{ "metadata": { "name": "", "signature": "sha256:bc77360c046c25bc1074d9e1441ee10dd742c14709d1f92baa61c939b8a28b76" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Chapter 14 : Further Developments" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Example 14.1 Page No : 532" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import math \n", "from matplotlib.pyplot import *\n", "\t\n", "#initialisation of variables\n", "a= 60.5\n", "Q= 0.2 \t#ft**3/sec flow rate\n", "d= 3. \t#in diameter\n", "u= 0.0325\n", "g= 32.2 \t#ft/sec**2\n", "T= [50.0, 60.0, 70.0, 80.0, 90.0, 100.0]\n", "Ep= [294.5, 188.6, 113.2, 60.4, 37.7, 24.5]\n", "Eh= [0 ,69.9, 139.8, 209.7, 279.5, 349.4]\n", "Et= [295, 258, 253, 270, 317, 374]\n", "\t\n", "#CALCULATIONS\n", "re= a*4*Q/(math.pi*(d/12)*u*g)\n", "\t\n", "#RESULTS\n", "print 'Reynolds Number = %.1f '%(re)\n", "print (T)\n", "print (Ep)\n", "print (Eh)\n", "print (Et)\n", "plot(T,Ep)\n", "plot(T,Eh)\n", "plot(T,Et)\n", "\n", "xlabel(\"T (F)\")\n", "ylabel(\"Eh,Ep,Eh&Ep (kW)\")\n", "suptitle(\"Variations of Ep, Eh and (Ep+Eh) with T\")\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n", "Reynolds Number = 58.9 \n", "[50.0, 60.0, 70.0, 80.0, 90.0, 100.0]\n", "[294.5, 188.6, 113.2, 60.4, 37.7, 24.5]\n", "[0, 69.9, 139.8, 209.7, 279.5, 349.4]\n", "[295, 258, 253, 270, 317, 374]\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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72bdvH0op2rRpo0sczZo1w9xA00bz+mTv39e6ZO/ehfXroWJFPQT3qKtXH4xr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\nw4dr+1/UqlVoIQhhtGSMQhhUWnoaXx7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"text": [ "" ] } ], "prompt_number": 1 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Example 14.2 Page No : 535" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\t\n", "#initialisation of variables\n", "wcb= 2. \t#ton weighing\n", "wc= 100. \t #ton\n", "wa= 6.5 \t #% of the weight\n", "wca= 20. \n", "r= 0.8\n", "r1= 1.2\n", "\t\n", "#CALCULATIONS\n", "wca1= wc/wa\n", "wca2= wcb*(wca1/wca)**1.5\n", "Wca= wcb*r**(9./4)*(1./r1)**(9./4)*(wca1/wca)**1.5\n", "\t\n", "#RESULTS\n", "print ' Wc/Wa = %.2f '%(wca1)\n", "print ' Wc,a = %.2f ton'%(wca2)\n", "print ' Wc,a = %.2f ton'%(Wca)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " Wc/Wa = 15.38 \n", " Wc,a = 1.35 ton\n", " Wc,a = 0.54 ton\n" ] } ], "prompt_number": 4 } ], "metadata": {} } ] }