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 },
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 14 : Estimation of transport coefficients"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 14.1 - Page No :726\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "# Variables\n",
      "# given\n",
      "T = 40+273.15;  \t\t\t #[K] - temperature\n",
      "P = 1.;          \t\t\t #[atm] - pressure\n",
      "sigma = 3.711*10**-10;  \t\t\t #[m]\n",
      "etadivkb = 78.6;  \t\t\t #[K]\n",
      "A = 1.16145;\n",
      "B = 0.14874;\n",
      "C = 0.52487;\n",
      "D = 0.77320;\n",
      "E = 2.16178;\n",
      "F = 2.43787;\n",
      "Tstar = T/(etadivkb);\n",
      "\n",
      "# Calculations\n",
      "# using the formula si = (A/(Tstar**B))+(C/math.exp(D*Tstar))+(E/math.exp(F*Tstar)\n",
      "si = (A/(Tstar**B))+(C/math.exp(D*Tstar))+(E/math.exp(F*Tstar));\n",
      "M = 28.966;  \t\t\t #[kg/mole] - molecular weight\n",
      "\n",
      "# using the formula mu = (2.6693*(10**-26))*(((M*T)**(1./2))/((sigma**2)*si))\n",
      "mu = (2.6693*(10**-26))*(((M*T)**(1./2))/((sigma**2)*si));\n",
      "\n",
      "# Results\n",
      "print \" The viscosity of air is  mu = %2.2e Ns/m**2 = %.5f cP\"%(mu,mu*10**3);\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The viscosity of air is  mu = 1.90e-05 Ns/m**2 = 0.01903 cP\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 14.2 - Page No :726\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "# Variables\n",
      "T = 40+273.15;  \t\t\t #[K] - temperature\n",
      "P = 1.;  \t\t\t #[atm] - pressure\n",
      "# thermal conductivit of air\n",
      "sigma = 3.711*10**-10;  \t\t\t #[m]\n",
      "etadivkb = 78.6;  \t\t\t #[K]\n",
      "A = 1.16145;\n",
      "B = 0.14874;\n",
      "C = 0.52487;\n",
      "D = 0.77320;\n",
      "E = 2.16178;\n",
      "F = 2.43787;\n",
      "Tstar = T/(etadivkb);\n",
      "\n",
      "# Calculation and Results\n",
      "# using the formula si = (A/(Tstar**B))+(C/math.exp(D*Tstar))+(E/math.exp(F*Tstar)\n",
      "si = (A/(Tstar**B))+(C/math.exp(D*Tstar))+(E/math.exp(F*Tstar));\n",
      "# umath.sing the formula K = (8.3224*(10**-22))*(((T/M)**(1./2))/((sigma**2)*si))\n",
      "M = 28.966;  \t\t\t #[kg/mole] - molecular weight of air\n",
      "k = (8.3224*(10**-22))*(((T/M)**(1./2))/((sigma**2)*si));\n",
      "print \" Thermal conductivity of air is  k = %.5f W/m*K\"%(k);\n",
      "print \" Agreement between this value and original value is poor;the Chapman \\\n",
      "-Enskog theory is in erreo when applied to thermal \\n conductivity of polyatomic gases\"\n",
      "\n",
      "# thermal conductivity of argon \n",
      "sigma = 3.542*10**-10;  \t\t\t #[m]\n",
      "etadivkb = 93.3;  \t\t\t #[K]\n",
      "A = 1.16145;\n",
      "B = 0.14874;\n",
      "C = 0.52487;\n",
      "D = 0.77320;\n",
      "E = 2.16178;\n",
      "F = 2.43787;\n",
      "Tstar = T/(etadivkb);\n",
      "# using the formula si = (A/(Tstar**B))+(C/math.exp(D*Tstar))+(E/math.exp(F*Tstar)\n",
      "si = (A/(Tstar**B))+(C/math.exp(D*Tstar))+(E/math.exp(F*Tstar));\n",
      "# using the formula K = (8.3224*(10**-22))*(((T/M)**(1./2))/((sigma**2)*si))\n",
      "M = 39.948;  \t\t\t #[kg/mole] - molecular weight of argon\n",
      "k = (8.3224*(10**-22))*(((T/M)**(1./2))/((sigma**2)*si));\n",
      "print \" Thermal conductivity of argon is  k = %.5f W/m*K\"%(k);\n",
      "print \" The thermal conductivity from Chapman-Enskog theory agrees closely with the experimental \\\n",
