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{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Chapter 1 - \"Introduction\""
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 1.1, Page number: 13"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"import math\n",
"\n",
"#Variables\n",
"k=35; #Thermal Conductivity, [W/m*K]\n",
"T1=110 # Temperature of front[C]\n",
"T2=50; # Temperature of back,[C]\n",
"A=0.4 #area of slab,[m**2]\n",
"x=0.03; #Thickness of slab,[m]\n",
"\n",
"#Calculations\n",
"q=-k*(T2-T1)/(1000*x); #formula for heat flux[KW/m^2]\n",
"Q=q*A; #formula for heat transfer rate[KW]\n",
"\n",
"#Results\n",
"print \"Heat flux is:\",q,\"KW/m^2\\n\"\n",
"print \"Heat transfer rate is:\",Q,\"KW \\n\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Heat flux is: 70.0 KW/m^2\n",
"\n",
"Heat transfer rate is: 28.0 KW \n",
"\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 1.2, Page number: 16"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"import math\n",
"from sympy import solve,symbols\n",
"\n",
"#Variables\n",
"x=symbols('x');\n",
"k1=372; # Thermal Conductivity of slab,W/m*K\n",
"x1=0.003; # Thickness of slab,m\n",
"x2=0.002 # Thickness of steel,m\n",
"k2=17; # Thermal Conductivity of steel,W/m*K\n",
"T1=400; # Temperature on one side,C\n",
"T2=100 #Temperature on other side,C\n",
"\n",
"#Calculations\n",
"Tcu=solve(x+2*x*(k1/x1)*(x2/k2)-(T1-T2),x);\n",
"#q=k1*(Tcu/x1)=k2*(Tss/x2);\n",
"Tss = Tcu[0]*(k1/x1)*(x2/k2); # formula for temperature gradient in steel side\n",
"Tcul=T1-Tss;\n",
"Tcur=T2+Tss;\n",
"q=k2*Tss/(1000*x2); # formula for heat conducted, kW\\m^2\n",
"\n",
"#Results\n",
"print \"Temperature on left copper side is :\",round(Tcul,3),\"C\\n\"\n",
"print \"Temperature on right copper side is :\",round(Tcur,3),\"C\\n\"\n",
"print \"Heat conducted through the wall is :\",round(q,3),\"kW\\m^2\\n\"\n",
"print \"Our initial approximation was accurate within a few percent.\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Temperature on left copper side is : 254.971 C\n",
"\n",
"Temperature on right copper side is : 245.029 C\n",
"\n",
"Heat conducted through the wall is : 1232.749 kW\\m^2\n",
"\n",
"Our initial approximation was accurate within a few percent.\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 1.3, Page number: 22"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"import math\n",
"\n",
"#Variables\n",
"q1=6000; #Heat flux, W*m**-2\n",
"T1=120; #Heater Temperature, C\n",
"T2=70; #final Temperature of Heater, C\n",
"q2=2000; #final heat flux, W*m**-2\n",
"\n",
"#Calculations\n",
"h=q1/(T1-T2) #formula for average heat transfer cofficient\n",
"Tnew=T2+q2/h; #formula for new Heater temperature, C\n",
"\n",
"#Results\n",
"print \"Average Heat transfer coefficient is:\",h,\"W/(m^2*K)\\n\"\n",
"print \"New Heater Temperature is:\",round(Tnew,3),\"C\\n\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Average Heat transfer coefficient is: 120.0 W/(m^2*K)\n",
"\n",
"New Heater Temperature is: 86.667 C\n",
"\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 1.4, Page number: 25"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"import math\n",
"from numpy import array\n",
"from numpy import linspace\n",
"import matplotlib.pyplot as plt\n",
"from pylab import *\n",
"%matplotlib inline\n",
"\n",
"#Variables\n",
"h=250; #Heat Transfer Coefficient, W/(m**2*K)\n",
"k=45; #Thermal Conductivity, W/(m*K)\n",
"c=180; #Heat Capacity, J/(kg*K)\n",
"a=9300; #density, kg/m**3\n",
"T1=200; #temperature, C\n",
"D=0.001; #diameter of bead, m\n",
"t1=linspace(0,5,50); #defining time interval of 0.1 seconds\n",
"T=linspace(0,5,50);\n",
"i=0;\n",
"\n",
"#Calculations\n",
"while i<50:\n",
" T[i]=T1-c*math.exp(-t1[i]/((a*c*D)/(6*h))); #Calculating temperature at each time in degree C\n",
" i=i+1;\n",
"\n",
"plt.plot(t1,T);\n",
"plt.xlabel(\"Time(in sec)\");\n",
"plt.ylabel(\"Temperature(in degree C)\");\n",
"plt.title(\"Thermocouple response to a hot gas flow\");\n",
"plt.show();\n",
"\n",
"Bi = h*(D/2)/k; #biot no.\n",
"\n",
"#Results\n",
"print \"The value of Biot no for this thermocouple is\",round(Bi,5);\n",
"print \"Bi is <0.1 and hence the thermocouple could be considered as a lumped heat capacity system and the assumption taken is valid.\\n\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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Ro0aNuuPO6qZRo0YZY+nUqRMAX331FfHx8Tg5OTFixAjmzp1Lv379Ct1+xYoVvP3229Sv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\n9aVlLh5rAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7fbd3da5b8d0>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The value of Biot no for this thermocouple is 0.00278\n",
"Bi is <0.1 and hence the thermocouple could be considered as a lumped heat capacity system and the assumption taken is valid.\n",
"\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 1.5, Page number: 32"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"import math\n",
"from sympy import solve,symbols\n",
"\n",
"#Variables\n",
"x=symbols('x');\n",
"T1=293; #Temperature of air around thermocouple, K\n",
"T2=373; #Wall temperature, K\n",
"h=75; #Average Heat Transfer Coefficient, W/(m**2*K)\n",
"s=5.67*10**-8; #stefan Boltzman constant, W/(m**2*K**4)\n",
"\n",
"#Calculations\n",
"x=solve((h*(x-T1)+s*(x**4-T2**4)),x);\t #Calculating Thermocouple Temperature, K\n",
"y=x[1]-273;\t\t\t\t #Thermocouple Temperature, C\n",
"\n",
"#Results\n",
"print \"Thermocouple Temperature is :\",round(y,3),\"C\\n\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Thermocouple Temperature is : 28.395 C\n",
"\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Example 1.6, Page number: 34"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"import math\n",
"from sympy import solve,symbols\n",
"\n",
"#Variables\n",
"x=symbols('x');\n",
"e=0.4; #emissivity\n",
"T1=293; #Temperature of air around Thermocouple, K\n",
"T2=273; #wall Temperature, K\n",
"h=75; #Average Heat Transfer Coefficient, W/(m**2*K)\n",
"s=5.6704*10**-8; #stefan Boltzman constant, W/(m**2*K**4)\n",
"\n",
"#Calculations\n",
"z=solve(((s*e*((373)**4 - (x)**4)) - h*(x-293)),x);\t#Calculating Thermocouple Temperature, K\n",
"y=z[0]-273;\t\t\t\t\t #Thermocouple Temperature, C\n",
"\n",
"'''NOTE: Equation written is absolutely correct and solving this equation\n",
" should give real result as: 296.112 i.e. 23.112 C, but somehow python is giving wrong result.'''\n",
"\n",
"#Results\n",
"print \"Thermocouple Temperature is :\",round(y,1),\"C \\n\"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Thermocouple Temperature is : 25.9 C \n",
"\n"
]
}
],
"prompt_number": 3
}
],
"metadata": {}
}
]
}
|
