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 "metadata": {
  "name": "",
  "signature": "sha256:27e5ceeda6b81a3ecd6f6bb5786adb4aab2ca8fa416667ba9a518e71b4bbf42b"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 4: Motion Fluid Particles and Streams"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 4.1, Page 103"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "from pylab import *\n",
      "import numpy as np\n",
      "%matplotlib inline\n",
      "\n",
      " #Initializing  the  variables\n",
      "x = [0, 23, 28, 31, 32, 29, 22, 14, 0]\n",
      "y = np.linspace(0,80,9)\n",
      "xlabel('Velocity  (m/s)')\n",
      "ylabel('Distance  from  one  side(mm)')\n",
      "title('Velocity Distribution Curve')\n",
      "grid(1)\n",
      "\n",
      " #Calculations\n",
      "plot(x,y,'-*')\n",
      "show()\n",
      "mu=[17.5 , 26.0,  29.6,  31.9,  30.7,  25.4,  18.1,  7.7]\n",
      " #  mean  velocity\n",
      "print \"Mean velocity (m/s):\",round(mean(mu),2)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['linalg', 'draw_if_interactive', 'random', 'power', 'info', 'fft']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Gl21NYPHixQwePJjp06db+pge/vftt9+2bzCpCQhR5D08tXXt2tptplJIfjyb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\nEEKIIkwaASGEKML+H/RWAsao5sbZAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3032e50>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mean velocity (m/s): 23.36\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 4.2, Page 106"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "from pylab import *\n",
      "import numpy as np\n",
      "\n",
      " #Initializing  the  variables  all  unknowns  are  assigned  0\n",
      "\n",
      "d  =  [0.0, 0.05, 0.075, 0, 0.030];\n",
      "Q  =  [0, 0, 0, 0, 0];\n",
      "V  =  [0, 0, 2, 1.5, 0];\n",
      "A  =  [0, 0, 0, 0, 0]\n",
      " #Calculations\n",
      "A2  =  math.pi*d[2]**2/4;\n",
      "Q[2]  =A2*V[2];\n",
      "Q  =  [0,Q[2], Q[2],  Q[2]/1.5 , 0.5*Q[2]/1.5];\n",
      "d[3]  =  (Q[3]*4/(V[3]*math.pi))**0.5;\n",
      "for i in range(0,5):\n",
      "    A[i]  =  math.pi*d[i]**2/4;\n",
      "V[1]  =  V[2]*(A[2]/A[1]);\n",
      "V[4]=Q[4]/A[4];\n",
      "\n",
      "\n",
      "header  =  \"Diameter(mm)  Area(m2)\\t Flow Rate(m3/s)  Velocity(m/s)\"\n",
      "print header\n",
      "for c in range(1,5):\n",
      "    mm=str(round(d[c]*1000,1))+'\\t  '+str(round(A[c],6))+' \\t    '+str(round(Q[c],6))+'     \\t'+str(round(V[c],2))\n",
      "    print mm"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Diameter(mm)  Area(m2)\t Flow Rate(m3/s)  Velocity(m/s)\n",
        "50.0\t  0.001963 \t    0.008836     \t4.5\n",
        "75.0\t  0.004418 \t    0.008836     \t2.0\n",
        "70.7\t  0.003927 \t    0.00589     \t1.5\n",
        "30.0\t  0.000707 \t    0.002945     \t4.17\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 4.3, Page 108"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division\n",
      "import math\n",
      "import sympy\n",
      "from sympy import diff, Symbol\n",
      "\n",
      "\n",
      "#Initializing  the  variables \n",
      "'''\n",
      "def df(x, h=0.1e-5):\n",
      "    return ( f(x+h/2) - f(x-h/2) )/h\n",
      "    return df\n",
      "\n",
      "print df(2*x,h)\n",
      "'''\n",
      "x = Symbol('x') \n",
      "vx  =  3-x\n",
      "vy  =  4+2*x\n",
      "vz  =  2-x  \n",
      "\n",
      " #Calculations\n",
      "delVx  =  vx.diff(x);  \n",
      "delVy  =  vx.diff(x);\n",
      "delVz  =  vx.diff(x);  \n",
      "\n",
      "result  =  delVx+delVy+delVz;#requirement  of  continuity  equation  (result  =  0)\n",
      "print \"Satisfy requirement of continuity \"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Satisfy requirement of continuity \n"
       ]
      }
     ],
     "prompt_number": 10
    }
   ],
   "metadata": {}
  }
 ]
}