjzGqSXseRm5uLjIwMZGZmwmAw4OHDh3j48KFxvS26du2K5ORknD59Gv7+/pgx\nY4ZNx5OSUuZ2c05xcU7xKCEjoJyctrJpjKNOnTqYNm2aWFkKaNOmjfHfLVu2xOrVq+3yPIwxxqwj\nyo1xriUAAA5OSURBVBiHvXXt2hV9+/ZF//79C6znrirGGLOepLOqfv31V1t2R7t27XDt2rUi62fN\nmoWuXbsCAGbOnIkKFSoUaTQYY4xJw6aGo/BgtbX27dtX7OOrVq3Crl27sH//frPbREZGGqcBv/DC\nCwgKCjJOLczrb5R6OW+dXPKYW160aJEszx+fT/su562TSx5Ty4WzSp3H3HJSUhLGjBkjmzx5yzqd\nDitXrgQA4+elTWy6m4cd7dmzh/z8/OjGjRtmt5Fx/AKUcnMXzikuzikeJWQkUk5OWz87RSk5Yg9e\nXl548uSJsTR7aGgovvnmmwLb8BgHY4xZTxa1qqTCDQdjjFlPFvfjYMXL3z8rZ5xTXJxTPErICCgn\np6244WCMMWYV7qpijLFnDHdVMcYYcyhuOBxAKf2enFNcnFM8SsgIKCenrbjhYIwxZhUe42CMsWcM\nj3EwxhhzKNk2HJ988gkCAwPh7++PsLAwXLp0SepIpaaUfk/OKS7OKR4lZASUk9NWsm04Jk2ahP/9\n739ITk5G7969MX36dKkjlVpSUpLUESzCOcXFOcWjhIyAcnLaSrYNh7Ozs/HfmZmZqFu3roRpbHP3\n7l2pI1iEc4qLc4pHCRkB5eS0lU1l1e1typQpWL16NapUqYKjR49KHYcxxhgk/sbRrl07aDSaIj87\nduwAINzEKTU1FZGRkfjwww+ljGqTlJQUqSNYhHOKi3OKRwkZAeXktJUipuOmpqaiffv2OHv2bIH1\njRs3xsWLFyVKxRhjytSoUSNcuHCh1PvLtqvq8uXLaNiwIQBg+/bt0Gg0Rbax5RdnjDFWOrL9xtGj\nRw9cvHgR2dnZaNiwIb7//ntFD5AzxlhZIduGgzHGmDzJdjpuYWPHjoWfnx/8/PzQpUsX3Lp1y/jY\nF198AT8/P2g0Guzdu9e4Pj4+HlqtFmq1GqNHj7Z7xo0bN0KtVqNcuXJISEgwrk9JSUHlypWh1Wqh\n1WoxfPhwyTIWlxOQz7ksLCoqCu7u7sZzuGfPnhIzSyU6OhoajQZ+fn6YM2eO1HEK8PT0REBAALRa\nLZo1awYAuH37Ntq1a4eAgAB06NBBkimlgwcPhqura4Eu6eJySfWam8opt/dmWloawsLCoNFo4O3t\njblz5wIQ+XzadMdyB9q/fz/l5uYSEdHEiRNpzJgxRER0/PhxCgkJoZycHNLr9eTp6UlPnjwhIiKN\nRkMJCQlERNStWzfasmWLXTOeOXOG/vzzTwoPD6f4+Hjj+suXL5O/v7/JfRydsbiccjqXhUVFRdGC\nBQuKrDeVOSsry6HZ8nv8+DF5enqSXq+n7OxsCgkJMZ43OfD09KRbt24VWPfBBx/QwoULiYho4cKF\nNGrUKIfnio2NpYSEhAL/n5jLJeVrbiqn3N6b165do5MnTxIRUUZGBnl5eVFSUpKo51Mx3zjatGkD\nJychbsuWLZGeng4A2LVrF/r27Yty5crBzc0NarUax44dQ2pqKgwGA7RaLQBgwIAB2LVrl10z+vj4\noEmTJhZvL0VGwHxOOZ1LU8hEr6qpzH/88YfDs+U5duwY1Go13NzcUL58efTp00eSc1Wcwudx9+7d\nGDhwIADpXtvWrVvjxRdftCiXlK+5qZyAvN6brq6u8Pf3ByBcSB0QEID09HRRz6diGo78li9fjm7d\nugEA0tPT4e7ubnzM3d0der0e6enp8PDwMK53c3ODXq93eNY8KSkpCAoKQosWLbB//34AgF6vl1VG\nuZ/Lr7/+Gr6+vhgwYABu375dbGapFH5Npc5TmEqlMnZXfPXVVwCAGzduoGbNmgCAWrVq4fr161JG\nNDKXS26vOSDf92ZKSgri4uLQqlUrUc+nrKbjtmvXDteuXSuyftasWejatSsA4aLAChUqoH///o6O\nB8CyjIXVq1cP6enpqFatGhITE9GlSxecOnVKdjmlZi7zzJkzMWLECEydOhWA0Kc8atQorFmzxtER\nS6RSqaSOUKyjR4+idu3auHHjBjp27AgfHx+pIymeXN+bmZmZ6NWrFxYvXoxq1aqJemxZNRz79u0r\n9vFVq1Zh165dxr/YAaF1TEtLMy7n/cVnan3+VtVeGU2pUKECKlSoAADQarXw9/fH2bNn4eHhYZeM\npc3p6HNZmKWZhw4dijZt2gAwn1kqhfOkpaVJmqew2rVrAwBcXFzQq1cvxMXFwcXFBTdv3kStWrVw\n48YN4zZSM5dLbq95rVq1jP+Wy3szOzsbPXv2RP/+/dG9e3cA4p5PxXRVRUdHY+7cufjll19QqVIl\n4/qIiAisX78eOTk50Ov1SE5ORrNmzeDh4QEnJyckJiYCANauXYuIiAiH5c3f53n79m0YDAYAwlfH\n5ORkNG7cWPKMhXPK9VwCKNB9snnzZqjV6mIzS6Vp06ZITk5Geno6srOzsWHDBnTq1EmyPPk9fPgQ\nDx8+BAA8ePAA0dHRUKvViIiIMP6FvGbNGoe/tuaYyyW311xu700iwpAhQ+Dn51egVJOo59NeI/ti\na9y4MdWvX5+CgoIoKCiIhg0bZnxs5syZ5OvrS2q1mqKjo43rjx8/TkFBQeTn50cjR460e8YtW7aQ\nu7s7VapUiVxdXaljx45ERLRx40ZSq9Wk0WjI39+fNm3aJFnG4nISyedcFjZgwAAKCAggHx8f6tCh\nA+n1+hIzS2X37t2kVqvJ19eXZs2aJXUco0uXLlFAQAAFBgaSl5cXffrpp0REdOvWLWrbti1pNBpq\n164d3blzx+HZ+vbtS3Xr1qXnnnuO3N3d6Ycffig2l1SveeGcK1askN178+DBg6RSqSgwMND4ebln\nzx5RzydfAMgYY8wqiumqYowxJg/ccDDGGLMKNxyMMcaswg0HY4wxq3DDwRhjzCrccDDGGLMKNxxM\nMe7du4elS5cal3U6ndXlU1atWoW//vpL7GgAgHLlyiE4ONjk8VeuXImRI0fa5XlLa/z48ahbty4W\nLFggdRSmMNxwMMW4c+cOvvnmG5uOsXLlSly9elWkRAVVqVIFCQkJdr1TJRGZrMRaGvPmzcP7778v\nyrHYs4UbDqYYkyZNwsWLF6HVajFhwgSoVCpkZmaib9++aNKkCXr37m38UP39998RGhqKgIAAtGnT\nBunp6di0aROOHz+O/v37Izg4GI8fP0ZUVBSaNWsGHx8fREZGGkvDhIeHY+zYsXjllVfg6+uLuLg4\n9OzZE40aNcLEiRMtyrts2TI0atQILVq0wJEjR4zrr127hi5duiAwMBBBQUE4cOAAAODvv/9Gq1at\nEBQUhPfeew+enp64ffs2UlJS4O3tjcjISAQFBUGv1+Ozzz5DQEAAfH19MXnyZOOxv/vuOwQGBkKt\nVmPw4MHIyclBTk4OBg4cCI1Gg4CAAP6GwWxnp6veGRNdSkpKgRvoxMTEUPXq1enatWtkMBgoNDSU\nYmJiKCsri4KDg+nmzZtERPTzzz9T//79iYiK3Lzq3r17xn8PHDjQWA4mPDycPv74YyIiWrx4MdWt\nW5du3LhBWVlZVK9ePbp+/XqRfM7OzsZ/p6amkpubG929e5dycnKodevWxlIt//73v+nQoUNERHTl\nyhVq1KgRERG98847NG/ePCIi2rdvH6lUKrp16xZdvnyZnJyc6Pjx40REtH37dnrvvfeIiCg3N5e6\ndOlC+/bto6SkJOrcuTPl5OQQEdGwYcPou+++oz/++IM6depkzJaRkWH8d1RUFM2fP9/Sl4AxIiKS\nVXVcxopDJrpomjVrBldXVwBAUFAQ0tLScOLECVy4cAFt27YFAOTm5hq3KXycnTt3YsGCBcjJycGt\nW7cKlBnv0qULAMDf3x/+/v7GKqiNGzdGeno6XFxczGb9/fff0bZtW1SvXh0A0Lt3b5w/fx4A8Ouv\nv+Ly5cvGbbOysnD//n0cOXIEn3zyCQCgbdu2BW4Y1KBBA7z88ssAgL1792Lv3r3GG2s9ePAAKSkp\nSEpKQmJiIkJCQgAAjx49MlbBvXDhAkaNGoWOHTvKpugiUy5uOJiiVaxY0fjvcuXKGbuaAgMDERsb\na3KfvHtmZGZmYsyYMThx4gTq1KmD6f/f3v27NBKEYRz/7hASRRPsYiOWESEqsVgI2CmCkEbSBowG\ntLIRC0FBY2PhH7BK0EIEMbG30dIUaawES8VSkCSIP1jxiuOWyxnl9oiF3vOpdneYeWeK3ZfZZWfW\n1nBd903bxpiGOMYYL857jDENCer3Y8uyqFQqBAJvb79myRGgo6Oj4XxlZYXp6emGa5ubm8zMzJDP\n59/UPz8/5/j4mEKhQKlUYmdn58P+i3xE3zjky2hvb/eWBX+PZVkMDAxwfX3tLQPvui6Xl5deG/f3\n9951YwxdXV08PDxQLBZb1lfbtjk9PaVarfLy8kKpVPLKRkdHcRzHO/+1qVcymeTo6AiAk5MT7u7u\nmrY9Pj7O7u4uj4+PwM9vI7e3t4yNjXF4eOjVq9Vq3NzceMv6T05Oks/nqVQqLRun/J8045AvIxqN\nMjQ0RH9/P6lUiomJiaY77gWDQYrFInNzczw9PeG6LvPz88RiMTKZDNlslkgkwtnZGdlslr6+Pnp