{ "metadata": { "name": "", "signature": "sha256:79bb0da696514eb28b3362f41a655c22b20a6f58bcc42fa9af6901509701d1d3" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Chapter5-Aircraft Engine INlets and Nozzles" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Ex1-pg251" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#calcualte overspeed mach no\n", "print(\"Example 5.1\")\n", "Md=1.5\n", "##From isentropic table,\n", "gm=1.4 ##gamma\n", "A=1.176 ##A=A1/Ath=A1/Acr\n", "##for same A, from isentropic table for M<1\n", "My=0.61\n", "##for My=0.61, from normal shock table\n", "Mx=1.8\n", "Mos=Mx\n", "print'%s %.1f %s'%(\"Overspeed Mach no.\",Mos,\"\")\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Example 5.1\n", "Overspeed Mach no. 1.8 \n" ] } ], "prompt_number": 10 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Ex2-pg252" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "#calculate contractio ratio and the maximum pressure recovery\n", "print(\"Example 5.2\")\n", "Md=2.65\n", "Mx=Md\n", "##for Mx=2.65, from normal shock table \n", "My=0.4996\n", "M1=My\n", "##from isentropic table for M1=0.5, \n", "A=1.34\n", "##for Md=2.65, from isentropic table (A=A1/Acr)\n", "A1=3.036\n", "Af=A1/A\n", "##from isentropic table Af, \n", "Mth=2.35\n", "##for Mth=2.35, from normal shock table\n", "p=0.5615 ##p=pty/ptx\n", "print'%s %.2f %s'%(\"Maximum total pressure recovery:\",p,\"\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Example 5.2\n", "Maximum total pressure recovery: 0.56 \n" ] } ], "prompt_number": 11 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Ex3-pg253" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#calculate inlet design contraction ratio and throat mach no\n", "print(\"Example 5.3\")\n", "Md=3.3 ##from isentropioc table \n", "A=5.629 ## A=A1/Acr=A1/Ath\n", "Mx=Md ##from normal shock table \n", "My=0.4596\n", "M1=My\n", "##from isentropic table \n", "A11=1.425\n", "pt=((1./A11-1./A)/(1./A))*100.\n", "Af=A/A11\n", "##for Af=3.95, from isentropic table for M>1\n", "M1th=2.95\n", "print'%s %.2f %s'%(\"Inlet design contraction ratio A1/Ath:\",A,\" \")\n", "print'%s %.2f %s'%(\"The % opening of the throat:\",pt,\" \")\n", "print'%s %.3f %s'%(\"Throat Mach no.:\",M1th,\"\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Example 5.3\n", "Inlet design contraction ratio A1/Ath: 5.63 \n", "The % opening of the throat: 295.02 \n", "Throat Mach no.: 2.950 \n" ] } ], "prompt_number": 13 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Ex4-pg256" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "#calculate inlet pressure recovery with the shock at the lip\n", "print(\"Example 5.4\")\n", "M0=1.4\n", "##from normal shock table \n", "p=0.9582 ##p=pt2/pt0\n", "M1=M0\n", "##from isentropic table:\n", "A=1.115 ##A=A1/Acr\n", "A11=1.1 ##A11=Ax/A1\n", "Af=A11*A\n", "##from normal shock table for M>1\n", "Mx=1.56\n", "##from normal table\n", "p1=0.91 ##p=pt2/pt0\n", "p2=p\n", "print'%s %.2f %s'%(\"(a)The best backpressure :\",p,\"\")\n", "print'%s %.3f %s'%(\"(b)The supercritical mode inlet total pressure recovery:\",p1,\"\")\n", "print'%s %.2f %s'%(\"(c)Inlet pressure recovery in subcritical mode with 10% spillage:\",p2,\"\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Example 5.4\n", "(a)The best backpressure : 0.96 \n", "(b)The supercritical mode inlet total pressure recovery: 0.910 \n", "(c)Inlet pressure recovery in subcritical mode with 10% spillage: 0.96 \n" ] } ], "prompt_number": 14 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Ex5-pg257" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#calculate total pressure recovery of this inlet \n", "import math\n", "print(\"Example 5.5\")\n", "##th=theta and b=beta.