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path: root/Material_Science_In_Engineering_by_Dr._K._M._Gupta/ch7.ipynb
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{
 "metadata": {
  "name": ""
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 7 : Mechanical Properties"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.3 pageno : 166"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "from matplotlib.pyplot import *\n",
      "\n",
      "# Variables\n",
      "a1 = 222.*10**9;\t\t\t#in N\n",
      "a2 = 168.*10**9;\t\t\t#in N\n",
      "e1 = 1.90;  \t    \t\t#in sqm\n",
      "e2 = 1.42;  \t\t    \t#in sqm\n",
      "da = a1-a2; \t\t\t    #in N\n",
      "de = e1-e2;\t    \t    \t#in sqm\n",
      "MPa = [14,28,56,84,110,138,193,221,276]\n",
      "strain = [.1,.21,.44,.67,.88,1.14,1.7,1.95,2.9]\n",
      "\n",
      "# Calculations\n",
      "e_math_tan = da/de;\n",
      "e_math_tann = e_math_tan*10**-9;\t\t\t#in Gpa\n",
      "a3 = 180.*10**9;                   \t\t\t#in N\n",
      "e3 = 1.46;\t\t\t                        #in sqm\n",
      "e_sec = 10**-9*a3/e3;\t\t\t            #in Gpa\n",
      "a = 85*10**6;\n",
      "e = .68*10**-3;\n",
      "e_y = 10**-9*a/e;\t\t\t                #in Gpa\n",
      "plot(strain,MPa)\n",
      "plot(strain,MPa,\"go\")\n",
      "xlabel(\"STRAIN\")\n",
      "ylabel(\"STRESS(MPa)\")\n",
      "suptitle(\"Stress-strain diagram\")\n",
      "\n",
      "# Results\n",
      "print \"Tangent Modulous of elasticity (in Gpa)  =  %.1f GPa\"%e_math_tann\n",
      "print \"Secant modulous of elasticity (in Gpa)  =  %d GPa\"%e_sec\n",
      "print \"Youngs modulous (in Gpa)  =  %d GPa\"%e_y\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        "Tangent Modulous of elasticity (in Gpa)  =  112.5 GPa\n",
        "Secant modulous of elasticity (in Gpa)  =  123 GPa\n",
        "Youngs modulous (in Gpa)  =  125 GPa\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nzhwGDRpUNWB1gZULGAasWmWuNT16tDmtRrNmzo5KxLXU+TiJ6dOnExsbS/fu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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f50104c24d0>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.4 page no : 179"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "from matplotlib.pyplot import *\n",
      "\n",
      "# Variables\n",
      "t = [0,1,2,4,8,16,24,32,40,48,60,72]       #time\n",
      "s = [0,.02,.029,.041,.057,.078,.094,.109,.122,.136,.156,.176]     # strain E (mm/mm)\n",
      "\n",
      "# calculations\n",
      "min_creep_rate = 12./14            # from curve\n",
      "creep_intercept = .055              # from curve\n",
      "\n",
      "#results\n",
      "plot(t,s)\n",
      "plot(t,s,\"go\")\n",
      "suptitle(\"Strain-Time Curve\")\n",
      "xlabel(\"Time(minute)\")\n",
      "ylabel(\"Strain\")\n",
      "\n",
      "print \"Minimum Creep rate : %.3f mm/mm\"%min_creep_rate\n",
      "print \"The creep intercept : %.3f mm/mm\"%creep_intercept\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        "Minimum Creep rate : 0.857 mm/mm\n",
        "The creep intercept : 0.055 mm/mm\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['draw_if_interactive', 'e']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IqKysNEJDQ438/PxabdLS0oxRo0YZhmEY2dnZRmRkpGEYhlFUVGT06dPHqKio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\nItIgJQsREWmQkoWIiDRIyUJERBr0/wHNQhTKgu5WJgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f50125b9350>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.5 pageno : 183"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "#Find Stress\n",
      "\n",
      "# Variables\n",
      "n = 3.;\n",
      "a = 300.;\n",
      "t = 365. * 24;\t\t\t#in hours\n",
      "e = 2.*10**6;\t\t\t#kgf/sqcm\n",
      "ai = 750.;\t    \t\t#in kgf/sqcm\n",
      "\n",
      "# Calculations\n",
      "v_cr = 2.8*10**-8;\t\t\t# in cm/cm/hour creep rate\n",
      "x = math.log(v_cr)-n*math.log(a);\n",
      "a1 = math.exp(x);\n",
      "a_tf = round(math.sqrt(1./((1./ai**(n-1))+(a1*e*(n-1)*t))),-2);\n",
      "\n",
      "# Results\n",
      "print \"Stress Remaining (in kgf/sq cm)  =  %.f kgf/cm**2\"%a_tf\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Stress Remaining (in kgf/sq cm)  =  200 kgf/cm**2\n"
       ]
      }
     ],
     "prompt_number": 19
    }
   ],
   "metadata": {}
  }
 ]
}