7\ne7Ftu2lcy7J87+zX09PD8vIyiUSC7u5u4vG4V+Y4Drlcjq2tLV5fX0kmk2xvb7O+vk46nWZvbw/b\ntolGo7S1tVGr1Rrip1IpLi4uSCQSBINBQqEQBwcHDA4OsrS0xMjICIFAAGMMjuMQCoWYmpry6m9s\nbPgai8iftKy6SIuEw2Hq9fo/139+fvYe+OVymVwu9+lbDK+urhIOh1lYWPjUOPK96FWVSItEIpF3\nfwD8G1dXVwwPDxOPx5mdnaVQKLS4h40WFxfZ39+ns7PzU+PI96MZh4iI+KIZh4iI+KLEISIivihx\niIiIL0ocIiLiixKHiIj4osQhIiK+/ADYIdwwaPRWXQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1d13bd0>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 8.3, Page number: 424"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"%pylab inline\n",
"from sympy import *\n",
"from math import *\n",
"\n",
"#Variable declaration:\n",
"n1=4000 #r/min\n",
"R=0.038 #m\n",
"a=b=pi/3 #rad\n",
"g=2.54*10**-4 #m\n",
"D=0.13 #m\n",
"N=100 #turns in both poles\n",
"uo=4*pi*10**-7 #permeability of free space(H/m)\n",
"Ll=0.005 #H\n",
"Vo=100 #phase voltage applied to phase 1.(V)\n",
"\n",
"\n",
"#Calculation:\n",
"wm=n1*pi/30\n",
"Lm=N**2*uo*a*R*D/(2*g)\n",
"thetam=symbols('thetam')\n",
"t=symbols('t')\n",
"#for part (a):\n",
"#for -60<=thetam<=0deg,\n",
"L11=Ll+(Lm/(pi/3))*(thetam+pi/3)\n",
"L111=diff(L11,thetam)\n",
"R1=L111*wm\n",
"#which is nuch greater than resistance R=1.5 ohm\n",
"thetam=-pi/3+wm*t\n",
"i1=Vo*t/(float(round(Ll,3))+float(Lm/(pi/3))*thetam+float(Lm/(pi/3))*pi/3)\n",
"\n",
"#for part (b):\n",
"V2=-200 #applied voltage(V)\n",
"thetam2=symbols('thetam2')\n",
"L12=Ll+(Lm/(pi/3))*(pi/3-thetam2)\n",
"L112=diff(L12,thetam2)\n",
"to=2.5*10**-3 #ms\n",
"thetam2=float(-pi/3+wm*to)\n",
"i1=Vo*t/(float(round(Ll,3))+float(Lm/(pi/3))*thetam+float(Lm/(pi/3))*pi/3)\n",
"i2=(0.25-200*(t-to))/(0.005+51.1*(5*10**-3-t))\n",
"\n",
"\n",
"#Results:\n",
"print \"i1 =\",i1,\"\\t, (where round(16.2934044186179*pi,2) = 51.1 )\"\n",
"print \"\\ni2 =\",i2,\"\\n\"\n",
"\n",
"\n",
"#Calculations & Results:\n",
"#for part (c):\n",
"from __future__ import division\n",
"from pylab import *\n",
"\n",
"Lleak=0.005\n",
"Posintegral=0\n",
"integral=0\n",
"N1=500\n",
"tmax=3.75*10**-3\n",
"t=[0]*503\n",
"thet=[0]*503\n",
"Torque=[0]*503\n",
"deltat = tmax/N1\n",
"thetm=[0]*503\n",
"i=[0]*503\n",
"for n in range(1,N1+2,1):\n",
" t[n-1]=tmax*(n-1)/N1\n",
" thetm[n-1]=-(pi/3)+(400*pi/3)*t[n-1]\n",
" if (thetm[n-1]<=0):\n",
" i[n-1]=100*t[n-1]/(0.005+51.1*t[n-1])\n",
" dld1d1theta = 0.122\n",
" Torque[n-1]=0.5*i[n-1]**2*dld1d1theta\n",
" Posintegral=Posintegral+Torque[n-1]*deltat\n",