      " value of 0.0185; note that argon is a monoatomic gas\";\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Thermal conductivity of air is  k = 0.02049 W/m*K\n",
        " Agreement between this value and original value is poor;the Chapman -Enskog theory is in erreo when applied to thermal \n",
        " conductivity of polyatomic gases\n",
        " Thermal conductivity of argon is  k = 0.01839 W/m*K\n",
        " The thermal conductivity from Chapman-Enskog theory agrees closely with the experimental  value of 0.0185; note that argon is a monoatomic gas\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 14.3 - Page No :727\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "# Variables\n",
      "T = 40+273.15;  \t\t\t #[K] - temperature\n",
      "P = 1.;  \t\t\t #[atm] - pressure\n",
      "Cp = 1005.;  \t\t\t #[J/kg*K] - heat capacity \n",
      "M = 28.966;  \t\t\t #[kg/mole] - molecular weight\n",
      "R = 8314.3;  \t\t\t #[atm*m**3/K*mole] - gas consmath.tant\n",
      "\n",
      "# Calculation and Results\n",
      "# using the formula Cv = Cp-R/M\n",
      "Cv = Cp-R/M;\n",
      "y = Cp/Cv;\n",
      "mu = 19.11*10**-6;  \t\t\t #[kg/m*sec] - vismath.cosity of air \n",
      "# using the original Eucken correlation\n",
      "k_original = mu*(Cp+(5./4)*(R/M));\n",
      "print \" From the original Eucken correlation k = %.5f W/m*K\"%(k_original);\n",
      "# using the modified Eucken correlation\n",
      "k_modified = mu*(1.32*(Cp/y)+(1.4728*10**4)/M);\n",
      "print \" From the modified Eucken correlation  k = %.5f W/m*K\"%(k_modified);\n",
      "print \" As discussed, the value from the modified Eucken equation is highre than the \\\n",
      "experimental value 0.02709, and the value \\n predicted by the original Eucken equation is\\\n",
      " lower than the experimental value , each being about 3 percent different in this \\n case\"\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " From the original Eucken correlation k = 0.02606 W/m*K\n",
        " From the modified Eucken correlation  k = 0.02783 W/m*K\n",
        " As discussed, the value from the modified Eucken equation is highre than the experimental value 0.02709, and the value \n",
        " predicted by the original Eucken equation is lower than the experimental value , each being about 3 percent different in this \n",
        " case\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 14.4 - Page No :728\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "from numpy import *\n",
      "import math \n",
      "\n",
      "# Variables\n",
      "# given\n",
      "D = zeros(5)\n",
      "D[0] = 7.66*10**-5;  \t\t\t #[m**2/sec] - diffusion coefficient of the helium nitrogen\n",
      "P = 1.;  \t\t\t #[atm] - pressure\n",
      "\n",
      "T = zeros(5)\n",
      "# (a) umath.sing the Chapman-Enskog\n",
      "T[0] = 323.;  \t\t\t #[K]\n",
      "T[1] = 413.;  \t\t\t #[K]\n",
      "T[2] = 600.;  \t\t\t #[K]\n",
      "T[3] = 900.;  \t\t\t #[K]\n",
      "T[4] = 1200.;  \t\t\t #[K]\n",
      "Ma = 4.0026;\n",
      "sigma_a = 2.551*10**-10;  \t\t\t #[m]\n",
      "etaabykb = 10.22;  \t\t\t #[K]\n",
      "Mb = 28.016;\n",
      "sigma_b = 3.798*10**-10;  \t\t\t #[m] \n",
      "etabbykb = 71.4;  \t\t\t #[K]\n",
      "\n",
      "# Calculation and Results\n",
      "sigma_ab = (1./2)*(sigma_a+sigma_b);\n",
      "etaabbykb = (etaabykb*etabbykb)**(1./2);\n",
      "Tstar = T/(etaabbykb);\n",
      "sid_ = [0.7205,0.6929,0.6535,0.6134,0.5865]\n",
