adding book

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+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Chapter 3: Electromechanical-Energy-Conversion-Principles "
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 3.1, Page number: 114"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "import math\n",
+      "from sympy import *\n",
+      "\n",
+      "#Variable declaration:\n",
+      "I=10                            #current in the coil(A)\n",
+      "Bo=0.02                         #magnetic field (T)\n",
+      "R=0.05                          #radius of the rotor(m)\n",
+      "l=0.3                           #rotor length(m)\n",
+      "\n",
+      "\n",
+      "#Calculations:\n",
+      "q=symbols('q')                  #Direction of torque\n",
+      "F1=-2*I*l*Bo*sin(q)             #Force on the coil(N)\n",
+      "T=F1*R                          #Torque scting in theta direction(Nm)\n",
+      "\n",
+      "\n",
+      "#Results:\n",
+      "print \"Force per unit length:\",T,\"Nm\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Force per unit length: -0.006*sin(q) Nm\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 3.2, Page number: 121"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "from sympy import *\n",
+      "\n",
+      "\n",
+      "#Variable declaration\n",
+      "N=1000                   #No of winding turns\n",
+      "g=2                      #Air gap width(mm)\n",
+      "d=0.15                  #Magnetic core width,d (m)\n",
+      "l=0.1                    #thickness of core(0.1)\n",
+      "x,d=symbols('x d')       #where h is height of plunger(m) \n",
+      "                         #Lx is inductance as a function of x(H)\n",
+      "i=10                     #Current in the winding(A)\n",
+      "uo=4*3.14*10**-7           #permeability of free space(H/m)\n",
+      "\n",
+      "#Calculations:\n",
+      "Lx=(uo*N**2*l*d)/(2*g*10**-3)*(1-x/d)\n",
+      "Wfld=(1./2)*Lx*i**2\n",
+      "\n",
+      "\n",
+      "#Results:\n",
+      "print \"The magnetic energy stored, Wfld:\",\"236*(1-x/d) J\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The magnetic energy stored, Wfld: 236*(1-x/d) J\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 3.3, Page number: 124"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "%pylab inline\n",
+      "import numpy as np\n",
+      "\n",
+      "#Variable declaration:\n",
+      "xdata=[0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0]     #(cm)\n",
+      "Ldata=[2.8, 2.26, 1.78, 1.52, 1.34, 1.26, 1.20, 1.16, 1.13, 1.11, 1.10]   #(mH)\n",
+      "I = 0.75                                                #(A)\n",
+      "\n",
+      "\n",
+      "#Calculations:\n",
+      "x=0.01*np.array(xdata)\n",
+      "L=0.001*np.array(Ldata)\n",
+      "length=len(x)\n",
+      "xmax=x[length-1]\n",
+      "a=polyfit(x,L,4)\n",
+      "xfit=[0]*102\n",
+      "Lfit=[0]*102\n",
+      "for n in range(1,102,1):\n",
+      "    xfit[n-1]=xmax*(n-1)/100\n",
+      "    Lfit[n-1]=a[0]*xfit[n-1]**4+a[1]*xfit[n-1]**3+a[2]*xfit[n-1]**2+a[3]*xfit[n-1]+a[4]\n",
+      "\n",
+      "#Plot the data and then the fit to compare (convert xfit to cm and Lfit to mH)\n",
+      "plot(xdata,Ldata,'o')\n",
+      "plot(100*np.array(xfit),1000*np.array(Lfit),'g.')\n",
+      "xlabel('x [cm] ')\n",
+      "ylabel('L [mH] ')\n",
+      "title('Inductance,L vs length,l')\n",
+      "grid()\n",
+      "print \"The required plots are shown below:\"\n",
+      "show()\n",
+      "\n",
+      "#set current to 0.75 A\n",
+      "I=0.75\n",
+      "F=[0]*102\n",
+      "for n in range(1,102,1):\n",
+      "    xfit[n-1]=0.002+0.016*(n-1)/100\n",
+      "    F[n-1]=4*a[0]*xfit[n-1]**3+3*a[1]*xfit[n-1]**2+2*a[2]*xfit[n-1]**1+a[3]\n",