" integral=Posintegral\n",
" else:\n",
" i[n-1]=(0.25-200*(t[n-1]-2.5*10**-3))/(0.005+51.1*(5*10**-3-t[n-1]))\n",
" dld11dtheta = -0.122\n",
" Torque[n-1] = 0.5*i[n-1]**2*dld11dtheta\n",
" integral = integral + Torque[n-1]*deltat\n",
"\n",
"print \"\\nPositve torque integral =\",Posintegral, \"[N-m-sec]\"\n",
"print \"\\nTorque integral=\",integral,\"[N-m-sec]\\n\"\n",
"\n",
"plot(1000*np.array(t),i)\n",
"xlabel('time [msec]')\n",
"ylabel('Phase current [A]')\n",
"title('(a) phase-1 current profile')\n",
"grid()\n",
"show()\n",
"plot(1000*np.array(t),Torque)\n",
"xlabel('time [msec]')\n",
"ylabel('Torque [N-m]')\n",
"title('(b) torque profile')\n",
"grid()\n",
"show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n",
"i1 ="
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 100*t/(51.1872396234976*t + 0.005) \t, (where round(16.2934044186179*pi,2) = 51.1 )\n",
"\n",
"i2 = (-200*t + 0.75)/(-51.1*t + 0.2605) \n",
"\n",
"\n",
"Positve torque integral = 0.000456384094483 [N-m-sec]\n",
"\n",
"Torque integral= 0.000335463884625 [N-m-sec]\n",
"\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"WARNING: pylab import has clobbered these variables: ['fmod', 'cosh', 'sinh', 'trunc', 'tan', 'gamma', 'degrees', 'radians', 'sin', 'expm1', 'ldexp', 'isnan', 'frexp', 'ceil', 'copysign', 'cos', 'tanh', 'fabs', 'sqrt', 'hypot', 'log', 'log10', 'pi', 'log1p', 'floor', 'modf', 'exp', 'isinf', 'e']\n",
"`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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cS9cJbpGRkZw9e7bCO3e3nBxITISuXY05vlWubbZCTitkBMnpapXN+Yc/QEGB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\ntWoxZMgQNmzYUGy7ioQ2e1e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7925q1Sr+X7G8QieEXmSMQVQbdevW5cqVKxX+uaI34EaN\nGnHjjTeyfv16+/dLG2OoqKysLDIzM+nXrx/z5s1j7969APTq1Yt3333Xvl3R8Xr37m1fOhkgIyPD\nJTmEcIYUBlFtBAYGEh4eTvv27Xn22Wex2Wz2q3eu/bro+bVfFz1fsWIF8+bNIywsjA4dOjg96Fva\nvoueZ2Rk0LdvXyIiIoiKiuL1118H4N1337XfAKZDhw4sWLAAgJkzZ3Ly5Enat29PeHg4W7ZsqUSL\nCFE5cj8GISrolVdewcfHhylTphhy/B49ejBv3jwiIyMNOb6o/uSMQYgK8vHx4b333jNsgltSUpLD\nOIYQriZnDEIIIRzIGYMQQggHUhiEEEI4kMIghBDCgRQGIYQQDqQwCCGEcPD/ATnLc2SOfyyJAAAA\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x388b510>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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cObLQ8nvvvafLvoQQxZOehvudOQMTJ8Lq1dqhzfPmga+v0alcU+qcxocffsjX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\n",