\n", "gm=1.4 ##gamma\n", "##OBLIQUE SHOCK 1\n", "M0=2.\n", "th=8. ##degree\n", "##from theta-beta-M chart,\n", "b1=37. ##degree\n", "Mn1=M0*math.sin(b1/57.3)\n", "p1=0.993 ##p=pt2/pt1\n", "Mn2=((2.+(gm-1.)*Mn1**2.)/(2.*gm*Mn1**2.-(gm-1.)))**(1/2.)\n", "M2=Mn2/math.sin(b1-th/57.3)\n", "##OBLIQUE SHOCK 2\n", "M0=M2\n", "th=12.\n", "##from oblique shock chart,\n", "b2=48.7\n", "Mn1=M0*math.sin(b2/57.3)\n", "p2=0.978\n", "Mn2=((2.+(gm-1.)*Mn1**2)/(2.*gm*Mn1**2.-(gm-1.)))**(1/2.)\n", "M3=Mn2/math.sin(b1-th/57.3)\n", "##NORMAL SHOCK\n", "M0=M3\n", "b3=90.\n", "pNS=0.977\n", "\n", "Po=p1*p2*pNS\n", "print'%s %.3f %s'%(\"Total pressure recovery:\",Po,\"\")\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Example 5.5\n", "Total pressure recovery: 0.949 \n" ] } ], "prompt_number": 15 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Ex6-pg271" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "#calculate percent increase in gross thurst \n", "print(\"Example 5.6\")\n", "M9=1. ## Mach no.\n", "p=1/8. ##p=p0/pt7\n", "gm=1.3 ##gamma\n", "V9cd=(2.*(1.-p**((gm-1.)/gm)))**(1/2.)\n", "px=p*((gm+1.)/2.)**(gm/(gm-1.))\n", "V9c=(2.*(gm-1.)/(gm+1.))**(1/2.)\n", "FR=(V9cd/V9c)/(1.+(1.-px)/gm)\n", "pr=(FR-1.)*100./1.\n", "print'%s %.3f %s'%(\"% increase in gross thrust:\",pr,\" \")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Example 5.6\n", "% increase in gross thrust: 7.304 \n" ] } ], "prompt_number": 16 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Ex7-pg273" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#calculate velocites at various point and coefficent\n", "import math\n", "print(\"Example 5.7\")\n", "p98=0.95 ##p98=pt9/pt8\n", "p87=0.98 ##p98=pt8/pt7\n", "p70=8. ##p70=pt7/pt0\n", "p97=8. ##p97=pt9/pt7\n", "Cp=1243.7 ##specific heat in J/kg.K\n", "gm=1.3 ##gamma\n", "Tt9=900. ##Total temp. of the gas entering a C-D nozzle\n", "Tt7=Tt9\n", "p90=1. ##p90=p9/p0\n", "p99=p98*p87*p70*p90 ##p99=pt9/p9\n", "M9=(2./(gm-1.)*(p99**((gm-1.)/gm)-1.))**(1/2.) ##exit mach no.\n", "T9=Tt9/(1.+(gm-1.)*M9**2/2.) ##The nozzle exit static temp.\n", "a9=((gm-1.)*Cp*T9)**(1/2.) ##speed of sound in exit plane\n", "V9=a9*M9 ##exit velocity\n", "V9s=(2.*Cp*Tt7*(1.-p97**-((gm-1.)/gm)))**(1/2.)\n", "p89=p87*p70*p90 ##p89=pt8/p9\n", "V9i=(2.*Cp*Tt7*(1.-p89**-((gm-1.)/gm)))**(1/2.)\n", "Cv=V9/V9i\n", "print'%s %.1f %s'%(\"(a)V9 in\",V9,\" m/s:\")\n", "print'%s %.1f %s'%(\"(b)V9s in\",V9s,\" m/s:\")\n", "print'%s %.1f %s'%(\"(c)V9i in \",V9i,\"m/s:\")\n", "print'%s %.3f %s'%(\"(d)The velocity coefficient Cv:\",Cv,\"\")\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Example 5.7\n", "(a)V9 in 911.1 m/s:\n", "(b)V9s in 923.7 m/s:\n", "(c)V9i in 920.2 m/s:\n", "(d)The velocity coefficient Cv: 0.990 \n" ] } ], "prompt_number": 18 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Ex8-pg275" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline\n", "print \"Example 5.8\"\n", "#calculate and graph the divergnece correction factor \n", "import math\n", "import numpy\n", "import matplotlib\n", "from matplotlib import pyplot\n", "alfa=0 #alfa=cone half angle\n", "dx=numpy.linspace(0,44,146)\n", "x=numpy.zeros(146)\n", "count=0;\n", "for alfa in dx:\n", "\tCa=(1+math.cos(alfa*math.pi/180.))