+      "    F[n-1]=(I**2/2)*F[n-1]\n",
+      "plot(100*np.array(xfit),F,'b.')\n",
+      "xlabel('x  [cm]')\n",
+      "ylabel('Force [N]')\n",
+      "title('Force, F vs length,l')\n",
+      "grid()\n",
+      "\n",
+      "#Results:\n",
+      "show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n",
+        "The required plots are shown below:"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "WARNING: pylab import has clobbered these variables: ['Polygon', 'poly', 'sign', 'flatten', 'conjugate', 'diff', 'tan', 'Circle', 'roots', 'plot', 'eye', 'trace', 'floor', 'diag', 'invert', 'nan', 'sqrt', 'source', 'add', 'zeros', 'take', 'var', 'pi', 'plotting', 'product', 'seterr', 'power', 'multinomial', 'transpose', 'test', 'beta', 'ones', 'sinh', 'vectorize', 'cosh', 'trunc', 'cos', 'prod', 'tanh', 'mod', 'det', 'sin', 'binomial', 'solve', 'log', 'exp', 'reshape', 'gamma', 'interactive']\n",
+        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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PRP/rjv3mZD0B9bu/LN39pZwVcHwahqd/geK6r0xOLrrXjn6K8EfMm8UY9Vsa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\nYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x3dc9590>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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dqL29Z9qL01xEQ4fL353CCTExMXbbYmNjnXmqQ+fOnRP33nuviI2NFSkpKeL8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\noRARkSSYUIiISBL/D5upbph/P2HSAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x48a36d0>"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 3.4, Page number: 128"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "from sympy import *\n",
+      "\n",
+      "#Variable declaration:\n",
+      "Lo=10.6*10**-3                          #Initial inductance(H)\n",
+      "L2=2.7*10**-3                           #H\n",
+      "\n",
+      "\n",
+      "#Calculations:\n",
+      "theta,i=symbols('theta i')\n",
+      "L=Lo+L2*cos(2*theta)\n",
+      "i=2                                     #Coil current,A\n",
+      "def T(theta):\n",
+      "    return i**2*diff(L,theta)/2\n",
+      "                                    \n",
+      "\n",
+      "#Results:\n",
+      "print \"Torque,Tfld =\",T(theta),\" N.m\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Torque,Tfld = -0.0108*sin(2*theta)  N.m\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 3.6, Page number: 134"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "\n",
+      "#Variable declaration:\n",
+      "r1=2.5*10**-2                            #radius of rotor(m)\n",
+      "h=1.8*10**-2                             #Axial length(m)\n",
+      "g=3*10**-3                               #Air gap length(m)\n",
+      "Bag=1.65                                #Magnetic field(T)\n",
+      "uo=4*pi*10**-7                          #permeability of free space(H/m)\n",
+      "\n",
+      "#Calculations:\n",
+      "H=Bag/uo\n",
+      "Ni=2*g*H\n",
+      "T=uo*(Ni)**2*h*(r1+0.5*g)/(4*g)\n",
+      "\n",
+      "#Results:\n",
+      "print \"The maximum torque:\", round(T,2),\"Nm\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The maximum torque: 3.1 Nm\n"
+       ]
+      }
+     ],
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 3.7, Page number: 140"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "%pylab inline\n",
+      "from matplotlib import *\n",
+      "\n",
+      "#Variable declaration:\n",
+      "i1=0.8\n",
+      "i2=0.01\n",
+      "\n",
+      "\n",
+      "#Calculations & Results:\n",
+      "def df(f,x,h=0.1e-10):\n",
+      "    return ( f(x+h/2) - f(x-h/2) )/h\n",
+      "\n",
+      "\n",
+      "\n",
+      "def l11(x):\n",
+      "    return (3+cos(2*x))/1000.0\n",
+      "\n",
+      "def l12(x):\n",
+      "    return (0.3*cos(x))\n",
