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+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:029b6e47f117cbc4316b9317b5e197658ff26f93dc8874c76dd6b6c464f4fec0"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Chapter 4: Motion Fluid Particles and Streams"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Example 4.1, Page 103"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import math\n",
+ "from pylab import *\n",
+ "import numpy as np\n",
+ "%matplotlib inline\n",
+ "\n",
+ " #Initializing the variables\n",
+ "x = [0, 23, 28, 31, 32, 29, 22, 14, 0]\n",
+ "y = np.linspace(0,80,9)\n",
+ "xlabel('Velocity (m/s)')\n",
+ "ylabel('Distance from one side(mm)')\n",
+ "title('Velocity Distribution Curve')\n",
+ "grid(1)\n",
+ "\n",
+ " #Calculations\n",
+ "plot(x,y,'-*')\n",
+ "show()\n",
+ "mu=[17.5 , 26.0, 29.6, 31.9, 30.7, 25.4, 18.1, 7.7]\n",
+ " # mean velocity\n",
+ "print \"Mean velocity (m/s):\",round(mean(mu),2)\n",
+ "\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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knMCmTZumjBw5cvfUqVM3AsCDBw+s6IweQhqPNm2AdevYU0oHDWJPNW3bFli7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\nYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x31fdd50>"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Mean velocity (m/s): 23.36\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Example 4.2, Page 106"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import math\n",
+ "from pylab import *\n",
+ "import numpy as np\n",
+ "\n",
+ " #Initializing the variables all unknowns are assigned 0\n",
+ "\n",
+ "d = [0.0, 0.05, 0.075, 0, 0.030];\n",
+ "Q = [0, 0, 0, 0, 0];\n",
+ "V = [0, 0, 2, 1.5, 0];\n",
+ "A = [0, 0, 0, 0, 0]\n",
+ " #Calculations\n",
+ "A2 = math.pi*d[2]**2/4;\n",
+ "Q[2] =A2*V[2];\n",
+ "Q = [0,Q[2], Q[2], Q[2]/1.5 , 0.5*Q[2]/1.5];\n",
+ "d[3] = (Q[3]*4/(V[3]*math.pi))**0.5;\n",
+ "for i in range(0,5):\n",
+ " A[i] = math.pi*d[i]**2/4;\n",
+ "V[1] = V[2]*(A[2]/A[1]);\n",
+ "V[4]=Q[4]/A[4];\n",
+ "\n",
+ "\n",
+ "header = \"Diameter(mm) Area(m2)\\t Flow Rate(m3/s) Velocity(m/s)\"\n",
+ "print header\n",
+ "for c in range(1,5):\n",
+ " mm=str(round(d[c]*1000,1))+'\\t '+str(round(A[c],6))+' \\t '+str(round(Q[c],6))+' \\t'+str(round(V[c],2))\n",
+ " print mm"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Diameter(mm) Area(m2)\t Flow Rate(m3/s) Velocity(m/s)\n",
+ "50.0\t 0.001963 \t 0.008836 \t4.5\n",
+ "75.0\t 0.004418 \t 0.008836 \t2.0\n",
+ "70.7\t 0.003927 \t 0.00589 \t1.5\n",
+ "30.0\t 0.000707 \t 0.002945 \t4.17\n"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "metadata": {},
+ "source": [
+ "Example 4.3, Page 108"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "from __future__ import division\n",
+ "import math\n",
+ "import sympy\n",
+ "from sympy import diff, Symbol\n",
+ "\n",
+ "\n",
+ "#Initializing the variables \n",
+ "'''\n",
+ "def df(x, h=0.1e-5):\n",
+ " return ( f(x+h/2) - f(x-h/2) )/h\n",
+ " return df\n",
+ "\n",
+ "print df(2*x,h)\n",
+ "'''\n",
+ "x = Symbol('x') \n",
+ "vx = 3-x\n",
+ "vy = 4+2*x\n",
+ "vz = 2-x \n",
+ "\n",
+ " #Calculations\n",
+ "delVx = vx.diff(x); \n",
+ "delVy = vx.diff(x);\n",
+ "delVz = vx.diff(x); \n",
+ "\n",
+ "result = delVx+delVy+delVz;#requirement of continuity equation (result = 0)\n",
+ "print \"Satisfy requirement of continuity \"\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Satisfy requirement of continuity \n"
+ ]
+ }
+ ],
+ "prompt_number": 10
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+} \ No newline at end of file