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diff --git a/Transport_Phenomena/ch14.ipynb b/Transport_Phenomena/ch14.ipynb new file mode 100644 index 00000000..942ef967 --- /dev/null +++ b/Transport_Phenomena/ch14.ipynb @@ -0,0 +1,620 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "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": [ + "# Estimate the viscosity of air at 40\u00b0C (313.15 K) and atmospheric pressure;\n", + "\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", + "Calculate the thermal conductivity of air and of argon at 40\u00b0C and\n", + "1 atm using the Chapman-Enskog equation.\n", + "'''\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", + "Calculate the thermal conductivity of air at 40\u00b0C and 1 atm, given\n", + "that the heat capacity cp is 1005 J kg\n", + "'''\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", + "(a) Use the Chapman-Enskog equation to find the diffusion coefficient at 413 K,\n", + "6OOK, 9OOK, and 12OOK.\n", + "(b) Use. the experimental diffusion coefficient and the Chapman-Enskog equa-\n", + "tion to estimate the diffusion coefficients in part (a).\n", + "(c) Use Eq. (2.50).\n", + "(d) Compare all answers with experimental results\n", + "'''\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": [ + "# Compare this result with that predicted by the Chapman-Enskog theory.\n", + "\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", + "Find the diffusion coefficient of the helium-1-propanol system at\n", + "423.2 K and 5 atm using the FSG correlation\n", + "'''\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": [ + "# Prepare a plot of viscosity of water between 273 K and 373 K;\n", + "\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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\nhCQcIYQQNlHnOw1oXZ8jK1gvhBCiush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+ "text": [ + "<matplotlib.figure.Figure at 0x3701990>" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Example 14.8 - Page No :737\n" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "'''\n", + "Estimate the thermal conductivity of tetrachloromethane\n", + "'''\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": [ + "# Compare the diffusion coefficient of water drffusing\n", + "\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": {} + } + ] +}
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