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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter8-Centrifugal Compressor Aerodynamics"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex1-pg505"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Example 8.1\"\n",
      "import numpy\n",
      "%matplotlib inline\n",
      "import matplotlib\n",
      "from matplotlib import pyplot\n",
      "z0=numpy.linspace(0.2,0.6,80)\n",
      "g1=numpy.zeros(80)\n",
      "gc1=0.\n",
      "gm=1.4\n",
      "i=0;\n",
      "M1=z0\n",
      "for i in range (0,80):\n",
      "\ty=1./((1+((gm-1)/2.)*M1[i]**2)**(1./2))\n",
      "\tg1[gc1]=y\n",
      "\tgc1=gc1+1\n",
      "\n",
      "\n",
      "pyplot.plot(z0,g1)\n",
      "pyplot.xlabel(\"Inlet Mach no M1\")\n",
      "pyplot.ylabel(\"Ratio of index to the impeller tip tangential Mach no.\")\n",
      "pyplot.title(\"Ratio of Mach index to impeller tip Mach no.\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Example 8.1\n"
       ]
      },
      {
       "metadata": {},
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myvsTCPPJrwHcBxxuZgsKFZhzzrnyVGtRmKTlwFTg3RpWm5kdmM/AsuVFYc45Vz+FGIRy\nT1Zu/VW92XHJ+/ZbaN687u2cc87lToPuILnOOsbee8OBB8LPfw5t2xY7KuecK12FrLwvW7NmhaTy\n8MOw6aaw++7w97/D7NnFjsw55xquvCYWSf0lzZD0lqShNaxvI+lBSZMljZO0Rdq60yRNkTQ1Toec\nWl4pab6kSfHRv7bzt2sHxxwDDzwAH34IZ54J06bBjjvC1lvDRRfB1KnQQG/anHOuKPJWFCapCTCT\n0PflPeBVYKCZTU/b5gpgkZldLKk78E8z21vSloQOmTsAS4AngN+Y2duSLgC+rGtImUyV98uWwZgx\nIeE88AA0awaHHAK//CVsvz0o6xtB55wrP4UYK+zRDPslaRXWB5hlZnPi8UYCBwHT07bpAQyLB5wp\nqYuk9ePycWb2bdz3eeAQwjAzsHJDgnpr0gR23TU8/vY3mDgR7r8fjjoKvv8eDj00JJkdd4TVGnRh\noXPO5V6mVmFXZnnsDsC8tNfzgeqTg00mJIz/k9QH6Bz3mwJcEofr/xb4OfBK2n6nSBoEjAfOMLPP\nVzVICbbbLjz+8pdQNHbffXDCCfDll3DYYeHhScY555KpNbGYWVWWx05SxjYMuFrSJEIymQQsM7MZ\nkoYDTwFfxeWpMcquBy6Kzy8mJMATajp4ZWXlD88rKiqoqKjIGIwEvXqFx4UXhvqYe+8NSWbx4pBg\njjjCi8uccw1DVVUVVVVVOT9ukrHCugGXAlsAqV4hZmZd69ivL1BpZv3j67OB5WY2PMM+s4FeZra4\n2vJLgblm9q9qy7sAj5pZrxqOldMOktOmwahRMHJkqKMZMCAkmV69PMk45xqGQjY3vhX4F6ESvQK4\nDbgzwX7jgc1ivUkzYABhBsofSGoV1yFpMPB8KqnEuhYkdQIOBu6KrzdMO8TBhDudvNtii9CKbObM\nUFS2dCkccEBILH/5C7zzTiGicM650pfkjmWimW0raUrqziC1rM6DS/sBVwFNgJvN7DJJQwDM7AZJ\n/YARhGKzqcAJZvZF3PcFYD1CQjvdzEbH5f8Bto77zAaG1DSGWSGGdFm+PLQuu/vuUGTWtWtoADBg\nAKy/fl5P7ZxzOZerO5YkiWUMsCthIMpngfeBy8yse7Ynz6dCjxW2ZAk88wzceSf897/Qrx/86lfw\ni19Ay5YFC8M551ZZIRNLH0IT4daEyvJ1gMvNbGy2J8+nYg5C+dVXobf/HXfAyy+H3v9HHw177BGa\nOjvnXCkqWGIpV6UyuvGCBaGo7D//gY8+CkVlxxwDPXsWOzLnnFtZ3hOLpKvN7LRaOkr6sPmrYOrU\nkGDuuAM6dAgJZuBAWG+9YkfmnHOFSSzbmdkESRU1rDYzez7bk+dTKSaWlKVLQ33MbbfB44/DPvvA\nccfBvvtC00xdVp1zLo8KWcfyezO7qq5lpaaUE0u6zz8PfWNGjIB580JdzPHHQ7duxY7MOdfYFLIf\nyzE1LDs22xO7oHVr