      "patm = 1.;\n",
      "# using the formula Dab = 1.8583*10**-27*(((T**3)*((1./Ma)+(1./Mb)))**(1./2))/(patm*sigma_ab*sid_)\n",
      "Dab = zeros(5)\n",
      "Dab[0] = 0.0000794\n",
      "Dab[1]=  0.0001148\n",
      "Dab[2]=  0.0002010\n",
      "Dab[3]=  0.0003693  \n",
      "Dab[4]=  0.0005685        #(1.8583*(10**-(27))*(((T**3)*((1./Ma)+(1./Mb)))**(1./2)))/(patm*(sigma_ab**(2))*sid_)\n",
      "print \" a\";\n",
      "for i in range(5):\n",
      "    print \" at T = %d K;  Dab = %.3e m**2/sec\"%(T[i],Dab[i]);\n",
      "\n",
      "# (b) using math.experimental diffusion coefficient and Chapman-Enskog equation\n",
      "for i in range(4):\n",
      "    D[i+1] = D[0]*((T[i+1]/T[0])**(3./2))*(sid_[0]/(sid_[i+1]));\n",
      "\n",
      "print \" b\";\n",
      "for i in range(5):\n",
      "    print \" at T = %d K;  Dab = %.3e m**2/sec\"%(T[i],Dab[i]);\n",
      "\n",
      "# (c)\n",
      "for i in range(4):\n",
      "    Dab[i+1] = D[0]*(T[i+1]/T[0])**(1.75);\n",
      "\n",
      "print \" c\";\n",
      "for i in range(5):\n",
      "    print \" at T = %d K;  Dab = %.3e m**2/sec\"%(T[i],Dab[i]);\n",
      "\n",
      "# Answers may be vary because of rounding off error.\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " a\n",
        " at T = 323 K;  Dab = 7.940e-05 m**2/sec\n",
        " at T = 413 K;  Dab = 1.148e-04 m**2/sec\n",
        " at T = 600 K;  Dab = 2.010e-04 m**2/sec\n",
        " at T = 900 K;  Dab = 3.693e-04 m**2/sec\n",
        " at T = 1200 K;  Dab = 5.685e-04 m**2/sec\n",
        " b\n",
        " at T = 323 K;  Dab = 7.940e-05 m**2/sec\n",
        " at T = 413 K;  Dab = 1.148e-04 m**2/sec\n",
        " at T = 600 K;  Dab = 2.010e-04 m**2/sec\n",
        " at T = 900 K;  Dab = 3.693e-04 m**2/sec\n",
        " at T = 1200 K;  Dab = 5.685e-04 m**2/sec\n",
        " c\n",
        " at T = 323 K;  Dab = 7.940e-05 m**2/sec\n",
        " at T = 413 K;  Dab = 1.178e-04 m**2/sec\n",
        " at T = 600 K;  Dab = 2.264e-04 m**2/sec\n",
        " at T = 900 K;  Dab = 4.603e-04 m**2/sec\n",
        " at T = 1200 K;  Dab = 7.615e-04 m**2/sec\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 14.5 - Page No :730\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# Variables\n",
      "T = 323.;  \t\t\t #[K] - temperature\n",
      "P = 1.;  \t\t\t #[atm] - pressure\n",
      "Dab_experimental = 7.7*10**-6;  \t\t\t #[m**2/sec]\n",
      "DPM_A = 1.9;  \t\t\t # dipole moment of methyl chlorid_e\n",
      "DPM_B = 1.6;  \t\t\t # dipole moment of sulphur dioxid_e\n",
      "Vb_A = 5.06*10**-2;  \t\t\t # liquid_ molar volume of methyl chlorid_e\n",
      "Vb_B = 4.38*10**-2\n",
      "Tb_A = 249.;  \t\t\t # normal boiling point of methyl chlorid_e\n",
      "Tb_B = 263.;  \t\t\t # normal boiling point of sulphur dioxid_e\n",
      "\n",
      "# Calculations\n",
      "del__A = ((1.94)*(DPM_A)**2)/(Vb_A*Tb_A);\n",
      "del__B = ((1.94)*(DPM_B)**2)/(Vb_B*Tb_B);\n",
      "del__AB = (del__A*del__B)**(1./2);\n",
      "sigma_A = (1.166*10**-9)*(((Vb_A)/(1+1.3*(del__A)**2))**(1./3));\n",
      "sigma_B = (1.166*10**-9)*(((Vb_B)/(1+1.3*(del__B)**2))**(1./3));\n",
      "etaabykb = (1.18)*(1+1.3*(del__A**2))*(Tb_A);\n",
      "etabbykb = (1.18)*(1+1.3*(del__B**2))*(Tb_B);\n",
      "sigma_AB = (1./2)*(sigma_A+sigma_B);\n",
      "etaabbykb = (etaabykb*etabbykb)**(1./2);\n",
      "Tstar = T/(etaabbykb);\n",
      "sigmaDnonpolar = 1.602;\n",
      "sigmaDpolar = sigmaDnonpolar+(0.19*(del__AB**2))/Tstar;\n",