/2.; #Flow angularity loss coefficient\n", "\tx[count]=Ca;\n", "\tcount=count+1;\n", "#disp(Ca,\"Divergence correction factor Ca:\")\n", "\n", "pyplot.plot(dx,x)\n", "\n", "pyplot.title(\"Flow convergence loss in a conical nozzle\")\n", "pyplot.xlabel(\"Cone half-angle\")\n", "pyplot.ylabel(\"Flow angularity loss coefficient\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n", "Example 5.8\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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onHNFqUUL+NOfoKwszI3xzDNpR+QaSl4DCUrqCuxGGCp8gpl9kHRg+fC7oZwr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\n0risf4JxOedcs9OtW0gY++4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"text": [ "" ] } ], "prompt_number": 1 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Ex9-pg276" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Example 5.9\"\n", "import math\n", "import numpy\n", "import matplotlib\n", "from matplotlib import pyplot\n", "alfa=0.1\n", "dx=numpy.linspace(0.1,44,88)\n", "x=numpy.zeros(88)\n", "g1=numpy.zeros(88)\n", "count=0\n", "g2=numpy.zeros(88)\n", "gc1=0;\n", "for alfa in dx:\n", "\tCa=(math.sin(alfa*math.pi/180.))/(alfa*math.pi/180.)\n", "\tCac=(1+math.cos(alfa*math.pi/180.))/2.\n", "\tx[count]=Ca\n", "\tcount=count+1;\n", "\tg1[gc1]=Cac;\n", "\tgc1=gc1+1;\n", "\n", "\n", "pyplot.plot(dx,g1)\n", "pyplot.plot(dx,x)\n", "pyplot.legend([\"Conical\",\"2D-CD\"])\n", "pyplot.xlabel(\"Divegent flap angle or Cone half-angle(degree)\")\n", "pyplot.ylabel(\"Flow angularity loss coefficient\")\n", "pyplot.title(\"Divergent loss of a conical nozzle and a 2D-CD nozzle\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Example 5.9\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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pppvo06cPNWrUOGdGPF+io6NRVQ4ePEj+/OcGsjxw4AAJCQmUzUBAtNTyDTb+\njLO40HfFc4kNcAQfwwgsBQq4Po3bb3cutxdf7GoaTzzhZvoLJs3KNaNZuWbsPrqbdxa/wxUfXUHV\n4lW5t9m9dK/V3YIZZpDzeckHgoSEBHr37k2+fPkYPXp0qmm3bduGiFC0aFGeeeYZnn32WQB69+7N\nSy+9REREBDt27KBGjfR51fnmm9mk2AwlIo+KyGGgnogcTlyA3cCUTFNoGOdB4cLOQKxb537XqweD\nB8PBg8Evu3TB0jx26WNsvG8jdzW9i5HzR1Lt1Wq8OPdFDv6VCQKMgKGq9O/fnz179vD5558TGRmZ\navpJkybRuHFj8ufPz6OPPnq2o/qNN94gf/78tGjRgokTJ6Zbh2++mU5anRrAc+ntCMmshSzUIWaE\nB1u3qvbvr1qqlHO9PX48c8tftG2R3vT5TVrsuWJ6z7R7dP2+9ZkrIIwJ5+d54MCB2rx5cz1y5Mg5\n2307ohMSEjQ+Pl6HDRum+fLl02+//TbF/ObOnatRUVE6YsQI3bt3r6qqLlu2TK+//vrzyjcpKV1T\nMtDB7e9LuRwu3MaliUt6CwrGEs43lxHerF7tvKYqVVIdP171zJnMLT/+z3gd/N1gLflCSe0xoYf+\ntPknTUhIyFwRYUa4Ps+bNm1SEdH8+fOfHfcQFRWlH330kcbFxWlERIRGRUVpwYIFNTo6Wnv16qUL\nFixIM9+FCxdqly5dtEiRIlq8eHG9+OKLdfz48arqjEVG8/UlkMYizRHcIvI8cB2wGjgbOFpVrwxk\nDScj2Ahu43z58UcX8fbkSXjxRdchnpkcPXmU95a9x6j5oyhZoCQPtniQq2pfRWRE6s0c2REbwR14\nMjXch4j8BtTTJLPUhQNmLIxAoAoTJ8LDD8OFF8ILL7gQ6ZnJmYQzTFk3hRFzR7D76G4ebPEgfRr2\nyVFTwpqxCDyZPfnR74BFTzOyLSLQqxesWQOXXuqi3d5zT2DDoqdFZEQkPWv3ZG7/ubzf431m/j6T\nmFdiGD57uI3XMMICf4zFcWCZiLwtIq95y6vBFmYYmU3evM7ddu1aSEiA2rXhtdfg1Km0jw0krSq2\n4svrv2R2n9lsPriZaq9W48GZDxJ/KD7tgw0jSPjTDNXH+5mYUHCdI+8nf0TmYc1QRjBZuRIeeAC2\nbXNjNTp2DI2O+EPxjJo3inHLxtGzVk8eaf0I1UtUD42YIGLNUIEn00OUi0gBoKKqrk1P5sHGjIUR\nbFRh6lSbumClAAAgAElEQVQYNMiN0XjpJahSJTRa9h/fz2sLXmP0otG0r9yewa0H06BMg9CICQJm\nLAJPpvZZiEg33HSnM7z1RiJig/KMHIGIi2a7ciU0a+aWxx8P7Nzg/lI8f3GGxg7lj3v/oGl0U7p8\n2IVuH3dj4baFmS/GyHH40wy1BGgHzFLVRt62lap6YaoHZgJWszAym/h452o7bx6MGgU9ejiDEgr+\nOv0XY5eO5fmfn6dWyVo8funjtK7YOjRiAoCE6kJmczLTdXaBql4sIkt9jMUKVQ3i/GT+YcbCCBWz\nZsFdd7n5wF991c0RHipOnjnJ+OXjeeanZ6hUpBJD2wylTUyb0Akywp5guc6uEpGbgFwiUl1EXgPm\nZkihYWQT2raF5cuhfXto0QKGDoW//gqNljyReeh/UX/W3rWWWxrcQv8p/WnzXhtmbZwVGkFGtsSf\nmkVB4DEg0RdkJvCUqobo0fgbq1kY4UB8PNx/PyxbBq+/Dp06hVbP6YTTfPTrRzw15ynKFSrHk7FP\nWk3DOIegeUNlFBHpDLwMRALvqurzSfYXA8YCVYC/gH6qusrbVxR4F6iLc9vtp6rzkxxvxsIIG6ZN\ng7vvhqZNnattBqYrCCinE07z4YoPGT5nOJWKVGJ42+FZuk/DCBwBNRYi8oqq3iciU5PZraraLQ0x\nkcA6oAOwDVgE3KCqa3zSjAAOqepTIlITeF1VO3j73gdmq+pYbw6Ngqr6Z5IyzFgYYcWxY/Cf/8Db\nb8NTT7n5NCJCPB/lqTOnGL9iPMNnD6dWyVoMbzucZuWahVaUEVICbSwaq+piEYlNZreq6uw0xLQA\nhqpqZ2/9Ee/A53zSfIULgf6Tt74BaAGcBJaqaqoe7WYsjHBl5UoYONCN03j7bRdzKtScPHOSMUvG\n8J8f/8NFZS/i6XZPU/+CkPupGCEgoB3cqrrY+/kL8KOqxqlqHPCjty0tygFbfdbjvW2+LAeuAhCR\nZkAloDxQGdgjIuNEZImIvOMNDDSMLMGFF7qItrfc4jrDn3gCToQ4FGeeyDzc2fRO1t+znrYxbek4\nviM3fn4j6/etD60wI0vgl+ss0F5Vj3jrhYCZqtoyjeOuBjqr6gBv/WbgYlW9xydNIeAVoBHwK1AL\nuA0XuHAe0FJVF4nIy7jmqieSlKFDhw49ux4bG0tsbKw/520Ymca2ba4vY+1aePddaNUq1IocR04e\n4ZX5rzBq/ih61urJ0NihlC9cPtSyjCAQFxdHXFzc2fUnn3wyKOMslqlqw7S2JXNcc2CYTzPUYCAh\naSd3kmM2AvWAKGCeqlb2trcGHlHVK5Kkt2YoI8vwxRcumm3PnvDss1CoUKgVOfYf388LP7/AO0ve\noX+j/jzS+hGK5y8eallGEAnWOIujItLYp5AmuEi0afELUF1EYkQkD24CpXPChIhIEW8fIjIA16F9\nRFV3AltFJHE28w7AKj/KNIyw5aqrXF/GsWMuztS334ZakaN4/uI81+E5fr3zVw6fOEzN0TV55sdn\nOHryaKilGWGEPzWLpsAEYIe3qSxwnaqm2W8hIl3423V2jKo+KyIDAVT1La8T/D2ca+xKoH+ix5OI\nNMC5zubBzanR17yhjOzCN984T6l27WDkSChaNNSK/mb9vvUMmTWEn7b8xNA2Q+nXqB+5InKFWpYR\nQIIZdTYPUBP3Ul+nqpkc4T95zFgYWZnDh+GRR2DKFOcx1aVLqBWdy6Jti3j4u4fZfng7z7Z/lh61\nelj8pmxCoF1n26vq915HteLmscD7jap+cT5iA4EZCyM78MMP0L+/85oKt1qGqjLz95k89O1DFMlX\nhBGXjaB5+eahlmWcJ4Hus7jU+3ult1zhLYnrhmEEgHbtYMUKyJcP6td3TVThgojQuVpnlg5cSr+G\n/ej1WS96fdaL3/f/HmppRiaTWs3iflV9WURaJw6aCzesZmFkN777ztUyunaFESMgKirUis7l2Klj\nvDz/ZUbOG8ktDW5hyKVDzHMqCxLomkVf7+9rGZdkGEZ66NDB1TJOnIAGDdzAvnCiQO4CPHrJo6z6\nv1UcO3WMWqNr8fL8lzl55mSopRlBJrWaxcdAE9yo66R1TrX5LAwjuEyZAnfcAb17w/DhkDdvqBX9\nk9V7VvOvb/7Fhv0beLHji1xZ40rrBM8CBNwbSkTK4EKSd+PvDm4AVHVTBjQGFDMWRnZnzx7nYvvH\nH/DBB258Rjgyc8NMBn0ziDJRZRjZcWS2mhs8OxLQZigR+d4bHDdTVTer6ibf5XzFGoaRNqVKuZHf\n99//95iMhIRQq/onnap1Yvkdy7m69tV0/KAjA6cOZM/RPaGWZQSQ1PosyopIK6CbiFwkIo29vxeJ\nyEWZJdAwcjoi0LcvLFwIn38OHTu6eFPhRq6IXPxf0/9j7V1ryZ87P3XeqMNLc1+y/oxsQmp9Fr2A\n/kArkokyq6ptgystbawZyshpnD7t4kqNHu1m5bvmmlArSpm1e9fywMwH2HhgIy93fpnO1TqHWpLh\nEZQR3CLyhKoOPy9lQcKMhZFTWbAAbroJ2rSBV14JPxfbRFSVr9d/zQMzH6B2ydqM7DSSasWrhVpW\njidYgQSfFpHeIvKEV0hFb+4JwzBCxMUXw9KlcOYMNG4MixenfUwoEBGuqHEFK+9cSasKrWj+bnMe\n/f5RC1KYBfHHWLyBm73uRm/9iLfNMIwQUqgQvPceDBsGnTvDiy+GZ+c3QN5ceXm49cOsuHMFW/7c\nQu3Xa/Ppqk+xloGsgz/NUEtVtVHiX2/bclUNuW+cNUMZhmPTJrjxRihcGN5/Hy64INSKUmfO5jnc\nM/0eSuQvweiuo6lTqk6oJeUogtUMdVJEIn0KKQWE6feLYeRMYmJg9my46CJo1MiFDQlnLq10KYtv\nX0zPWj1p814b/v3Nvzl84nCoZRmp4I+xeA2YBJQWkWeAn4Fng6rKMIx0kzs3PPMMjB8Pt94Kjz7q\nvKfClVwRubjn4ntYeedK9hzbQ5036ljTVBjj73wWtYH23ur3qromqKr8xJqhDCN5du92YUKOHYOP\nP4byWWBq7Z+2/MSdX99J2aiyjO46mholaqR9kJEhgtUMBZAXF+5DcDPXGYYRxpQuDdOnw+WXQ5Mm\nMG1aqBWlTeuKrVly+xI6V+tMyzEteWLWExw/5c8MzkZmkKaxEJH7gA+AUkBp4AMRuTfYwgzDOD8i\nItxMfBMnuoCEDz8Mp8JijsuUyR2Zm0EtBrHsjmWs2buGem/W45vfw2iCjxyMP95QvwLNVfWot14Q\nmK+qIQ9pZs1QhuEfe/e6ZqkjR2DCBChXLtSK/GPa+mncPe1umpVrxqhOoyhbqGyoJWULgtkMlZDC\n77QEdRaRtSKyXkQeTmZ/MRGZJCLLRWSBiNRNsj9SRJaKyFR/yzQM45+ULAlff+3m+W7SJLxm40uN\nrtW7svL/VlKlWBXq/7c+byx6gwQ1Z8xQ4E/NYhDQB/gC12fRA3hPVUelcVwksA7oAGwDFgE3+HaO\ni8gI4JCqPiUiNYHXVbVDkrIbA4VUtVsyZVjNwjDSSVycCxVy++3w+OOuuSorsGr3KgZ+NZAzeoa3\nr3ibeheEvHEjyxKUmoWqjsTNmncA2Af0SctQeDQDNnghzU8BE4DuSdLUBmZ55awDYrxxHIhIeaAr\n8C5J5tIwDCPjxMbCL7/A99+76Vv37g21Iv+oW7ouc/rOoV/DfrT/X3sGfzfYOsAzEX86uJsD61X1\nFVV9FfhdRC72I+9ywFaf9Xhvmy/Lgau8cpoBlYBEJ79RwL+xAYCGEXDKlnXGol49F1tq4cJQK/KP\nCIlgQOMBrLhzBRsPbqTem/X4/o/vQy0rR5DLjzT/BRr5rB9NZlty+NM+9BzwiogsBX4FlgIJInIF\nsFtVl4pIbGoZDBs27Ozv2NhYYmNTTW4Yhkfu3DBiBLRo4Vxsn37aNU1lhVlRy0SVYcI1E/j6t6/p\nN6UfbWPa8lLHlyhRoESopYUlcXFxxMXFnVce/vRZLFPVhkm2rUhrDm6vRjJMVTt764OBBFV9PpVj\nNgL1gcFAb+A0kA8oDHyuqrckSW99FoYRAH77DXr2hGbN4I03IH/+UCvyn8MnDjPkhyF8uvpTRnUa\nxXV1r7N5wNMgWPNZTML1K7yJ6zu4E2irqj3SOC4XroO7PbAdWMg/O7iLAMdV9aSIDABaqWqfJPm0\nAf6lqlcmU4YZC8MIEEeOwG23wfr1bka+mJhQK0of87bO47apt1GlWBXevPxNyhfOAsPWQ0SwXGfv\nwM2Wtw3X79AcuD2tg1T1NHA3MBNYDXyiqmtEZKCIDPSS1QF+FZG1QCfgvpSy80OnYRjnQVSUCw1y\n883QvHn4ByNMSosKLVhy+xIal21Mw/825L+//NfcbAOIX7GhwhWrWRhGcIiLgxtugAcfdEtWa9VZ\ntXsV/ab0o0DuArx75btULV411JLCimAOyjMMIwcRG+umbp0wwRmNo1lsYru6pesyt99crqxxJc3H\nNGfUvFGcSTgTallZGqtZGIaRIsePw513uilcv/wSKlcOtaL0s2H/Bm6bchsnz5xkbPex1CpZK9SS\nQo7VLAzDCCj588O4cdC/v3Ox/eGHUCtKP9WKV+OHW3/g5vo3c8m4S3jh5xc4nRDGE32EKf54Q90P\njAMO4UZTXwQ8oqozgy8vdaxmYRiZxw8/uKlbBw+Ge+/Nev0YABsPbOS2qbdx5OQRxnUfl2Oncw1W\nzaKfqv4JdASK48Y/PJcBfYZhZGHatYN582DsWOdie+JEqBWln8rFKvNd7+/o27Avl467lBd+fsH6\nMvzEH2ORaH0uB8ar6sog6jEMI4ypXBl+/hkOHnTGY+fOUCtKPyLCHU3uYNGARczYMINWY1uxdu/a\nUMsKe/wxFotF5BtcUL+ZIlIYi9dkGDmWqCj47DO47DI34nvJklAryhiVi1Xmu1u+o3f93rQe25pR\n80bZuIxU8KfPIgIXB+p3VT0oIiWAcqq6IjMEpob1WRhGaJk40XlLvfkmXHNNqNVknA37N9B3cl8i\nJIJx3cdRpViVUEsKKsHqs2gBrPMMRW9gCPBnRgQahpG9uOYamDkTBg2C4cMhq367VStejbhb4+he\nszvN3mnGW7+8hX2Inou/06rW95b3cB5R16pqm6CrSwOrWRhGeLBjB/ToAVWquA7wrBSIMClr9qyh\n96TelCpYijHdxhBdKDrUkgJOsGoWp703cg/cTHavA4UyItAwjOxJ2bIuRIgItGmTNTu+E6ldqjbz\n+s+jebnmNHqrEZ+s/CTUksICf2oWc4AZuNnyLgH2AMtUNeRzGlrNwjDCC1V46ikYMwamToX6qU5k\nEP78sv0Xek/qTaMyjXi96+sUy18s1JICQrBqFtcBJ3DjLXbiZrsbkQF9hmFkc0TgiSfg+eehfXv4\n+utQKzo/mkQ3YfHtiylZoCQN/tuA7/7IYqF4A4hfsaFEpAzQFBcqfKGq7g62MH+wmoVhhC/z58NV\nV8Ejj7gR31mdb37/hn6T+3F17at5rsNz5M+ddTtmglKzEJFrgQVAL+BaYKGI9MqYRMMwcgrNm8Pc\nufDWW85YnMniA6U7Vu3IijtXsPPoTpq804RlO5eFWlKm4k+fxQqgQ2JtQkRKAd+nNa1qZmA1C8MI\nfw4ehF69IG9eF/I8KirUis4PVeWDFR8w6JtBPNTyIR5s+SARkrVisgarz0JwndqJ7OPvECCGYRip\nUrQoTJvmPKYuuQS2bQu1ovNDROjdoDeLBixiym9T6PC/DsQfig+1rKDjj7GYgQvz0UdE+gLTgOnB\nlWUYRnYid254+224/noX6vzXX0Ot6PyJKRpD3K1xdKjSgcZvN+bz1Z+HWlJQ8acZSoCrgNa4Du4f\nVXVSJmhLE2uGMoysx4QJrg/jww9dfKnswMJtC7nx8xtpU6kNr3R5hag84d3WFpRmKHV8rqoPqOqg\n9BoKEeksImtFZL2IPJzM/mIiMklElovIAhGp622vICKzRGSViKwUkWzgT2EYxvXXw+efQ+/ebmKl\n7ECzcs1YOnApinLRWxfxy/ZfQi0p4KRYsxCRI7iaRHKoqhZOM3ORSGAd0AHYBiwCblDVNT5pRgCH\nVPUpEamJGyXewXPXLaOqy0QkClgM9EhyrNUsDCOLsm4ddOkCt9wCQ4dmzcmUkuOTlZ9wz/R7+HfL\nf4dt53dAaxaqGqWqhVJY0jQUHs2ADaq6SVVPAROA7knS1AZmeWWuA2JEpJSq7lTVZd72I8AaIPsF\naTGMHErNmm4ypa++ctO2njoVakWB4boLr2PRgEVMXjeZTh90YsfhHaGWFBCCbfLKAVt91uO9bb4s\nx/WJICLNgEpAed8EIhKDC5O+IEg6DcMIARdc4GJK7doFV14Jhw+HWlFgqFS0EnF94mhVoRUXvX0R\n09dnfZ+gXEHO3582oueAV0RkKfArsBQ4O3zHa4KaCNzn1TDOYdiwYWd/x8bGEhsbe36KDcPIVKKi\nYPJk+L//c0EIp02DMmVCrer8yRWRi2Gxw2hXuR03f3Ez19S5hmfbP0veXHkzXUtcXBxxcXHnlYdf\n4T4ynLlIc2CYqnb21gcDCar6fCrHbATqqeoREckNfAVMV9WXk0lrfRaGkU1IDEL4/vswYwZUrx5q\nRYFj37F99J/Sn62HtjLh6glULxHakwtWuI97RSSjoRZ/AaqLSIyI5MEFJZySJP8i3j5EZAAw2zMU\nAowBVidnKAzDyF4kBiEcPBguvRQWLgy1osBRokAJJl03iX4N+9FybEs+XPFhqCWlG3/GWfwH95Jf\nAowFZqbnc15EugAvA5HAGFV9VkQGAqjqWyLSAjepkgIrgf6q+qeItAbmACv4uzlrsKrO8MnbahaG\nkQ2ZOtV1er//vvOYyk4s27mM6yZeR6sKrXity2sUzFMw0zVkpGbhb9TZCKAj0AdoAnyKe/H/ngGd\nAcOMhWFkX+bNg5494YUXnHttduLIySPcNe0uFm5byKfXfEq9CzJ3eqBgxYZCVROAncAuXOdzMWCi\nN0bCMAwj4LRoAbNmweOPw4svhlpNYInKE8X7Pd7nkVaP0O5/7Xh3ybthP+e3P81Q9wG34AIIvgtM\nUtVTXm1jvapWDb7MFLVZzcIwsjnx8dCpk2uOeuEFiAi/MW7nxeo9q7lu4nXUK12Pt654