+M1vYOxYePrp0Ply111ht91Csvnqq2JH6Jxz9ZOpKGwgcCShqfGLaavWJgy7\nslf+w1t15XLHUpMlS0KT5ZtvhpdegsMPh8GDw3hm3svfOZcvhahj6QxsQhjPaygrRhT+EphsZkuz\nPXk+lXNiSffee3DrrSHJtG4dEsxRR0GrVsWOzDnX0Hhz4zo0lMSSsnw5PPss3HgjPPVUmD9myBDo\n08fvYpxzuVHIyvtDCXct7Vlx12Jmtk62J8+nhpZY0i1YEOpfbrwx9OofMiT08l+npH8jzrlSV8jE\n8jawf/rMj+WgISeWlOXL4bnn4IYbQvPlww8PDQG22abYkTnnylEhW4V9WG5JpbFYbTXYe+8wAOYb\nb0CnTmFssr59Q0fMb78tdoTOucYoyR3L1cAGwEPA93GxmdkDeY4tK43hjqUmy5bBY4/BddfBhAlw\n7LHhLqZrxtlznHOusEVhI+LTlTY0s+OyPXk+NdbEkm7WrFBMNmJEuIs5+eTQy9+nWHbO1cRbhdXB\nE8sKX38devdfc02YYvl3vwtDyHiTZedcuoLVsUjqLulZSdPi660knZvtiV3htGgRhomZODGMTzZu\nHGyySUgw0732zDmXY0kKRW4EzmFF/coUYGDeInJ5I8FOO4Vh/KdODaMq77FHGPzyf/8Lrcyccy5b\nSRJLCzMbl3oRy5eW5C8kVwgbbQQXXQTvvhv6wJx/Pmy+Ofzzn6G4zDnnVlWSxLJQ0k9SLyT9Evgg\nfyG5QlpjDRg0CMaPh1tugdGjoXNn+OMfYe7cYkfnnCtHSRLLycANwOaS3gdOB07Ka1Su4CTYZRe4\n777QTNksdLQcMCDUyTjnXFKJW4VJagmsZmZf5jek3PBWYdlbtCjcxVx9NWy4IZxxRuiA2aRJsSNz\nzuVDIfuxnEG1PizAF8AEM3st2wDyxRNL7ixdCg89BFdeCR99BKefHport2xZ7Micc7lUyMRyF7A9\n8ChhEMqfE1qGdQbuM7Ph2QaRD55Y8mPMmJBgXnghDH55yinQvn2xo3LO5UIhxwrbGNjWzM4wsz8A\n2wHrA7tTx0ySkvpLmiHpLUlDa1jfRtKDkiZLGidpi7R1p0maImmqpNPSlq8r6WlJb0p6SlLrhO/V\n5cBOO8H998PLL8Onn0KPHvDrX8PMmcWOzDlXKpIklnas6MMCoalxezP7Gqh1mENJTYBrgf5AT2Cg\npB7VNjsHmGhmvYFBwNVx3y2BE4EdgN7A/pI2jfucBTxtZt2AZ+NrV2A/+UkYj2zmzNB0eddd4eCD\nwxTLzrnGLUliuRMYJ+kCSZXAGOCuWJn/Rob9+gCzzGyOmS0BRgIHVdumBzAawMxmAl0krR+XjzOz\nb81sGfA8cEjc50Dgtvj8NuAXCd6Dy5N27aCyEubMgb32goEDYffdw0CYXhLpXONUZ2Ixs4uBXxMq\n7D8DhpjZhWb2lZkdlWHXDsC8tNfz47J0k4kJQ1IfQr1NB0Idzq6x2KsFoV6nY9ynvZktiM8XECYg\nc0XWokUY5PKtt0Ldy9lnQ+/ecNddofLfOdd4NE243UTg/bi9SepkZnV1n0vy/+ow4GpJkwjJZBKw\nzMxmSBoOPAV8lVr+oxOYmaRaz1NZWfnD84qKCioqKhKE5LLRtCkceWS4c3niCbjsMjjvPDjzzDCE\nf/PmxY7QOZdSVVVFVVVVzo+bpFXYKcAFwEekfbmbWa869usLVJpZ//j6bGB5plZkkmYDvcxscbXl\nlwJzzexfkmYAFWb2oaQNgdFmtnkNx/JWYSXipZdg2LDQ8fL008P8MGuvXeyonHPVFbJV2O+B7mbW\n08x6pR4J9hsPbCapi6RmwADgkfQNJLWK65A0GHg+lVRiXQuSOgEHA3fF3R4BjonPjyFMQOZK2M47\nw6OPhjuYiRPDpGOVlaFVmXOu4UmSWOYCi+p7YDNbShgO5klCJf8oM5suaYikIXGznsCUeBfyU+C0\ntEPcF4fqfwT4rZmlYhgG7CPpTWDP+NqVga22CiMrjxkD8+fDZpvB0KGwYEHd+zrnykeSorBbgG7A\n/1h5auK/5Tm2rHhRWOmbOxeuuALuvDMMhHnmmdChevMO51zBFLIobC7wDNAMWAtYOz6cy0qnTmFW\ny2nTQqV/r15w0klhKH/nXPnyqYldyVi4EP7+d7jhBjjkkNBkuWvXYkflXONRyKmJ15f0V0mPSRod\nH89le2LnqmvXDi69FN58EzbYAHbYIUyp/PbbxY7MOVcfSXvezwC6ApXAHEKLL+fyYr314OKLYdas\nUFy2446eYJwrJ0kSy3pmdhPwvZk9b2bHEVpjOZdXbdqEZslvvbUiwRx3HLzzTrEjc85lkiSxpFqC\nfShpf0nbAm3yGJNzK0lPMBtvHIrIBg8O45M550pPksRySRya/gzgj8BNhOmJnSuoNm3gootCgmnf\nHrbbLvTinzev7n2dc4WTJLF8bmafm9kUM6sws20B7zPtimbddeGSS8KQ/a1bw9Zbw6mnwgcfFDsy\n5xwkSyzXJFzmXEG1bRvGIHvjDWjSBLbYInSy/PjjYkfmXONWa2KR1C/Od99O0h8knREflZn2c67Q\n2rcP/V9efx0WL4bu3eGCC+CLL4odmXONU6