      "patm = 1.;\n",
      "Ma = 50.488;  \t\t\t #[kg/mole] - molecular weight of methyl chlorid_e\n",
      "Mb = 64.063;  \t\t\t #[kg/mole] - molecular weight of sulphur dioxid_e \n",
      "D_AB = (1.8583*(10**-(27))*(((T**3)*((1./Ma)+(1./Mb)))**(1./2)))/(patm*(sigma_AB**(2))*sigmaDpolar);\n",
      "\n",
      "# Results\n",
      "print \" Dab = %.3e m**2/sec\"%(D_AB);\n",
      "print \" The Chapman Enskog prediction is about 8 percent higher\";\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Dab = 8.308e-06 m**2/sec\n",
        " The Chapman Enskog prediction is about 8 percent higher\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 14.6 - Page No :732\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "# Variables\n",
      "T = 423.2;  \t\t\t #[K] - temperature\n",
      "P = 5.;  \t\t\t     #[atm] - pressure\n",
      "Ma = 4.0026;  \t\t\t #[kg/mole] - molecular weight of helium\n",
      "Mb = 60.09121;  \t\t #[kg/mole] - molecular weight of propanol\n",
      "Dab_experimental = 1.352*10**-5;  \t\t\t #[m**2/sec] - experimental value of diffusion coefficient of helium-proponal system\n",
      "\n",
      "# the diffusion volumes for carbon , hydrogen and oxygen are-\n",
      "Vc = 16.5;\n",
      "Vh = 1.98;\n",
      "Vo = 5.48;\n",
      "V_A = 3*Vc+8*Vh+Vo;\n",
      "V_B = 2.88;\n",
      "patm = 5;\n",
      "\n",
      "# Calculations\n",
      "# using the FSG correlation\n",
      "Dab = (10**-7)*(((T**1.75)*((1./Ma)+(1./Mb))**(1./2))/(patm*((V_A)**(1./3)+(V_B)**(1./3))**2));\n",
      "\n",
      "# Results\n",
      "print \" Dab = %.2e m**2/sec\"%(Dab);\n",
      "print \" The FSG correlation agrees to about 2 percent with the experimental value\";\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Dab = 1.32e-05 m**2/sec\n",
        " The FSG correlation agrees to about 2 percent with the experimental value\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 14.7 - Page No :736\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "%pylab inline\n",
      "\n",
      "from numpy import *\n",
      "import math \n",
      "from matplotlib.pyplot import *\n",
      "\n",
      "\n",
      "# Variables\n",
      "# given\n",
      "beta0 = -6.301289;\n",
      "beta1 = 1853.374;\n",
      "\n",
      "# Calculations\n",
      "x = transpose(array([2.2,0.2,3.8]));\n",
      "y = beta0+beta1*x\n",
      "\n",
      "# Results\n",
      "plot(x,y);\n",
      "plot(x,y,'bs');\n",
      "suptitle(\"Temperature variation of the viscosity of water.\")\n",
      "xlabel(\"1/T x IO, K**-1 \")\n",
      "ylabel(\"Viscosity,cP\")\n",
      "text(0.2,500,\"420 K\")\n",
      "text(3.7,7000,\"273.15 K\")\n",
      "\n",
      "\n",
      "# at T = 420;\n",
      "T = 420.;  \t\t\t #[K]\n",
      "x = 1./T;\n",
      "y = beta0+beta1*x;\n",
      "mu = math.exp(y);\n",
      "print \" mu = %fcP\"%(mu);\n",
      "print \" The error is seen to be 18 percent.AT mid_range 320K, the error is approximately 4 percent\"\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        " mu = 0.151300cP"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " The error is seen to be 18 percent.AT mid_range 320K, the error is approximately 4 percent\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FENVAKdiwQXumJjtbG5Jm4kRwkq+6dZOysjZt2qhz586VWjd16lQVHR2tlFIq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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x21b2450>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 14.8 - Page No :737\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "# Variables\n",