"text": [
"<matplotlib.figure.Figure at 0x3a36f90>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 8.4, Page number: 433"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"%pylab inline\n",
"\n",
"#Variavle declaration:\n",
"rpm=2500 #rpm of motor\n",
"\n",
"\n",
"#Calculations & Results:\n",
"#For part (a):\n",
"theta=[0]*12\n",
"i=[0]*102\n",
"lambda1=[0]*102\n",
"for m in range(1,11,1):\n",
" theta[m-1]=10*(m-1)\n",
" for n in range(1,102,1):\n",
" i[n-1]=30*(n-1)/100\n",
" lambda1[n-1]=i[n-1]*(0.005+0.09*((90-theta[m-1])/90))*(8/(i[n-1]+8))\n",
"\n",
" \n",
" plot(i,lambda1,'.')\n",
" \n",
" if m==1:\n",
" hold(True)\n",
" \n",
"xlabel('current [A]')\n",
"ylabel('Lambda [Wb]')\n",
"title('Family of lambda-i curves as theta_m varies from 0 to 90 degrees') \n",
"annotate('theta_m=0 deg',xy=(6,0.03))\n",
"annotate('theta_m=0 deg',xy=(8,0.5))\n",
"\n",
"\n",
"#for part (b):\n",
"lambdamax=25*(0.005+0.09*(8/(25+8)))\n",
"AreaWnet=0\n",
"AreaWrec=0\n",
"deli=0.25\n",
"for n in range(1,102,1):\n",
" i[n-1]=25*(n-1)/100\n",
" AreaWnet=AreaWnet + deli*i[n-1]*(0.09)*(8/(i[n-1]+8))\n",
" AreaWrec=AreaWrec + deli*(lambdamax-i[n-1]*(0.005+0.09*(8/(i[n-1]+8))))\n",
"\n",
"Ratio=(AreaWnet+AreaWrec)/AreaWnet\n",
"print \"part (b): Ratio =\", round(Ratio,2)\n",
"\n",
"#for part(b):\n",
"rps=rpm/60\n",
"T=1/rps\n",
"Pphase=2*AreaWnet/T\n",
"Ptot=2*Pphase\n",
"print \"part (c): AreaWnet =\", round(AreaWnet,2),\"Joules\"\n",
"print \"Pphase =\",round(Pphase),\"W\",\"\\tPtot =\",round(Ptot),\"W\\n\"\n",
"plot(AreaWrec=0.7,AreaWnet=25)\n",
"grid()\n",
"show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n",
"part (b): Ratio ="
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1.55\n",
"part (c): AreaWnet = 9.91 Joules\n",
"Pphase = 825.0 W \tPtot = 1651.0 W\n",
"\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"WARNING: pylab import has clobbered these variables: ['power', 'random', 'fft', 'linalg', 'info']\n",
"`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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arSaffvqpYN+uu+46sn//fkIIIc888wxJSkrq8ZzywQ6pTZvmmRCaN8M2LkJh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\naNR1qM4+wHTq0MnJCb6+vgCA6dOnw9/fH7dv3zaJOtRkG2A69afY9PD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"text": [
"<matplotlib.figure.Figure at 0x3a59f50>"
]
}
],
"prompt_number": 4
}
],
"metadata": {}
}
]
}
|