YE0YzQw75J/LlWfCwCfpn/0Jyrn44d4frr4dVXQ8X+\nZpvB8OHw9dfFjsy5xiXJWGFdzGxOYcLJHe9576ZPh/PPD8P2n3sunHgiNGtW7KicK10F63lfjknF\nOYAePeDee8OQ/Q8/DJtvDrffDst+NGWccy6XfKww12hUVYXxxxYvDkPH7L8/KOv/zZxrOHJ1x+KJ\nxTUqZuEO5uyzQ7+Y4cPDRGTOucIOQrmppEclfSxpoaSHJfmYs64sSXDggaEF2YknwpFHhtfTphU7\nMucajiTNhu8C7gE2BDYC7gXuzmdQzuVbkyZw7LGhk2VFBeyxB5xwQpjZ0jmXnSSJZU0zu93MlsTH\nHUDzfAfmXCE0bw5/+EMYqn/99cP0yUOHwuefFzsy58pXksTyuKSzJXWJj6Fx2bqS1s13gM4VQuvW\ncNllMGUKfPIJdOsGV10F331X7MicKz9J+rHMAWrbyMysJOtbvPLeZWPaNDjrrPDz0kvh8MNhNR9v\nwjVw3iqsDp5YXC6MHg1/+lOo9P/rX2G33YodkXP5k/fEImkvM3tW0qHUcMdiZg9ke/J88sTicmX5\nchg1KjRR3mab0ES5W7diR+Vc7hWiuXHqf7MDank41yisthoMHAgzZsBOO4V+L6ec4qMoO1ebJHUs\nXc3snbqW1bJvf+AqwkCWN5nZ8Grr2wC3AF2Bb4HjzWxaXHc28CtgOTAFOM7MvoujK58ILIyHOdvM\nnqjh3H7H4vLi44/DhGN33x1akJ1yCqyxRrGjci57BesgCdxXw7J769pJUhPgWqA/0BMYKKlHtc3O\nASaaWW9gEHB13LcLMBjY1sx6ERLTEXEfA/5mZtvEx4+SinP51LYt/OMf8OKL8MIL0LMn3Hdf6NXv\nnMs8H0uPWL/SWtIhkg6NP48lWT+WPsAsM5tjZkuAkcBB1bbpAYwGMLOZQBdJ7QhD8y8BWkhqCrQA\n3ksPL9nbcy5/Nt8cHnkEbrwxzGi5224wYUKxo3Ku+DLdsXQj1KW0ij/3jz+3JdxN1KUDkD4b+fy4\nLN1k4BAASX2AzkBHM/sUuBKYC7xPmB75mbT9TpE0WdLNkloniMW5vNlzz5BQjjkGDjgg9Oh///1i\nR+Vc8TStbYWZPQw8LGknMxuzCsdOUjAwDLha0iRCPcokYJmkTYHfA12AL4B7JR1lZncC1wMXxf0v\nJiSgE2o6eGVl5Q/PKyoqqKioWIW34VzdmjQJY48NGBA6WvbqBb//Pfzxj7DmmsWOzrmaVVVVUVVV\nlfPj5q0fi6S+QKWZ9Y+vzwaWV6/Ar7bPbKAX8HNgHzM7MS4/GuhrZr+rtn0X4NFYD1P9WF5574pm\n9uzQ/+XVV+Hyy+Gww3yIflf6Cll5v6rGA5vFYWCaAQOAR9I3kNQqrkPSYOB5M1sMzAT6SlpTkoC9\ngTfidhumHeJgwp2OcyVlk03CJGO33RZ67u+2G0ycWOyonCuMjIlF0mqSDl+VA5vZUuBk4ElCUhhl\nZtMlDZE0JG7WE5giaQbwU+C0uO9rwH8Iyen1uO2/48/hkl6XNBnYHTh9VeJzrhB23z3UvwwaBD/7\nGQweDB99VOyonMuvJP1YJpjZdgWKJ2e8KMyVms8/hwsvhDvugHPOgZNPhtVXL3ZUzq1QsLHCJA0D\nPgZGAV+llseWWyXLE4srVdOnh4r9uXNDf5h99il2RM4FhUwsc6h5rLBNsj15PnlicaUsNUXy6adD\n795w5ZWhXsa5YipY5b2ZdTGzTao/sj2xc41ZaorkadNgu+1ghx3gggvg66+LHZlz2Usy531LSedJ\nujG+3kzS/vkPzbmGr3lz+POfYdKkMMhlz57w4IM+PIwrb0mKwu4BJgCDzGwLSS2BMXF8r5LlRWGu\nHD33XBjUsmPHUP/SvXuxI3KNSSH7sWwaOzV+D2BmX9WxvXNuFe25J7z2GvTvH4bnP+ssWLy42FE5\nVz9JEst3kn4YlCIOt+IzgTuXJ6uvHir1p0yB+fN99GRXfpIUhe0L/JnQmfFpYGfgWDMbnf/wVp0X\nhbmG4vkFey+3AAAd30lEQVTn4Xe/g402gmuv9dkrXf4UdM57SW2BvvHlWDMr+bnzPLG4hmTJErjm\nmjA8zEknhQ6WPrily7VCzHm/HSv3X0mdzADMrKRHPvLE4hqi996DM86AV14Jlfv7e/tMl0OFSCxV\nZBj63sz2yPbk+eSJxTVkzzwTisd69AgJplOnYkfkGoKCFoWVI08srqH77ju44gq46qowRP/pp/vY\nYy47hbhjOZTMdywPZHvyfPLE4hqLWbPCgJbvvQfXXw+77FLsiFy5KkRiGUHmxHJctifPJ08srjEx\nC02STz899IEZPhzWW6/YUbly40VhdfDE4hqjRYvg3HPhnnvCzJVHH+0zV7rkCjm68QbAX4AOZtZf\nUk+gn5ndnO3J88kTi2vMxo+HIUOgVatQPOZDw7gkCjmkywjgKWCj+PotfNZG50ra9tvDuHFw0EFh\naJiLLgqV/c4VQpLE0tbMRgHLAMxsCbA0r1E557LWtCmcdloYOXnCBNh6a3jhhWJH5RqDJIllsaQf\nqgEl9QW+yF9Izrlc2nhjeOih0Gv/yCPhxBPhs8+KHZVryJIkljOAR4GuksYAtwOn5jUq51xOSXDw\nwWFisTXWgC22gFGjfGBLlx9JxwprCnQnDOsyMxaHlTSvvHeudi+/DIMHQ+fOcN114adzBau8j0Pm\nnwZcAlwEnCypebYnds4VT79+MHEi7LRTmBr5H/+AZcuKHZVrKJI0N74XWATcQbhjORJoZWaH5T+8\nVed3LM4lM3Mm/PrXodXYTTfBllsWOyJXLIVsbryFmZ1gZqPN7DkzOxHYIsnBJfWXNEPSW5KG1rC+\njaQHJU2WNE7SFmnrzpY0TdIUSXdJWiMuX1fS05LelPSUpNZJ36xz7se6d4fRo+H442GPPeC887xp\nsstOksQyUVK/1IvYKmxCXTtJagJcC/QnTBI2UFKPapudA0w0s97AIODquG8XYDCwrZn1ApoAR8R9\nzgKeNrNuwLPxtXMuC6utFu5aXnstzFy5zTYwZkyxo3LlKkli2R54SdK7kuYAY4Dt453E6xn26wPM\nMrM5sbJ/JHBQtW16AKMBzGwm0EVSO0LR2xKgRWw40AJ4L+5zIHBbfH4b8IsE78E5l0CHDvDgg3Dh\nhXDooXDqqbB4cbGjcuUmSWLpD3QFdgcq4vP9gAMIX/K16QDMS3s9Py5LNxk4BEBSH6Az0NHMPgWu\nBOYC7wNfmNkzcZ/2ZrYgPl8AtE/wHpxzCUlw2GEwdWoYe2zLLeGpp4odlSsnTevawMzmSGoDbJy+\nfYIZJJPUnA8DrpY0CZgCTAKWSdoU+D3QhdAZ815JR5nZndViM0m1nqeysvKH5xUVFVRUVCQIyTkH\nYXTkESPgySdDMdmee8KVV0KbNsWOzOVKVVUVVVVVOT9uklZhFwPHAu8Ay1PL65pBMtbFVJpZ//j6\nbGC5mQ3PsM9soBfwc2Cf2FAASUcDfc3sd5JmABVm9qGkDYHRZrZ5DcfyVmHO5ciXX8LZZ4disuuu\nC2OQuYankK3CBgCbmtnuZrZH6pFgv/HAZpK6SGoWj/NI+gaSWsV1SBoMPG9mi4GZQF9Ja0oSsDfw\nRtztEeCY+PwY4KEEsTjnsrD22nDttTByJJx5JhxxBCxcWOyoXKlKklimAfW++TWzpcDJwJOEpDDK\nzKZLGiJpSNysJzAl3oX8lNAREzN7DfgPITmlGgj8O/4cBuwj6U1gz/jaOVcAu+4aWo517AhbbRXm\nffGCAVddkqKwHYCHgalAqnW7mVmmivui86Iw5/Jr7Fg47jjo2RP++U/YYINiR+SyVciJvqYD1xMS\nS6qOxczs+WxPnk+eWJzLv2+/DU2Tb7kFrroqFJH5jJXlq5CJ5VUz2yHbExWaJxbnCufVV+HYY6Fb\ntzBjpd+9lKdCVt6/KOkySf0kbZt6ZHti51zDscMOYTKxHj2gd2+4+26ve2nMktyxVFFDn5SELcOK\nxu9YnCuO1N3L5puHu5f11y92RC6pghWFlStPLM4Vz7ffQmVl6GB5zTWhJ78rfXlPLJKONrPbJZ3B\nyncsIlTe/y3bk+eTJxbnim/s2HD30rt3aDnWtm2xI3KZFKKOpUX8uXa1x1rxp3POZdS3L0yatKLf\nyyOP1L2PK39eFOacK4gXXwx3L7vtFpomt2pV7IhcdYVsFeacc1nbdVeYPBnWXBN69YKnny52RC5f\n/I7FOVdwTz8NJ5wABx4Iw4dDy5bFjsiB37E458rYPvuEu5cvvgizVb78crEjcrlUZ2KRtIGkmyU9\nEV/3lHRC/kNzzjVkbdrA7bfDZZfBwQfDOefA998XOyqXC0nuWEYATwEbxddvAafnKyDnXONy6KHh\n7mXqVNhxx/DTlbckiaWtmY0ClgHE+euX5jUq51yj0r49PPwwnHwy7LFHmKly2bJiR+VWVZLEsljS\neqkXcWbIL/IXknOuMZJChf64cfDQQ7DXXvDuu8WOyq2KJInlDOBRoKukMcDtwKl5jco512h17QpV\nVfCzn4XBLW+/3Qe0LDeJmhtLWh3oHl/OjMVhJc2bGztX/l57DX71qzCZ2PXXw3rr1b2PW3WFbm7c\nB+gNbAcMlDQo2xM751xdtt4axo8PQ8L07u2dKstFkmHz7wC6Aq8RK/ABzOyU/IaWHb9jca5heeaZ\nMBXyL38Zmig3b17siBqeQk9N3LPcvqU9sTjX8Hz6KQwZAtOnw113hYEtXe4UsihsKrBhtidyzrls\nrbsu3HMP/OlPodXY3/4Gy5cXOypXXab5WB6NT9cCtgFeAb6Ly8zMDsx/eKvO71ica9hmzw4V+y1a\nhAnFOnQodkTlrxATfVXEp0aY3CudmdnzdR5c6g9cBTQBbjKz4dXWtwFuIdThfAscb2bTJHUHRqZt\n2hU4z8z+IakSOBFYGNedbWZP1HBuTyzONXBLl4b6lmuvDa3GDjmk2BGVt0LWsVxuZn+qtmy4mQ2t\nY78mwExgb+A94FVgoJlNT9vmCmCRmV0ck8k/zWzvasdZLe7fx8zmSboA+LKuGSw9sTjXeIwdG+5e\nKirg6qt9tORVVcg6ln1qWPazBPv1AWaZ2ZzY72UkcFC1bXoAowHMbCbQRVK7atvsDbxtZvPSlmX9\nxp1zDUdqpsqlS2HbbWHChGJH1LjVmlgknSRpCtBd0pS0xxzg9QTH7gCkJ4P5cVm6ycAh8Xx9gM5A\nx2rbHAHcVW3ZKZImx1GXWyeIxTnXwK29dqhruegi2G8/uPxyr9gvlkx3LHcBBwCPAPvH5wcA25nZ\nUQmOnaQcahjQWtIk4GRgEml9ZSQ1i+e8N22f64FNgK2BD4ArE5zHOddIDBgAr74K//0v7LsvvP9+\nsSNqfJrWtsLMviAMNnnEKh77PWDjtNcbE+5a0s/xJXB86rWk2cA7aZvsB0wws4Vp+3yUtv1NhHHM\nalRZWfnD84qKCioqKur5Fpxz5ahzZxg9Gv7yl1A0duONcMABxY6q9FRVVVFVVZXz4+ZtamJJTQmV\n93sB7xOaK1evvG8FfGNm30saDOxsZsemrR8JPG5mt6Ut29DMPojPTwd2MLMjazi/V94753jpJTjq\nKNh/f7jiClhzzWJHVLpKfmpiM1tKKN56EngDGGVm0yUNkTQkbtYTmCJpBvBT4LTU/pJaEiruH6h2\n6OGSXpc0Gdgdn3TMOZfBzjuHwSw//hj69IFp04odUcOXdHTjDYAdCPUmr6QXR5Uqv2NxzqUzg1tv\nhaFDQxHZ4MFhDhi3QiH7sRwOXAGkOkTuBpxpZvfWvlfxeWJxztVkxgw44gj4yU9C3UubNsWOqHQU\nsijsXEI9xiAzG0S4czkv2xM751wxbL556FDZoUMYlv+ll4odUcOTJLGIFcOnAHyCd1B0zpWx5s1D\nD/1rr4VDD4VLL4Vly+rezyWTpCjsCsIkX3cREsoA4PXqw7yUGi8Kc84lMX9+aDW2+uphGuQNG/FY\n7gUrCjOzM4EbgK2AXsANpZ5UnHMuqY4d4bnnYJddQp+XJ58sdkTlL8kdy48GnEwyCGWx+R2Lc66+\nnn8+DGZ51FFw8cXhLqYxKWTl/b41LEsyCKVzzpWV3XeHiRNhypTw/N13ix1RecrnIJTOOVd22rWD\nRx8Nlfp9+sCDDxY7ovKTaaKvVkAbwkCRQ1nREuxLM/ukMOGtOi8Kc85la9y40OflwAPDaMlrrFHs\niPKrYB0ky5UnFudcLnz2GRx/PMybB/fcA127Fjui/Cn5scKcc64haNMGHngABg0KE4rdd1+xIyp9\nfsfinHMJjR8f5nvZbz+48sqGVzRWsDsWST1rWFaR7Ymdc67cbL99mPb4gw/CqMlvv13siEpTkqKw\neyQNVdBC0jWECn3nnGt0WrcOxWHHHAP9+sH99xc7otKTpINkS2A4sD2wFmFol2FmVtKzSXtRmHMu\n3159NRSNpVqNNWtW7IiyU8jK+6XAN8CaQHPgnVJPKs45Vwg77BCKxmbPhl139Q6VKUkSyyvAt4Q7\nll2BIyWV9FwszjlXKG3awEMPweGHhw6V//1vsSMqviRFYTuY2avVlh1tZrfnNbIseVGYc67QXnoJ\nBg6EI4+ESy6Bpk2LHVH9FLIobIKkoyWdH0/cCXgz2xM751xDs/POoWhs0iTYa6/QeqwxSpJYrgP6\nAUfG14uBf+YtIuecK2Pt2sFjj4XEst12MHp0sSMqvCSJZUcz+y2hAh8z+xRoZINJO+dcck2awPnn\nw223hWKxSy+F5Y2oyVOSxPK9pCapF5LaAY3oEjnn3KrZZ5/QW/9//wtNkj/7rNgRFUaSxHIN8CCw\nvqRLgZeAy/IalXPONRAdOkBVFWy2WSgamzCh2BHlX5Kpie8gDJt/GfA+cJCZ3ZPk4JL6S5oh6S1J\nP5pxUlIbSQ9KmixpnKQt4vLukialPb6QdGpct66kpyW9KekpSa3r84adc67QVl8d/v53GD4c+veH\nf/8bGnKj1UzzsaxbfVH8afBDXUvtBw7FZzOBvYH3gFeBgWY2PW2bK4BFZnaxpO7AP81s72rHWS3u\n38fM5km6HPjYzC6PyaqNmZ1Vw/m9ubFzruTMnBkmEdt+e7juOmjRotgRrVCI5sYTgQnx58eEJsZv\nxudJbub6ALPMbI6ZLQFGAgdV26YHMBrAzGYCXWIdTrq9gbfNbF58fSBwW3x+G/CLBLE451xJ6N4d\nxo6F77+HnXZqmANZ1ppYzKyLmW0CPA3sb2brmdl6wM/jsrp0AOalvZ4fl6WbDBwCIKkP0BnoWG2b\nIwjjk6W0N7MF8fkCoH2CWJxzrmSstRbceSeceGJILo8+WuyIcitJ5X0/M3ss9cLMHgd2SrBfknKo\nYUBrSZOAk4FJwLLUSknNgAOAGoeQiWVdXt7lnCs7Epx8chgO5re/hXPPhWXL6t6vHCQZcOB9SecC\ndxDqWY4k1HnU5T1g47TXGxPuWn5gZl8Cx6deS5oNvJO2yX7ABDNbmLZsgaQNzOxDSRsCH9UWQGVl\n5Q/PKyoqqKioSBC2c84VTr9+oaXYEUeECcTuugvati3Muauqqqiqqsr5cZOMFbYecAFhAEqAF4AL\nE1TeNyVU3u9FaE32Cj+uvG8FfGNm30saDOxsZsemrR8JPG5mt6Utuxz4xMyGSzoLaO2V9865crd0\nKfz5zzBqVJjvZfvtCx9Drirv8zo1saT9gKuAJsDNZnaZpCEAZnaDpH7ACEJx1lTgBDP7Iu7bEngX\n2CTe2aSOuS5wD9AJmAMcbmaf13BuTyzOubJz//3wm9/AZZeFOphCKlhiic2A/wh0YUXRmZnZntme\nPJ88sTjnytXMmXDwwbDLLnDNNbDGGoU5byETy+vA9YRmx6mqJTOzku4/6onFOVfOvvwSjjsO5s4N\ndzEbb1z3PtkqZGKZYGbbZXuiQvPE4pwrd2ZwxRWh1/5dd8Eee+T3fIVMLJXAQuAB4LvU8roq74vN\nE4tzrqF49lk46ij405/g9NNDU+V8KGRimUMNfUVi58mS5YnFOdeQvPsuHHIIdOsGN90ELVvm/hxl\n0SqsmDyxOOcamm++gZNOgokT4cEHYdNNc3v8vCcWSXuZ2bOSDqXmO5YHsj15Pnlicc41RGZh8MqL\nLgoTifXvn7tj5yqxZOp5vxvwLGFIlZq+oUs6sTjnXEMkwe9+B1ttBQMGwKmnwtCh+at3WRVeFOac\nc2Vq/vwwBH+nTnDrrWFwy2wUYth855xzJaxjR3jhBWjVCvr2hVmzih1R4InFOefK2BprwI03huKx\nnXeGJ58sdkQZEoukw+LProULxznnXH1JobXYffeF3vrDhxd36uNMrcImmdk2qZ8FjitrXsfinGuM\n5s0L/V26doVbbqlff5dCNDd+htAabAfgxWqrzcwOzPbk+eSJxTnXWH3zTRghefLkMJFYly7J9itE\nYmkGbEuY4OsEwiRfKWZmz2d78nzyxOKca8zM4OqrYdgwGDkSksxzWMghXdqZ2UJJa4VgbXG2Jy0E\nTyzOOQfPPBPGGTv33DAVcqb+LoVMLL2A/wDrxUULgWPMbGq2J88nTyzOORe88w4cdBD06RN67dc2\nv0sh+7H8G/iDmXUys07AGXGZc865MtC1K7z8Mnz2Gey5J3z4YX7PlySxtDCz0akXZlYF5GFcTeec\nc/my1lqhOfK++4Y7l/Hj83euJIlltqTzJHWRtImkc4F38heSc865fFhtNbjgArjqKthvvzB5WD4k\nqWNZF7gQ2DkuehGoNLPP8hNSbngdi3PO1W7KlFDvMmAAXHIJNGni87HUyROLc85l9vHH8Mtfwtpr\nw513QqtWPgilc865LLRtC08/HQaz7Ns3d8f1OxbnnHNcfz389rdlcMciqb+kGZLekjS0hvVtJD0o\nabKkcZK2SFvXWtJ9kqZLekPSjnF5paT5kibFRw7nT3POucbppJNyd6w6E4ukjeOX/8L4uF9SxwT7\nNQGuBfoDPYGBknpU2+wcYKKZ9QYGAVenrbsaeMzMegBbATPicgP+ZmbbxMcTdcVSqqqqqoodQiIe\nZ255nLnlcZaeJHcstwKPABvFx6NxWV36ALPMbI6ZLQFGAgdV26YHMBrAzGYCXSS1k9QK2NXMbonr\nlprZF2n7ldAknKuuXD5oHmdueZy55XGWniSJpZ2Z3WpmS+JjBLB+gv06APPSXs+Py9JNBg4BkNQH\n6Ax0BDYBFkq6VdJESTdKapG23ymx+OxmSa0TxOKcc65AkiSWTyQdLamJpKaSfgV8nGC/JDXnw4DW\nkiYBJwOTgGVAU8LIyteZ2bbAV8BZcZ/rCYlna+AD4MoE53HOOVcoZpbxAXQhFH8tjI+HgU4J9usL\nPJH2+mxgaB37zAbWAjYAZqct3wX4by2xTanlWOYPf/jDH/6o36Ou7/Ykj6bUwczmAAfUtV0NxgOb\nSeoCvA8MAAambxDrUr4xs+8lDQaej8PyL5Y0T1I3M3sT2BuYFvfZ0Mw+iIc4GJhSS9wNoh7GOefK\nTa2JRdJQMxsu6ZoaVpuZnZrpwGa2VNLJwJNAE+BmM5suaUhcfwOhtdgISQZMJUwolnIKcGeccOxt\n4Li4fLikrQnZdTYwJMkbdc45VxiZZpA8wMwelXQs4Uv8h1WExHJbAeJzzjlXZmqtvDezR+PTr83s\ntrTHCOCbgkRXgwSdLo+KLcZel/SSpK2S7ltCcc6JyydJeqXIcR4U45wkaYKkPZPuW0JxFuR6Jr0e\nknaQtFTSofXdtwTiLKXPZoWkL9I6S5+bdN8ix3le2rqSuZ5psU6SNFVSVX32XUmCSvhJSZYV4kEo\nUptFqLRfHXgN6FFtm35Aq/i8PzA26b6lEGdaI4Z1S+R6tkx73ovQN6kUr2eNcRbqeia9HnG754D/\nAoeW4rWsLc4S/GxWAI+s6nssdpwleD1bE+qyO8bXbVf1etZ6xyJpv1i/0kHSPyRdEx8jgCW17Zdn\ndXa6NLOXbUVnynGEfjGJ9i2ROFMK0fggSZxfpb1cixVNzUvtetYWZ0q+r2fS63EKcB+hhWV99y12\nnCkl8dnMEEspXs9M16xUrueRwP1mNh/AzFb5bz1TP5b3gQnAt/Fn6vEI8NN6vaXcSdLpMt0JwGOr\nuG82sokTQp3WM5LGK7SWy5dEcUr6haTpwOPAqfXZtwTihMJczzpjlNSB8Ad5fVpcifbNoWziTD0v\nlc+mATvFItDHJPWsx76lEGdqXalcz82AdSWNjvEcXY99V1JrqzAzmwxMlnSXmX2fNPo8q7mlQQ0k\n7QEcz4oJyhLvmwPZxAmws5l9IKkd8LSkGWb2Yq6DJGGcZvYQ8JCkXYHbJW2eh1gyhpBoo2pxAt3j\nqkJczyQxXgWcZWYmSaz4T7XUPpu1xQml9dmcCGxsZl9L2g94COiWh1gyyTbOUrqeqxM6pu8FtABe\nljQ24b4rSdLzvovCKMNvSJodH8Wamvg9YOO01xsTsudKFCrCbwQOtBUzXSbatwTixGI/HTNbCDxI\nuBUtWpxpcb1I+Gdk3bhdSV3PlFScktaLrwtxPZPEuB0wUtJs4FDgOkkHJty3FOIsqc+mmX1pZl/H\n548DqyvMeFtSn80McZbU9STclTxlZt+Y2SfAC0DvhPuuLEGlz0uEDoqvE8byqgQuzndlUy2xNCX0\naekCNKPmCqhOhIqmvvXdt0TibAGsHZ+3jNd/3yLGuSkrmqVvC7xdoteztjgLcj3rez0IA7keUorX\nMkOcpfbZbJ/2O+8DzCnF65khzlK7npsDzxAq61sQOp/3XJXrWWfPe2BNM3tGkszsXaBS0kTgvLp2\nzDVL1unyfKANcH24i2eJmfWpbd9Si5MwnM0DcVlT4E4ze6qIcR4KDJK0BFgMHJFp31KLkwJdz4Qx\n1mvfXMeYbZyU3mfzl8BJkpYCX1O6n80a46TErqeZzZD0BOEmYjlwo5m9AVDf61nnDJKSxgC7ElqI\nPEuo1L/MzLpn3NE551yjlCSx9AGmE9o4XwysA1xuZmPzH55zzrlyU+8572MrkcPNbFR+QnLOOVfO\nMnWQXEvSGZKuk/RbSatJOpjQM/OowoXonHOunGQahPIBYBHwMrAvoYnZt8CpZvZawSJ0zjlXVjIl\nltfNbKv4vAlhtsbOZla0ASidc86VvkwdJJelnpjZMuA9TyrOOefqkimxbCXpy9QD6JX2elGhAnSu\nPiQtTrBNlaTt6tjm95LWzLD/u9WWPRT/TupN0gilDU2fD5IqJS2XtGnast/HZdvG13+RNHdV34dz\nKZnmY2liZmunPZqmPV+nkEE6Vw9Jmjmm5vfO5DRC7+PafCZpZwBJrYENE567tnjyzQg9qY9IW3YY\nYebWlEfI35AirhFJMlaYc2VHYcKiKkn3Spou6Y5atttX0hiFycHukdRS0qnARsBoSc/WsJsBo1jx\nJX0IcD9xsMbYovKZeMzXU+NsxXWDFEa5fU1S+iysuylM+PZ2TXcvkrrE9/FvhUmYnpTUPK7bWtLY\neNwHYqKryUPE4c7jncvnwCepuM1snJl9WMu+ziXmicU1ZFsT7jx6Al0l7ZS+UlJb4M/AXma2HWFa\niD+Y2T8II0xUmNletRz7WUIyWA0YQEg0Kd8AB8dj7glcGc+3RTzfHmaWig3CF/sGZrYzsD8wrJZz\n/gS41sy2JCSFVAL6D3CmmfUm3JVcUMv+i4C5MY70mAs5urJrBJKMFeZcuXrFzN4HkPQaYRC9MXGd\ngL6EpDMmjtfULG19XZYB/wcMBJqb2bvxGBD+YbtMYfj+5cBGktoTksw9ZvYpgJl9Hrc3wt0Ecfym\n9rWcc7aZvR6fTyCMPL4OYSbS1FDrtwH3Zoh7VIx5X8Lw6MclfL/OJeaJxTVk36U9X0bNn/enzezI\nVTi2EWbSe5Af3yEcBbQFtjWzZQrDzzeP+9Q2W2D6nEe1bVP9/TSvYZtMsxEaYarhK4BXzezLtGTo\nXM54UZhrrAwYC+ycaikV61c2i+u/JIyLV/sBwl3CpcDd1VatA3wUk8oehOkmjDCH/GGKc3FIapPl\ne5CZLSI0JNglLjsaqMqw/TfAUOAvWZ7buVp5YnENTfVpdGvfMMzpfSxwt6TJhGKw1Kjd/waeqKXy\nPv0Yf0sVbaWd705ge0mvE77op8dt3yB8oT8fi+aurGfc1ZenXh8DXBHfw1bARZn2N7NRNY2eIely\nSfOANSXNk3R+LcdxLqN6D0LpnHPOZeJ3LM4553LKE4tzzrmc8sTinHMupzyxOOecyylPLM4553LK\nE4tzzrmc8sTinHMupzyxOOecy6n/BxxlTRO4HfPVAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x589b2b0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex3-pg515"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "#calculate static pressure ratio across the rotor and diffuser and estimate diffuser staic presure rise\n",
      "print(\"Example 8.3\")\n",
      "M1=1.2 ##Mach no at impeller tip\n",
      "gm=1.4 ##gamma\n",
      "p31=(1+(gm-1)*M1**2)**(gm/(gm-1)) ##p=p3/p1\n",
      "p32=p31**(1/2.) ##p31=p3/p2\n",
      "Cp=(2/(gm*M1**2.))*(2.2-1) ##static pressure rise in radial diffuser\n",
      "print\"%s %.3f %s\"%(\"(a)The static pressure the rotor and diffuser p3/p1 :\",p31,\"\")\n",
      "print\"%s %.4f %s\"%(\"The static pressure ratio across the diffuser p3/p2\",p32,\"\")\n",