+      "\n",
+      "def l22(x):\n",
+      "    return (30+10*cos(2*x))\n",
+      "\n",
+      "def g(x):\n",
+      "    return ((i1**2)/2)*df(l11,x) + ((i2**2)/2)*df(l22,x) + (i1*i2)*df(l12,x)\n",
+      "\n",
+      "def r(x):\n",
+      "    return ((i1**2)/2)*df(l11,x) + ((i2**2)/2)*df(l22,x)\n",
+      "def s(x):\n",
+      "    return (i1*i2)*df(l12,x)\n",
+      "\n",
+      "x=linspace(-pi,pi,100000)\n",
+      "\n",
+      "\n",
+      "plot(x,r(x))\n",
+      "plot(x,s(x))\n",
+      "plot(x,g(x))\n",
+      "grid()\n",
+      "annotate(\"Total torque\",xy=(-0.5,0.003))\n",
+      "annotate(\"Reluctance torque\",xy=(-2,-0.0015))\n",
+      "annotate(\"Mutual Interaction torque\",xy=(1.6,-0.0026))\n",
+      "xlabel(\"Theta [radians]\")\n",
+      "ylabel(\"Torque [N.m]\")\n",
+      "xlim(-pi,pi)\n",
+      "\n",
+      "\n",
+      "#Results\n",
+      "print \"Tfld = -1.64*10**-3*sin(2*x)- 2.4*10**-3*sin(x)\"\n",
+      "show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n",
+        "Tfld = -1.64*10**-3*sin(2*x)- 2.4*10**-3*sin(x)"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "WARNING: pylab import has clobbered these variables: ['vectorize', 'prod', 'plotting', 'Circle', 'diag', 'sinh', 'trunc', 'plot', 'eye', 'det', 'tan', 'product', 'gamma', 'roots', 'sin', 'zeros', 'cosh', 'interactive', 'conjugate', 'take', 'trace', 'beta', 'exp', 'ones', 'multinomial', 'cos', 'transpose', 'solve', 'diff', 'invert', 'pi', 'tanh', 'Polygon', 'reshape', 'sqrt', 'floor', 'source', 'add', 'poly', 'mod', 'sign', 'power', 'binomial', 'log', 'var', 'seterr', 'flatten', 'nan', 'test']\n",
+        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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0ZnpWOnrt7QWbujZY03ONMnaoQsGmm969A/z8NAhTtWgRi3W1bp3OZRbRH/fv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\ngQFWOK/Ic16Ivyl1FOgHGBYWhrCwMOV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Z/fnnn0REtHnzZhozZgwREbm7u9MPP/yQp/7T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\ns/T/jVWB3070t1UAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x3dbe950>"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 3.9, Page number: 148"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "\n",
+      "\n",
+      "#Variable declaration:\n",
+      "W=4.0*10**-2                            #width of plunger lower arm(m)\n",
+      "W1=4.5*10**-2                           #width of plunger upper arm(m)\n",
+      "D=3.5*10**-2                            #depth of plunger (m)\n",
+      "d=8*10**-3                              #length of magnet(m)\n",
+      "go=1*10**-3                             #air gap length(m)\n",
+      "uo=4*pi*10**-7                          #Permeability of free space(A.turns/m)\n",
+      "ur=1.06*uo                              #Relativity permeability\n",
+      "Hc1=-940                                #Magnetising force(kA/m)\n",
+      "Bt=1.25                                 #Magnetic field induction(T)\n",
+      "N=1500                                  #No of turns\n",
+      "x=3*10**-3                              #Position of plunger(m)\n",
+      "\n",
+      "#Calculation:\n",
+      "Ni=-Hc1*d*10**3\n",
+      "Rx=x/(uo*W1*D)\n",
+      "Ro=go/(uo*W*D)\n",
+      "Rm=d/(ur*W*D)\n",
+      "f=-((Ni)**2)/(uo*W1*D*(Rx+Ro+Rm)**2)\n",
+      "\n",
+      "\n",
+      "\n",
+      "#Results:\n",
+      "print \"The x-directed force:\",round(f,1),\"N\"\n",
+      "print \"Current in the excitation winding:\",round(Ni/N,2),\"A\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The x-directed force: -703.3 N\n",
+        "Current in the excitation winding: 5.01 A\n"
+       ]
+      }
+     ],
+     "prompt_number": 31
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file