i0J5gz9r\ndbBqFsWBq1S1o6p+6g2uS6xtXJkBnYZhGH5Tvjz8+KNrlurbN/sM3kukTqk6LLhtAQVzF6Tx241Z\nvnN5qCUliz/GoqqqbvbdICLjAVR1dVBUGYZh+FC8OHz7LezZ42bfO3481IoCS4HcBXin2zs80eYJ\nOozvwDuL3wm7Zil/mqGWqmojn/VcwApVrRNscWlhzVCGkbM4dcrVLjZvdh5TRYuGWlHgWbNnDb0+\n60XDMg357xX/DUoE24A2Q4nIoyJyGKgnIocTF2A3ScZKGIZhZAa5c8P//gcXXeRGe+/cGWpFgad2\nqdosHLCQ3JG5afZOM1bvCY8GHH9qFs+p6iOZpCddWM3CMHImiaO9x493zVMxMaFWFBzGLR3HQ989\nxKhOo7i5/s0Byzeg4yxEpJaqrhWRxiQT40lVl2RMZuAwY2EYOZvXXnMeUjNnQp2QN4wHhxW7VnDN\np9fQNqYtr3R5hXy58p13noE2Fu+o6gARiSN5Y9E2QyoDiBkLwzA++AD+9S/Xh9G0aajVBIdDJw7R\nf0p//jjwBxN7TaRyscrnlV/AR3B7YylaqOrP56UsSJixMAwD/g4P8tlnri8jO6KqvLrgVZ756Rne\nvfJdrqyZ8ZELQQn3ISLLVLVhhlUFETMWhmEkMmsWXHcdvPcedO0aajXBY+7WuVw38Tp61+/N8LbD\nyRWR/pkmgmUsXgTmA5+H25vZjIVhGL4sWADdusGrrzrDkV3ZfXQ3N3x+A4Lw8dUfU6pgqXQdH6wR\n3HfgAgee9HGhPZQuZYZhGJnAxRc776gHHoAxY0KtJniULliamTfPpGl0Uxq/3ZgF8cGfRDSokx8F\nG6tZGIaRHOvXQ4cO8OCDcO+9oVYTXCavncyAqQN4MvZJ7mhyB24qoNQJZojyYkB14KzPlqrOSU9B\nwcCMhWEYKbF5M7Rv7zq+Bw8OtZrgsmH/Bnp+0pPGZRvz5uVvkj93/lTTB2umvAG4SYi+AZ4EZgLD\n0lOIYRhGZlOpEsyZ41xrH3vMDeTLrlQrXo35/edz8sxJWo1txcYDGwNehj99FvcBzYBN3tiKRsCf\nAVdiGIYRYKKjIS4Opk+HQYOyt8EomKcgH171Ibc2uJXmY5rz277fApq/P95Qv6hqExFZBjRX1b9E\nZLUFEjQMI6tw4ICbD6NRI3j99ew3J0ZSlu9cTr0L6hEhyZ9osLyhtnp9Fl8C34rIFGBTegoxDMMI\nJcWKwTffwMqVrg/jzJlQKwouDco0SNFQZJR0eUOJSCxQGJihqif9SN8ZeBmIBN5V1eeT7C8GjAWq\nAH8B/VR1lT/HemmsZmEYht8cPQrdu0OpUi4IYa70j2fLFgQ6NlTx1A5U1f1piIkE1gEdgG3AIuAG\nVV3jk2YEcEhVnxKRmsDrqtrBn2O9481YGIaRLo4fh6uvhgIF4OOPXdjznEagm6GWAItTWdKiGbBB\nVTd5U7FOALonSVMbmAWgquuAGBEp7eexhmEY6SZ/fpg0CU6ehF694MSJUCvKGqRoLFQ1RlUrp7T4\nkXc5YKvPery3zZflwFUAItIMqASU9/NYwzCMDJE3L0ycCJGRbprWv/4KtaLwJ80WOxG5NLntfgzK\n86d96DngFRFZCvwKLAXO+HksAMOGDTv7OzY2ltjYWH8PNQwjB5MnD0yYAL17u3hSkye7Wkd2JC4u\njri4uPPKwx/X2a/4++WdD9dEtFhV26VxXHNgmKp29tYHAwnJdVT7HLMRqAdc6M+x1mdhGMb5cvo0\n3Hor7NoFU6a4vozsTlBcZ1X1ClW90lsuw73ID/qR9y9AdRGJEZE8wHUkmbtbRIp4+xJHis9W1SP+\nHGsYhhEIcuVy83qXLQtXXOE8pox/khFH3Hhcx3SqqOpp4G5ceJDVwCequkZEBorIQC9ZHeBXEVkL\ndMKNFk/x2AxoNQzDSJPISDcPRsWKcPnlcORIqBWFH/40Q73msxoBNAQ2qmrgZg/PINYMZRhGIDlz\nBgYMgN9/h2nToGDBUCsKDsGa/KiPz+ppXIyon9IvL/CYsTAMI9AkJMBtt2VvgxG0EOXhihkLwzCC\nQXY3GMGqWfyK84byzfhP3Kjqp1V1X3qFBgozFoZhBIvsbDCCZSxG4JqfPsIZjOuBAsBOoJWqXpkx\nueePGQvDMIJJosHYuBG+/jr7uNUGy1gsVdVGyW0TkV9VtV4GtAYEMxaGYQSbM2egXz+Ij4epU7OH\nwQhWiPJIEbnYp5BmPsedTk9hhmEYWY3ISBg71o3D6N7dBSLMifhTs2gKjAOivE2Hgf7AKuByVf00\nqApT12Y1C8MwMoXTp11okAMH4MsvIV++UCvKOEH1hhKRIgCqGjZTqpqxMAwjMzl9Gm68EY4dg88/\ndwEJsyLB6rPIB1wNxPB34EFV1eEZERlIzFgYhpHZnDoF117r5vP+7LOsOR9GsPosJgPdgFPAEW+x\n6CmGYeRIcueGTz75u5ZxOof03PpTs1ipqhdmkp50YTULwzBCxV9/QY8eULy4m6I1MjLUivwnWDWL\nuSJSP4OaDMMwsiX58rkZ93btcvGkEhJCrSi4+FOzWANUAzYCiRMQqqqG3IBYzcIwjFBz9Ch07gz1\n6sHrr4Ok63s9NASrgzsmue2quik9BQUDMxaGYYQDhw7BZZdBq1bw0kvhbzCCNfnRJs8wHAMSfBbD\nMAwDKFwYZsyAuDgYMiTUaoJDmsZCRLqJyHpcM9RsYBMwPci6DMMwshTFisE337gBe888E2o1gcef\nDu6ngRbAb6paGWgPLAiqKsMwjCxIyZLw3Xcwbhy88kqo1QSWXGkn4ZSq7hWRCBGJVNVZIpLNLoNh\nGEZgKFsWvv8eLr3UhTW/7bZQKwoM/hiLAyJSCPgR+FBEduMG5hmGYRjJULGiq2HExrootTfeGGpF\n548/zVDdcZ3bDwAzgA2AX3NYiEhnEVkrIutF5OFk9pcUkRkiskxEVvpO4Soig0VklYj8KiIfiUgW\njcJiGEZOpFo1mDkTBg2CyZNDreb8Cdq0qiISCawDOgDbcDPr3aCqa3zSDAPyqupgESnppb8AKA/8\nANRW1RMi8gkwTVXfT1KGuc4ahhHWLF4MXbrARx9Bhw6hVuMI1gjujNIM2OC53p4CJuBqKb7sAAp7\nvwsD+1T1NHAIF4uqgIjkws3Mty2IWg3DMIJC48YuQu2NN8LcuaFWk3GCaSzKAVt91uO9bb68A9QV\nke3AcuA+AFXdD7wEbAG2AwdV9bsgajUMwwgal1zi4kf17AnLloVaTcZIs4NbRDoAP6tqeueH8qd9\n6FFgmarGikhV4FsvDtUFwP24sOh/Ap+JyE2q+mHSDIYNG3b2d2xsLLGxsemUaRiGEXw6dYI33oCu\nXd3gvRo1Mq/suLg44uLizisPf8J9/A9oDhwA5njLT6p6II3jmgPDVLWztz4YSFDV533STAP+o6o/\ne+vfA48AlYGOqnqbt7030FxV70pShvVZGIaRpRg3DoYNgx9/dF5ToSBY4T5uUdUaQE9cs9LrwB4/\n8v4FqC4iMSKSB7gOmJIkzVpcBzgicgFQE/gd19HdXETyi4h4aVb7d0qGYRjhS9++8MADrrN7165Q\nq/Eff5qhegOtgfo4IzEa+Cmt41T1tIjcDcwEIoExqrpGRAZ6+98CngHGichynOF6yOuv2O/VaH7B\nxaFaArydgfMzDMMIO+6/383l3amTa5IqWjTUitLGn2aofbiv/TeBOFXdmBnC/MGaoQzDyKqown33\nwdKlbjxGgQKZV3awQpQLUBe4xFuq4eJE3ZxRoYHCjIVhGFmZhAS49VbYv98FIMys+byDNc6iEFAR\nqOeX3uUAABJVSURBVITzTiqKhSg3DMM4byIiYOxY97dPn/Cebc+fmsUK4GdcbKg5qhqfGcL8wWoW\nhmFkB44fd7Pt1a8Pr74a/MmTgtIM5ZN5Idx0qmETRNCMhWEY2YU//3SBB3v2hCeeCG5ZQWmGEpF6\nIrIUWAWsFpHFInJhRkUahmEY/6RIETfb3v/+5wbvhRv+hCh/GxikqrMARCTW29YyiLoMwzByHBdc\n4Gbbu/RSKF4crr8+1Ir+xh9jUSDRUACoapyIFAyiJsMwjBxLlSowbRpcdpkzGB07hlqRwx9vqI0i\n8rg3EruyiAwB/gi2MMMwjJxK/fouUu1NN8GiRaFW4/DHWPQDSgNfAJ8DpbxthmEYRpBo3RrGjIFu\n3WDdulCrCeLkR5mBeUMZhpHdGTsWnnoKfv4ZoqMDk2dGvKFS7LMQkampHKeq2i09BRmGYRjpp18/\nF3CwUycXqTZUcaRSrFl4Xk8poao6OyiK0oHVLAzDyAmouuCDy5a5OFL58p1ffgEdlCcilVR18/lJ\nCi5mLAzDyCkkJMANN8Dp0/DppxAZmfG8Aj0o70ufjD/PsCrDMAzjvImIcAP2Dh6Ee+5xtY1MLd/P\ndFWCqsIwDMNIk7x5YdIkmDcPnn46c8v2Z1CeYRiGESYULgzTp0PLllCunOsAzwxS67M4AxzzVvMD\nx312q6oWDrK2NLE+C8Mwcirr1kGbNs61tmvX9B0b1Kiz4YgZC8MwcjLz58OVV7rwIE2b+n9csCY/\nyjAi0llE1orIehF5OJn9JUVkhogsE5GVItLHZ19REZkoImtEZLWINA+mVsMwjKxG8+Z/j/LesCG4\nZQWtZiEikcA6oAOwDVgE3KCqa3zS/H975x5tVXHf8c8XkIiID4SgMRhNtL5WIoohRGK9KZbaRqDW\ntNZEg7RBV5eoa9kYH63N1SRaNTEPH41SFdSoGCwujSmChBuNgBTkoUAwUXT5QMDHYiG2KvDrH/Pb\n3s3xnLvvJffcfS78PmvddefMnj3z3b8zZ//2zOyZaQY+ZmaXShrg6QeZ2WZJU4DfmNntknoBfc1s\nQ0UZ0bIIgmCn59Zb4brrYO5cGDiwOH2jtSyGAX8wsxfN7APgPmBsRZo1QDb2sQfwpjuKPYHjzex2\nADPbXOkogiAIgsTZZ6flzE8+GTZtqk8Z9XQW+wMv5z6/4nF5JgFHSnoNWApc4PEHAesl3SHpaUmT\nJO1WR61BEATdmiuvhMMOS05j8+bOz7+ezqI9/UOXAUvM7BPAEOAm3761F3AMcLOZHQNsAi6pm9Ig\nCIJujgSTJsF778HEiZ0/aa+e8yxeBQbnPg8mtS7yHAd8H8DMnpe0GjjU071iZtlK7tOo4Syam5s/\nDDc1NdHU1NQJ0oMgCLofvXvDtGlp0cHnnoNDD03xLS0ttLS0/FF513OAuxdpwHok8BqwgI8OcF8P\nbDCzKyQNAhYBnzOztyQ9DnzTzJ7zgfA+ZnZxRRkxwB0EQVDB1q1peZBadOoS5X8sPlA9EXgU6Anc\nZmYrJZ3jx28BrgLukLSU1CX2bTN7y7M4D/i5pN7A88D4emkNgiDYkWjLUWwvMSkvCIJgJ6PRXp0N\ngiAIdhDCWQRBEASFhLMIgiAICglnEQRBEBQSziIIgiAoJJxFEARBUEg4iyAIgqCQcBZBEARBIeEs\ngiAIgkLCWQRBEASFhLMIgiAICglnEQRBEBQSziIIgiAoJJxFEARBUEg4iyAIgqCQcBZBEARBIeEs\ngiAIgkLCWQRBEASFhLMIgiAICqmrs5B0kqTfSfq9pIurHB8gaYakJZKelXRWxfGekhZLerieOoMg\nCIK2qZuzkNQTuBE4CTgCOF3S4RXJJgKLzWwI0AT8UFKv3PELgBWA1UtnPWhpaSlbwkcITe0jNLWf\nRtQVmupHPVsWw4A/mNmLZvYBcB8wtiLNGmAPD+8BvGlmmwEkfRL4K+A/AdVRZ6fTiJUjNLWP0NR+\nGlFXaKof9XQW+wMv5z6/4nF5JgFHSnoNWEpqSWT8CLgI2FpHjUEQBEE7qKezaE/X0WXAEjP7BDAE\nuElSP0knA+vMbDHdrFURBEGwIyKz+gwHSBoONJvZSf75UmCrmV2TS/Mr4Ptm9qR/ng1cApwCnAls\nBnYldVE9YGbfqCijW41lBEEQNApm1qEH8Xo6i17AKmAk8BqwADjdzFbm0lwPbDCzKyQNAhYBnzOz\nt3JpTgC+ZWaj6yI0CIIgKKRXcZLtw8w2S5oIPAr0BG4zs5WSzvHjtwBXAXdIWkrqEvt23lHks6uX\nziAIgqCYurUsgiAIgh2HbjuDu2jCXxlIelHSMp9IuKAkDbdLWivpmVxcf0mzJD0naaakvRpEV7Ok\nV9xeiyWd1IV6BkuaI2m5Twg93+NLtVUbusq01a6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"text": [ "" ] } ], "prompt_number": 2 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Ex10-pg282" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print (\"Example 5.10\")\n", "%pylab inline\n", "import numpy\n", "import matplotlib\n", "from matplotlib import pyplot\n", "p=0.96 #p=p't8/pt8\n", "f=0.02\n", "fAB=0.04\n", "\n", "z0=numpy.linspace(0.45,0.63,7)\n", "gmr=1.3/1.33 #gm=gm/gm' gm=gamma\n", "gm=1.33\n", "gm1=1.3\n", "tlAB=7.\n", "tl=6.