      "print\"%s %.3f %s\"%(\"Diffuser static pressure rise :\",Cp,\"\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Example 8.3\n",
        "(a)The static pressure the rotor and diffuser p3/p1 : 4.914 \n",
        "The static pressure ratio across the diffuser p3/p2 2.2168 \n",
        "Diffuser static pressure rise : 1.190 \n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex4-pg517"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Example 8.4\"\n",
      "%matplotlib inline\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore')\n",
      "#calculate and graph the inducer D-factor for solidity of one and over a range of impeller tip mach number and radius ratios\n",
      "import numpy\n",
      "import matplotlib\n",
      "from matplotlib import pyplot\n",
      "M=2;\n",
      "i=1;\n",
      "sigma=1\n",
      "z0=numpy.linspace(0.1,0.5,5)\n",
      "gm=1.4;\n",
      "\n",
      "for M in range(2,4):\n",
      "    g1=numpy.zeros(5)\n",
      "    gc1=0;\n",
      "    for r in z0:\n",
      "\t\ty=1-(1/(1+(r**2)*(M**2)))+((M*r)/(2*sigma*(1+(r**2)*(M**2))**(1/2.)))\n",
      "\t\tg1[gc1]=y\n",
      "\t\tgc1=gc1+1;\n",
      "    number=0;\n",
      "    pyplot.plot(z0,g1)\n",
      "    i=i+1;\n",
      "    pyplot.xlabel(\"Eye-to-lip radius ratio (r1/r2)\")\n",
      "    pyplot.ylabel(\"D inducer\")\n",
      "    pyplot.title(\"Inducer performance and centrifugal compressor design parameters (solidity=1)\")\n",
      "    pyplot.legend(\"Mt/Mz1=2\",\"Mt/Mz1=3\",\"Mt/Mz1=4\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Example 8.4\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wpB49dk/dqQesUtV1ACLyGXALsMw3zWagtve6FLDdn9CNiXe7Duxi0IJB9J7d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\nNIPBCwfz/KTnufH8G1nceTF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       "text": [
        "<matplotlib.figure.Figure at 0x58bc2d0>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ex6-pg522"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "print(\"Example 8.6\")\n",
      "#calculate the compressor total to static efficency\n",
      "Tt1=288.\n",
      "Cp=1004.\n",
      "gm=1.4\n",
      "ett=0.8\n",
      "p=6.8 ##pt3/pt1\n",
      "C1=200.\n",
      "pt1=101.\n",
      "Tt3=Tt1*(1.+(1./ett)*(p**((gm-1.)/gm)-1.))\n",
      "Tt2s=Tt1*p**((gm-1.)/gm)\n",
      "T1=Tt1-C1**2./(2.*Cp)\n",
      "ets=(Tt2s-T1)/(Tt3-T1)\n",
      "print\"%s %.4f %s\"%(\"Compressor total-to-static efficiency :\",ets,\"\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Example 8.6\n",
        "Compressor total-to-static efficiency : 0.8141 \n"
       ]
      }
     ],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}