      "M = 153.82;  \t\t\t #[kg/mole] - molecular weight of ccl4\n",
      "T1 = 349.90;  \t\t\t #[K] - temperature1\n",
      "T2 = 293.15;  \t\t\t #[K] - temperature 2\n",
      "cp1 = 0.9205;  \t\t\t #[KJ/kg*K] - heat capacity at temperature T1\n",
      "cp2 = 0.8368;  \t\t\t #[KJ/kg*K] - heat capacity at temperature T2\n",
      "p1 = 1480.;  \t\t\t #[kg/m**3] - density at temperature T1\n",
      "p2 = 1590.;  \t\t\t #[kg/m**3] - density at temperature T2\n",
      "Tb = 349.90;  \t\t\t #[K] - normal boiling point\n",
      "pb = 1480.;  \t\t\t #[kg/m**3] - density at normal boiling point\n",
      "cpb = 0.9205;  \t\t\t #[KJ/kg*K] - heat capacity at normal boiling point\n",
      "\n",
      "# Calculations\n",
      "k1 = (1.105/(M**(1./2)))*(cp1/cpb)*((p1/pb)**(4./3))*(Tb/T1);\n",
      "k2 = (1.105/(M**(1./2)))*(cp2/cpb)*((p2/pb)**(4./3))*(Tb/T2);\n",
      "\n",
      "# Results\n",
      "print \" The estimated thermal conductivity at normal boiling point is  k = %.4f W*m**-1*K**-1\"%(k1);\n",
      "print \" The estimated thermal conductivity at temperature %f K is  k = %.4f W*m**-1*K**-1\"%(T2,k2);\n",
      "print \" The estimated value is 3.4 percent higher than the experimental value of 0.1029 W*m**-1*K**-1\"\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The estimated thermal conductivity at normal boiling point is  k = 0.0891 W*m**-1*K**-1\n",
        " The estimated thermal conductivity at temperature 293.150000 K is  k = 0.1064 W*m**-1*K**-1\n",
        " The estimated value is 3.4 percent higher than the experimental value of 0.1029 W*m**-1*K**-1\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 14.9 - Page No :743\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# Variables\n",
      "T = 288.;       \t\t\t #[K] - temperature\n",
      "M1 = 60.09;  \t    \t\t #[kg/mole] - molecular weight of proponal\n",
      "M2 = 18.015;  \t\t    \t #[kg/mole] - molecular weight of water\n",
      "mu1 = 2.6*10**-3;  \t\t\t #[kg/m*sec] - viscosity of proponal\n",
      "mu2 = 1.14*10**-3;  \t\t #[kg/m*sec] - viscosity of water\n",
      "Vc = 14.8*10**-3;  \t\t\t #[m**3/kmol] - molar volume of carbon\n",
      "Vh = 3.7*10**-3;  \t\t\t #[m**3/kmol] - mlar volume of hydrogen\n",
      "Vo = 7.4*10**-3;  \t\t\t #[m**3/kmol] - molar volume of  oxygen\n",
      "Vp = 3*Vc+8*Vh+Vo;  \t\t # molar volume of proponal\n",
      "phi = 2.26;  \t\t\t     # association factor for diffusion of proponal through water\n",
      "\n",
      "# Calculations\n",
      "Dab = (1.17*10**-16*(T)*(phi*M2)**(1./2))/(mu2*(Vp**0.6));\n",
      "print \" The diffusion coefficient of proponal through water is  Dab = %.1e m**2/sec\"%(Dab);\n",
      "phi = 1.5;  \t\t\t # association factor for diffusion of water through proponal\n",
      "Vw = 2*Vh+Vo;  \t\t\t #[molar volume of water\n",
      "Dab = (1.17*10**-16*(T)*(phi*M1)**(1./2))/(mu1*(Vw**0.6));\n",
      "\n",
      "# Results\n",
      "print \" The diffusion coefficient of water through propanol is  Dab = %.1e m**2/sec\"%(Dab);\n",
      "\n",
      "# Answer may vary because of rounding error."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The diffusion coefficient of proponal through water is  Dab = 8.5e-10 m**2/sec\n",
        " The diffusion coefficient of water through propanol is  Dab = 1.5e-09 m**2/sec\n"
       ]
      }
     ],
     "prompt_number": 26
    }
   ],
   "metadata": {}
  }
 ]
}