{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 15 : Non newtonian phenomena"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 15.1 - Page No :760\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# find the power law parameters\n",
      "\n",
      "%pylab inline\n",
      "\n",
      "import math \n",
      "from numpy import *\n",
      "from matplotlib.pyplot import *\n",
      "\n",
      "# Variables\n",
      "# given\n",
      "r = array([10, 20, 50, 100, 200, 400, 600, 1000, 2000])\n",
      "tau = array([2.2, 3.1 ,4.4, 5.8, 7.4, 9.8, 11.1, 13.9, 17.0])\n",
      "\n",
      "# Calculation and Results\n",
      "#tau = tau*(10**-4);\n",
      "plot(r,tau);\n",
      "plot(r,tau,'ro');\n",
      "suptitle(\"asic shear diagram for the fluid in\")\n",
      "xlabel(\"Shear rate, S**-1 \")\n",
      "ylabel(\"Shear streets, Nm**-2 \")\n",
      "\n",
      "# the data falls nearly on a straight line\n",
      "# from the graph the slope and the intercept are\n",
      "slope = 0.3841;\n",
      "intercept = 9.17046;\n",
      "# from the relation tau = K*(-r)**n;\n",
      "K = math.exp(intercept);\n",
      "n = slope\n",
      "print \"K = \",K\n",
      "print \"n = \",n\n",
      "print \" The fluid_ is pseudo plastic, since the slope is less than 1 \"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        "K = "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 9609.04383369\n",
        "n =  0.3841\n",
        " The fluid_ is pseudo plastic, since the slope is less than 1 \n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4uFh4eXnpPK4OYdm0tG3bxDteXkKo5sUTAhCxjl7iab9t4sgRc0dHRJbKkJ+d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\n0dGmDoWIiHSwyOkziIjIvMz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       "text": [
        "<matplotlib.figure.Figure at 0x24156d0>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 15.2 - Page No :774\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# find the parameters in Eq. (10.5). The literature value of n\u2019 using 25 points is 0.887\n",
      "\n",
      "# Variables\n",
      "a = array([651, 1361, 2086, 5089, 7575, 11140, 19270, 25030])\n",
      "tau = array([3.71, 7.49, 11.41, 24.08, -35.21, 46.25, 77.50, 96.68])\n",
      "\n",
      "# from the graph\n",
      "betao = -4.3790154;\n",
      "beta1 = 0.8851;\n",
      "\n",
      "# Calculations\n",
      "K = math.exp(betao);\n",
      "n = beta1;\n",
      "plot(a,tau);\n",
      "suptitle(\"Capillary shear diagram for polyisobutylene L-80 in cyclohexane.\")\n",
      "xlabel(\"Pseudoshear rate\")\n",
      "ylabel(\"Wall shear stress \")\n",
      "\n",
      "# Results\n",
      "print \" The final rheological model_ is  tauw  =  %f*8*Uz,avg/do)**%f\"%(K,n);\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The final rheological model_ is  tauw  =  0.012538*8*Uz,avg/do)**0.885100\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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f5ORJGDVK2ydECHH/SjSP5Nq1a8TFxeHv72/JmIok80hESWRkwCefwPvvwzPP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\nCCFEqUgiEUIIUSr/D5BULYf7jJs0AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x330ed50>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 15.3 - Page No :774\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Find the power-law parameters for the data of Example 15.2 (Table 15.2).\n",
      "\n",
      "# Variables\n",
      "# from Example  15.2 \n",
      "n = 0.8851;\n",
      "K = 0.01254;\n",
      "n = n;\n",
      "\n",
      "# Calculations\n",
      "K = K/((3*n+1)/(4*n));\n",
      "\n",
      "# Results\n",
      "print \"n = \",n\n",
      "print \"K  =  %f N/m**2\"%(K);\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "n =  0.8851\n",
        "K  =  0.012146 N/m**2\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 15.4 - Page No :775\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Obtain the basic shear diagram.\n",
      "\n",
      "# Variables\n",
      "a = array([10, 20, 50, 100, 200, 400, 600, 1000, 2000])\n",
      "tau = array([2.24, 3.10, 4.35, 5.77, 7.50, 9.13, 11.0, 13.52, 16.40])\n",
      "tau = tau*10**-4;\n",
      "betao = 8.96694;\n",
      "beta1 = 0.48452520;\n",
      "beta2 = 0.010923041;\n",
      "\n",
      "# Calculations\n",
      "# such a plot suggests a second order polynomila  of the type y = betao+beta1*x+beta2*x**2;\n",
      "# where y = ln(tauw) and x = ln(8*Uz,avg/do) = ln(a);\n",
      "# from the graph\n",
      "n = beta1+2.*beta2*a;\n",
      "phiw = ((3.*n+1.)/(4.*n))*(a);\n",
      "mu = tau/phiw;\n",
      "\n",
      "# Results\n",
      "\n",
      "print \" 8*Uz,avg/do     n         ((3*n+1)/4*n)    phiw            mu\"\n",
      "for i in range(9):\n",
      "    print \" %6.0f       %8.4f      %8.4f      %8.4f       %6.6f\"%(a[i],n[i],3*n[i]+1/4*n[i],phiw[i],mu[i])\n",
      "\n",
      "\n",
      "# Answer in book is wrong. Please calculate manually."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8*Uz,avg/do     n         ((3*n+1)/4*n)    phiw            mu\n",
        "     10         0.7030        2.1090       11.0563       0.000020\n",
        "     20         0.9214        2.7643       20.4262       0.000015\n",
        "     50         1.5768        4.7305       45.4273       0.000010\n",
        "    100         2.6691        8.0074       84.3663       0.000007\n",
        "    200         4.8537       14.5612      160.3013       0.000005\n",
        "    400         9.2230       27.6689      310.8425       0.000003\n",
        "    600        13.5922       40.7765      461.0358       0.000002\n",
        "   1000        22.3306       66.9918      761.1954       0.000002\n",
        "   2000        44.1767      132.5301      1511.3182       0.000001\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}