added books

author: hardythe1 2015-04-07 15:58:05 +0530
committer: hardythe1 2015-04-07 15:58:05 +0530
commit: c7fe425ef3c5e8804f2f5de3d8fffedf5e2f1131 (patch)
tree: 725a7d43dc1687edf95bc36d39bebc3000f1de8f /Thermodynamics_for_Engineers/Chapter_11_2.ipynb
parent: 62aa228e2519ac7b7f1aef53001f2f2e988a6eb1 (diff)
download: Python-Textbook-Companions-c7fe425ef3c5e8804f2f5de3d8fffedf5e2f1131.tar.gz
Python-Textbook-Companions-c7fe425ef3c5e8804f2f5de3d8fffedf5e2f1131.tar.bz2
Python-Textbook-Companions-c7fe425ef3c5e8804f2f5de3d8fffedf5e2f1131.zip
1 files changed, 656 insertions, 0 deletions
diff --git a/Thermodynamics_for_Engineers/Chapter_11_2.ipynb b/Thermodynamics_for_Engineers/Chapter_11_2.ipynb
new file mode 100755
index 00000000..3a5ea2bf
--- /dev/null
+++ b/Thermodynamics_for_Engineers/Chapter_11_2.ipynb
@@ -0,0 +1,656 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:902aab6334eafa9c076aa9433328b057ddfc9db48d6910774ed6bb3aa051c994"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Chapter 11 - Thermodynamics of Fluid flow"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 1 - Pg 181"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#calculate the reynolds number\n",
+      "#Initialization of variables\n",
+      "d=2.067 #in\n",
+      "P=20 #psia\n",
+      "R=53.35 \n",
+      "T=600 #R\n",
+      "mu=0.0486 #lb /ft.hr\n",
+      "v=50 #ft/s\n",
+      "#calculations\n",
+      "rho=P*144./(R*T)\n",
+      "Re=d*v*rho*3600./(12*mu)\n",
+      "#results\n",
+      "print '%s %d' %(\"Reynolds number = \",Re)\n",
+      "print '%s' %('The answers are a bit different due to rounding off error in textbook')\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Reynolds number =  57398\n",
+        "The answers are a bit different due to rounding off error in textbook\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 2 - Pg 184"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#calculate the change in pressure and the percent change\n",
+      "#Initialization of variables\n",
+      "eps=0.00015 \n",
+      "D=2.067/12. #ft\n",
+      "l=100 #ft\n",
+      "P=20 #psia\n",
+      "R=53.35 \n",
+      "T=600 #R\n",
+      "mu=0.0486 #lb /ft.hr\n",
+      "v=50 #ft/s\n",
+      "g=32.17 #ft/s^2\n",
+      "#calculations\n",
+      "rho=P*144/(R*T)\n",
+      "Re=D*v*rho*3600./(mu)\n",
+      "ed=eps/D\n",
+      "print '%s' %(\"From figure 11.5\")\n",
+      "f=0.0235\n",
+      "dp=f*l*rho*v*v /(2*D*g) /144.\n",
+      "change=dp/P *100.\n",
+      "#results\n",
+      "print '%s %.2f %s' %(\"Change in pressure =\",dp,\"psi\")\n",
+      "print '%s %.2f %s' %(\"\\n Percentage change in pressure =\",change,\" percent\")\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "From figure 11.5\n",
+        "Change in pressure = 0.33 psi\n",
+        "\n",
+        " Percentage change in pressure = 1.66  percent\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 3 - Pg 190"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#calculate the final pressure\n",
+      "#Initialization of variables\n",
+      "v1=60. #ft/s\n",
+      "d1=10. #in\n",
+      "d2=15. #in\n",
+      "P=15. #psia\n",
+      "R=53.35\n",
+      "T=540. #R\n",
+      "g=32.17 #ft/s^2\n",
+      "v1=60. #ft/s\n",
+      "#calculations\n",
+      "v2=v1*d1*d1 /d2/d2\n",
+      "rho=P*144/(R*T)\n",
+      "dp=rho*(v2*v2 -v1*v1)/(2*g) /144.\n",
+      "p2=P-dp\n",
+      "#results\n",
+      "print '%s %.2f %s' %(\"Final pressure =\",p2,\" psia\")\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Final pressure = 15.02  psia\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 4 - Pg 192"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#calculate the change in entropy\n",
+      "#Initialization of variables\n",
+      "J=778 #ft.lb/Btu\n",
+      "D=2.067/12. #ft\n",
+      "l=100 #ft\n",
+      "P=20 #psia\n",
+      "R=53.35 \n",
+      "T=600 #R\n",
+      "mu=0.0486 #lb /ft.hr\n",
+      "v=50 #ft/s\n",
+      "g=32.17 #ft/s^2\n",
+      "#calculations\n",
+      "f=0.0235\n",
+      "ds=f*v*v *l /(J*2*D*g*T)\n",
+      "#results\n",
+      "print '%s %.6f %s' %(\"Change in entropy =\",ds,\" Btu/lbm R\")\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Change in entropy = 0.001136  Btu/lbm R\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 5 - Pg 193"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#calculate the pressure, enthalpy, entropy, temperature and density of the gas\n",
+      "#Initialization of variables\n",
+      "v=210 #ft/s\n",
+      "g=32.17 #ft/s^2\n",
+      "p=200. #psia\n",
+      "z=5. #ft\n",
+      "x=2.361\n",
+      "h=1210.3\n",
+      "J=778.\n",
+      "#calculations\n",
+      "P0=p + v*v /(2*g*144*x) + z/(144*x)\n",
+      "h0=h + v*v /(2*J*g) +z/J\n",
+      "S=1.5594 #units/lb\n",
+      "S0=S\n",
+      "t0=401.9 #F\n",
+      "v0=2.342 #cu ft/lb\n",
+      "rho0=1./v0\n",
+      "#results\n",
+      "print '%s %d %s' %(\"Pressure =\",P0,\"psia\")\n",
+      "print '%s %.2f %s' %(\"\\n Enthalpy =\",h0,\" Btu/lb\")\n",
+      "print '%s %.4f %s' %(\"\\n Entropy  =\",S0,\"units/lb\")\n",
+      "print '%s %.1f %s' %(\"\\n Temperature =\",t0,\" F\")\n",
+      "print '%s %.3f %s' %(\"\\n Density =\",rho0,\" lb/cu ft\")\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Pressure = 202 psia\n",
+        "\n",
+        " Enthalpy = 1211.19  Btu/lb\n",
+        "\n",
+        " Entropy  = 1.5594 units/lb\n",
+        "\n",
+        " Temperature = 401.9  F\n",
+        "\n",
+        " Density = 0.427  lb/cu ft\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 6 - Pg 197"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#calculate the exit temperature\n",
+      "#Initialization of variables\n",
+      "%matplotlib inline\n",
+      "import matplotlib\n",
+      "from matplotlib import pyplot\n",
+      "import math\n",
+      "p1=40. #psia\n",
+      "t1=80. #F\n",
+      "p2=30. #psia\n",
+      "ar=0.5 #sq ft\n",
+      "v1=200. #ft/s\n",
+      "R=53.35\n",
+      "cp=0.24\n",
+      "g=32.17\n",
+      "J=778.\n",
+      "#calculations\n",
+      "rho1=144*p1/(R*(t1+460))\n",
+      "G=rho1*v1\n",
+      "h10= cp*t1 + p1*p1 /(2*g*rho1*rho1 *J)\n",
+      "t2=78 #F\n",
+      "h2=cp*t2\n",
+      "g2=h10-h2\n",
+      "rho2=math.sqrt(p1*p1 /(2*g*g2*J))\n",
+      "P2=rho2*R*(t2+460)/144. \n",
+      "ds2=cp*math.log((t2+460.)/(t1+460.)) - R/J *math.log(P2/p1)\n",
+      "t3=77 #F\n",
+      "h3=cp*t3\n",
+      "g3=h10-h3\n",
+      "rho3=math.sqrt(p1*p1 /(2*g*g3*J))\n",
+      "P3=rho3*R*(t3+460)/144. \n",
+      "ds3=cp*math.log((t3+460.)/(t1+460.)) - R/J *math.log(P3/p1)\n",
+      "t4=79 #F\n",
+      "h4=cp*t4\n",
+      "g4=h10-h4\n",
+      "rho4=math.sqrt(p1*p1 /(2*g*g4*J))\n",
+      "P4=rho4*R*(t4+460)/144. \n",
+      "ds4=cp*math.log((t4+460)/(t1+460.)) - R/J *math.log(P4/p1)\n",
+      "h5=18.62\n",
+      "t5=h5/cp\n",
+      "Gv=([h4, h2, h3])\n",
+      "Pv=([P4, P2, P3])\n",
+      "Sv=([ds4, ds2, ds3])\n",
+      "pyplot.figure(1)\n",
+      "pyplot.title(\"Fanno line diagram , Enthalpy vs Entropy\")\n",
+      "pyplot.xlabel(\"Entropy\")\n",
+      "pyplot.ylabel(\"Enthalpy Btu/lb\")\n",
+      "pyplot.plot(Sv,Gv)\n",
+      "\n",
+      "pyplot.figure(2)\n",
+      "pyplot.title(\"Fanno line diagram , Pressure vs Entropy\")\n",
+      "pyplot.xlabel(\"Entropy\")\n",
+      "pyplot.ylabel(\"Pressure psia\")\n",
+      "pyplot.plot(Sv,Pv)\n",
+      "#results\n",
+      "print '%s %.1f %s' %(\"Temperature at exit =\",t5,\" F\")\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n",
+        "Temperature at exit = 77.6  F"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "WARNING: pylab import has clobbered these variables: ['f']\n",