\n", "i=0;\n", "z1=numpy.linspace(7,9,3)\n", "for tlAB in z1:\n", " tt=6.5\n", " g1=numpy.zeros(7)\n", " gc1=0;\n", " for tt in z0:\n", " A=(1+f+fAB)/(1+f)*((gmr)**(1./2))*1/p*((tlAB/(tl*tt))**(1./2))*((((gm1+1)/2.)**((gm1+1)/(2*(gm1-1))))/(((gm+1)/2.)**((gm+1)/(2.*(gm-1)))))\n", " g1[gc1]=A\n", " gc1=gc1+1;\n", " number=0;\n", " pyplot.plot(z0,g1)\n", " i=i+1;\n", " pyplot.xlabel(\"Turbine expansion parameter\")\n", " pyplot.ylabel(\"A8-AB-ON/A8-AB-OFF\")\n", " pyplot.title(\"Nozzle throat area variation with \")\n", " pyplot.legend([\"tau(AB)=7\",\"tau(AB)=8\",\"tau(AB)=9\"])\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Example 5.10\n", "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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xuReGkQ9CLRKTgT3An1RdRD3vS/0PQENVHZZHI+MILBJ/AKqp6i9FpAGwFLhV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\nd28nGN26Qfny4bbQKAwyNIN1h9Zljpj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"text": [ "" ] } ], "prompt_number": 3 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Ex12-pg293" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print (\"Example 5.12\")\n", "%pylab inline\n", "import numpy\n", "import matplotlib\n", "from matplotlib import pyplot\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "gm=1.1\n", "M0=2.5\n", "g1=numpy.zeros(40)\n", "\n", "z0=numpy.linspace(0,4,40)\n", "i=1;\n", "z1=numpy.linspace(1.1,1.4,4)\n", "for gm in z1:\n", " gc1=0;\n", " for M in z0:\n", "\t\tp0=(1+(gm-1)/2*(M**2))**(gm/(gm-1))\n", "\t\tp20=.4*p0-.5*p0\n", "\t\tM=3\n", "\t\tp42=0.37\n", "\t\tNPR=p20*p42\n", "\t\tg1[gc1]=p0\n", "\t\tgc1=gc1+1;\n", "\t\tpyplot.plot(z0,g1)\n", "\t\tpyplot.title(\"Total-to-static pressure ratio\")\n", "\t\tpyplot.xlabel(\"Flight Mach no. (M0)\")\n", "\t\tpyplot.ylabel(\"pt0/p\")\n", "\t\tpyplot.legend([\"gamma=1.1\",\"gamma=1.2\",\"gamma=1.3\",\"gamma=1.4\"])\n", "\t\ti=i+1;\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Example 5.12\n", "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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62pqEpdRCSUkeJW5+Li1PTR3Xr2GdifVDymsNjYEHRGSoMeYBl5ZEKaUucgd2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\neRw4EsHVl2TxVO1UxkwbQ/7mQtr36s0VTy0kTA3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"text": [ "" ] } ], "prompt_number": 5 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Ex13-pg296" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Example 5.13\"\n", "%pylab inline\n", "import numpy\n", "from numpy import linspace\n", "import matplotlib\n", "from matplotlib import pyplot\n", "#T=Th/Tc\n", "z0=numpy.linspace(0,8,160)\n", "i=0\n", "z1=numpy.linspace(1,4.5,7)\n", "for T in z1:\n", "\tg1=numpy.zeros(160);\n", "\tgc1=0;\n", "\tfor alfa in z0:\n", "\t\tFR=((1+alfa)**(1./2)*(T+alfa)**(1./2))/(T**(1./2)+alfa)\n", "\t\tg1[gc1]=FR\n", "\t\tgc1=gc1+1;\n", "\tnumber=0;\n", "\tpyplot.plot(z0,g1)\n", "\ti=i+1;\n", "\tpyplot.xlabel(\"Bypass ratio(alfa)\")\n", "\tpyplot.ylabel(\"Ratio of mixed to seperate-flow turbofan engines gross thrust\")\n", "\tpyplot.legend(\"T(hot)/T(cold)=1.5\",\"T(hot)/T(cold)=2\",\"T(hot)/T(cold)=2.5 so on\")\n", "\tpyplot.title(\"Ideal gross thrust gain with a perfect mixer\")\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Example 5.13\n", "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Pdwyf7yhe7xG83qP4fEfweo9QU3OAxMQ0HI4ROBwjsdtHmPOjsNkG6AdIF6Gu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\nD/ysVvo34F6gEbhHROYA31DVP/f/XzewrF+/gkljK6iYO4o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