+        "`%matplotlib` prevents importing * from pylab and numpy\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEZCAYAAAC99aPhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niYiIZEWJREREsvL/2P9NFOaFOqMAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x5bd2c30>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEZCAYAAABiu9n+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x5bf3270>"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 7 - Pg 199"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#calculate the velocity required\n",
+      "#Initialization of variables\n",
+      "p1=40 #psia\n",
+      "t1=80. #F\n",
+      "p2=30 #psia\n",
+      "ar=0.5 #sq ft\n",
+      "v1=200 #ft/s\n",
+      "R=53.35\n",
+      "cp=0.24\n",
+      "g=32.17\n",
+      "J=778.\n",
+      "t2=78. #F\n",
+      "#calculations\n",
+      "G=40 #lb/sq ft/sec\n",
+      "rho2=144*p2/(R*(t2+460.))\n",
+      "v2=p1/rho2\n",
+      "#results\n",
+      "print '%s %d %s' %(\"Velocity =\",v2,\"ft/s\")\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Velocity = 265 ft/s\n"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 8 - Pg 206"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#calculate the velocity, diameter, specific volume, density and diameter of nozzle\n",
+      "#Initialization of variables\n",
+      "%matplotlib inline\n",
+      "import math\n",
+      "import numpy\n",
+      "import matplotlib\n",
+      "from matplotlib import pyplot\n",
+      "P2=([180., 160., 140., 120., 100., 80., 60., 40., 20.])\n",
+      "k=1.4\n",
+      "p1=200 #psia\n",
+      "t1=240+460. #R\n",
+      "cp=0.24\n",
+      "J=778.\n",
+      "gc=32.2\n",
+      "R=53.35\n",
+      "m=4. #lb/sec\n",
+      "#calculations\n",
+      "b = len(P2)\n",
+      "pr=numpy.zeros(b);\n",
+      "prr=numpy.zeros(b);\n",
+      "T2=numpy.zeros(b);\n",
+      "dt=numpy.zeros(b);\n",
+      "dh=numpy.zeros(b);\n",
+      "v2=numpy.zeros(b);\n",
+      "vol=numpy.zeros(b);\n",
+      "A2=numpy.zeros(b);\n",
+      "dia=numpy.zeros(b);\n",
+      "rad=numpy.zeros(b);\n",
+      "den=numpy.zeros(b);\n",
+      "for i in range (0,b):\n",
+      "\tpr[i] = p1/P2[i]\n",
+      "\tprr[i]=math.pow(pr[i],((k-1)/k))\n",
+      "\tT2[i]=t1 /prr[i]\n",
+      "\tdt[i]= t1 -T2[i]\n",
+      "\tdh[i]=dt[i]*cp\n",
+      "\tv2[i]=math.sqrt(2*gc*J*dh[i])\n",
+      "\tvol[i]=(R*T2[i]) /(P2[i]*144.)\n",
+      "\tA2[i]=m*vol[i]*144. /v2[i]\n",
+      "\tdia[i]=math.sqrt(4/ math.pi *A2[i])\n",
+      "\trad[i]=dia[i]/2.\n",
+      "\tden[i]=1. /vol[i]\n",
+      "\n",
+      "pyplot.figure(1)\n",
+      "pyplot.title ('Velocity vs pressure')\n",
+      "pyplot.xlabel('Pressure in psia')\n",
+      "pyplot.ylabel('velocity in ft/s')\n",
+      "pyplot.plot(P2,v2)\n",
+      "\n",
+      "pyplot.figure(2)\n",
+      "pyplot.title('specific volume vs pressure')\n",
+      "pyplot.xlabel('Pressure in psia')\n",
+      "pyplot.ylabel('specific volume in cu ft/lb')\n",
+      "pyplot.plot(P2,vol)\n",
+      "\n",
+      "pyplot.figure(3)\n",
+      "pyplot.title('Radius vs Pressure')\n",
+      "pyplot.xlabel('Pressure in psia')\n",
+      "pyplot.ylabel('Radius in in')\n",
+      "plot(P2,rad)\n",
+      "#results\n",
+      "print '%s' %('Velocity in ft/s')\n",
+      "print (v2)\n",
+      "print '%s' %('Specific volume in cu ft/lb')\n",
+      "print (vol)\n",
+      "print '%s' %('Density in lb/cu ft')\n",
+      "print (den)\n",
+      "print '%s' %('Diameter of nozzle in in')\n",
+      "print (dia)\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n",
+        "Velocity in ft/s"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "[  499.61112617   721.04131991   903.0653517   1069.14361272  1229.75524527\n",
+        "  1392.40468067  1565.24666139  1761.46433404  2014.34825773]\n",
+        "Specific volume in cu ft/lb\n",
+        "[ 1.39805384  1.52076257  1.6729549   1.86768387  2.12745999  2.4950706\n",
+        "  3.06425423  4.09358926  6.71623201]\n",
+        "Density in lb/cu ft\n",
+        "[ 0.71528003  0.65756484  0.59774475  0.53542252  0.47004409  0.40079026\n",
+        "  0.32634368  0.2442844   0.14889301]\n",
+        "Diameter of nozzle in in\n",
+        "[ 1.43255795  1.24370371  1.16559809  1.1318789   1.12638708  1.14637045\n",
+        "  1.19822204  1.30551392  1.56373036]\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEZCAYAAAC5AHPcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7b96290>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEZCAYAAACQK04eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4hzkiETAuWYAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x5d968d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEZCAYAAABrUHmEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x5d7e810>"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 9 - Pg 210"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#calcualte the nozzle exit area\n",
+      "#Initialization of variables\n",
+      "import math\n",
+      "p1=200. #psia\n",
+      "t1=480. #F\n",
+      "eff=0.95\n",
+      "g=32.2 #ft/s^2\n",
+      "J=778.\n",
+      "mf=3.4 #lb/s\n",
+      "#calculations\n",
+      "print '%s' %(\"From steam tables,\")\n",
+      "h1=1257.8 \n",
+      "h2=1210.5 \n",
+      "dh=eff*(h1-h2)\n",
+      "ve=math.sqrt(2*g*J*dh)\n",
+      "h3=h1-dh\n",
+      "vs=3.961\n",
+      "Ae=mf*vs/ve *144.\n",
+      "#results\n",
+      "print '%s %.3f %s' %(\"Nozzle exit area =\",Ae,\"sq.in\")\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "From steam tables,\n",
+        "Nozzle exit area = 1.292 sq.in\n"
+       ]
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 10 - Pg 216"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#calculate the final temperature, pressure and exit velocity in both cases\n",
+      "#Initialization of variables\n",
+      "import math\n",
+      "import numpy\n",
+      "from numpy import roots\n",
+      "R=53.35\n",
+      "v=300. #ft/s\n",
+      "p=100 #psia\n",
+      "t1=200 #F\n",
+      "q=500 #Btu/s\n",
+      "gc=32.2 #ft/s^2\n",
+      "J=778\n",
+      "#calculations\n",
+      "rho1=p*144/(R*(460.+t1))\n",
+      "s=([1., -0.206, 0.00535])\n",
+      "vec=numpy.roots(s)\n",
+      "rho2=vec[0]\n",
+      "t2=(236.6 - 0.301/rho2/rho2)/0.248\n",
+      "P2=rho2*R*(t2+462) /144.\n",
+      "v2=math.sqrt(2*gc*J*(236.6-0.248*t2))\n",
+      "v22=rho1*v/rho2\n",
+      "#results\n",
+      "print '%s %.1f %s' %(\"Final temperature =\",t2,\" F\")\n",
+      "print '%s %.1f %s' %(\"\\n Final pressure =\",P2,\" psia\")\n",
+      "print '%s %.1f %s' %(\"\\n Exit velocity in case 1 =\",v2,\"ft/s\")\n",
+      "print '%s %.1f %s' %(\"\\n Exit velocity in case 2 =\",v22,\" ft/s\")\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Final temperature = 914.6  F\n",
+        "\n",
+        " Final pressure = 89.5  psia\n",
+        "\n",
+        " Exit velocity in case 1 = 699.7 ft/s\n",
+        "\n",
+        " Exit velocity in case 2 = 699.0  ft/s\n"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
+\ No newline at end of file
author	hardythe1	2015-04-07 15:58:05 +0530
committer	hardythe1	2015-04-07 15:58:05 +0530
commit	c7fe425ef3c5e8804f2f5de3d8fffedf5e2f1131 (patch)
tree	725a7d43dc1687edf95bc36d39bebc3000f1de8f /Thermodynamics_for_Engineers/Chapter_11_2.ipynb
parent	62aa228e2519ac7b7f1aef53001f2f2e988a6eb1 (diff)
download	Python-Textbook-Companions-c7fe425ef3c5e8804f2f5de3d8fffedf5e2f1131.tar.gz Python-Textbook-Companions-c7fe425ef3c5e8804f2f5de3d8fffedf5e2f1131.tar.bz2 Python-Textbook-Companions-c7fe425ef3c5e8804f2f5de3d8fffedf5e2f1131.zip