Removed duplicates

1 files changed, 302 insertions, 0 deletions

diff --git a/Fluid_Mechanics_by_John_F._Douglas/Chapter_23.ipynb b/Fluid_Mechanics_by_John_F._Douglas/Chapter_23.ipynb
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@@ -0,0 +1,302 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:ef9e21b794b8d045cd677d625f05028772e4370ff57190a11a0021b5fe3769e2"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Chapter 23: Performance of Rotodynamic Machines"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 23.1, Page 814"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "import math\n",
+      "from pylab import *\n",
+      "%matplotlib inline\n",
+      "\n",
+      " #Initializing  the  variables\n",
+      "Q  =  []\n",
+      "for c in range(9):\n",
+      "    Q.append(7*c)\n",
+      "H  =  [40, 40.6, 40.4, 39.3, 38, 33.6, 25.6, 14.5, 0];\n",
+      "n  =  [0, 41, 60, 74, 83, 83, 74, 51, 0];\n",
+      "N1  =  750;\n",
+      "N2  =  1450;\n",
+      "D1  =  0.5;\n",
+      "D2  =  0.35;\n",
+      "\n",
+      " #Calculations\n",
+      "Q2=[]\n",
+      "H2=[]\n",
+      "for c in range(9):\n",
+      "    Q2.append(Q[c]*(N2/N1)*(D2/D1)**3);\n",
+      "    H2.append(H[c]*(N2/N1)**2*(D2/D1)**2);\n",
+      "   \n",
+      "plot(Q,H,label='H1')\n",
+      "plot(Q,n,label='n1')\n",
+      "plot(Q2,H2,'--',label='H2')\n",
+      "plot(Q2,n,'--',label='n2')\n",
+      "legend( loc='upper right', numpoints = 1 )\n",
+      "xlabel(\"Q  (m3/s)\");\n",
+      "ylabel(\"H (m of water) and n(percent)\")\n",
+      "grid(True)\n",
+      "show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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PtMI0uuXIHQ6Hg23btsHZ2RnGxsaYPn06amtrMW7cOEyZMgX6+vrQ09PD4sWL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\nIK1PDnOiPzgAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x322b110>"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 23.2, Page 831"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "import math\n",
+      "\n",
+      "\n",
+      " #Initializing  the  variables\n",
+      "n  =  0.9;\n",
+      "g  =  9.81;\n",
+      "D  =  1.45;\n",
+      "N  =  375/60;\n",
+      "H  =  200;                                                                #  Real  height\n",
+      "x  =  165;                                                                #  Theta\n",
+      "P  =  3750*10**3;\n",
+      "rho  =  1000;\n",
+      "\n",
+      " #Calculations\n",
+      "h  =  n*H;                                                                #Effective  Head\n",
+      "v1  =  (2*g*h)**0.5;\n",
+      "u  =  math.pi*D*N;\n",
+      "\n",
+      "n_a  =  (2*u/v1**2)*(v1-u)*(1-n*math.cos(math.radians(x)));\n",
+      "\n",
+      "P_b  =  P/n_a;\n",
+      "ppj  =  P_b/2;                                                        #  Power  per  jet\n",
+      "d  =  (8*ppj/(rho*math.pi*v1**3))**0.5  ;\n",
+      "print \"the efficiency of runner :\",round(n_a,3)\n",
+      "print \"Diameter of Jet (m)      :\",round(d,3)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "the efficiency of runner : 0.933\n",
+        "Diameter of Jet (m)      : 0.156\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 23.3, Page 834"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "import math\n",
+      "                                     #Example  23.3  \n",
+      "\n",
+      " #Initializing  the  variables\n",
+      "g  =  9.81;\n",
+      "H  =  12;\n",
+      "n  =  0.8;\n",
+      "w  =  300*2*math.pi/60;\n",
+      "Q  =  0.28;\n",
+      "\n",
+      " #Calculations\n",
+      "V_f1  =  0.15*(2*g*H)**0.5;\n",
+      "V_f2  =V_f1;\n",
+      "V_w1  =  (n*g*H)**0.5;\n",
+      "u1  =  V_w1;\n",
+      "theta  =  math.atan(V_f1/u1);\n",
+      "u2  =0.5*u1;\n",
+      "B2  =  math.atan(V_f2/u2);\n",
+      "r1  =  u1/w;\n",
+      "b1  =  Q/(V_f2*0.9*2*math.pi*r1);                          #  vanes  occupy  10  per  cent  of  the  circumference  hence  0.9\n",
+      "b2  =  2*b1;\n",
+      "\n",
+      "print \"Guide vane angle    (degree)  :\",round(theta*180/math.pi,2)\n",
+      "print \"Vane angle at exit  (degree)  :\",round(B2*180/math.pi,2)\n",
+      "print \"Width of runner at inlet (mm) :\",round(b1*1000,1) \n",
+      "print \"Width of runner at exit  (mm) :\",round(b2*1000,1)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Guide vane angle    (degree)  : 13.34\n",
+        "Vane angle at exit  (degree)  : 25.38\n",
+        "Width of runner at inlet (mm) : 69.6\n",
+        "Width of runner at exit  (mm) : 139.3\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 23.4, Page 838"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "import math\n",
+      "import sympy\n",
+      "from sympy import solve,symbols\n",
+      "\n",
+      " #Initializing  the  variables\n",
+      "H  =  35;\n",
+      "g  =  9.81;\n",
+      "D  =  2;\n",
+      "N  =  145/60;\n",
+      "z  =  30*math.pi/180;                          #  angle  between  vanes  and  direction  of  runner  rotation\n",
+      "y  =  28*math.pi/180;                          #  angle  between  runner  blades  at  the  outlet.\n",
+      "\n",
+      " #Calculations\n",
+      "H_net  =  0.93*H  ;                            #  since  7%  head  is  lost\n",
+      "v1  =  (2*g*H_net)**0.5;\n",
+      "u  =  math.pi*N*D;\n",
+      "\n",
+      " # from inlet velocity triangle\n",
+      "V_r1=14.3\n",
+      "## ash = cos(beta1+z)\n",
+      "ash = (V_r1**2+v1**2-u**2)/(2*V_r1*v1)\n",
+      "beta1=(z+math.acos(ash))*180/math.pi  # in degrees\n",
+      "\n",
+      "V_r2=(1-8/100)*V_r1         #8 % loss due to friction\n",
+      "V_w1= u + V_r1*math.cos(math.radians(beta1))\n",
+      "V_w2= u - V_r2*math.cos(y)\n",
+      "\n",
+      "E  =  (u/g)*(V_w1  -  V_w2);\n",
+      "n  =  E/H;\n",
+      "print \"Blade angle at inlet :\",round(beta1,1)\n",
+      "print \"Efficiency    (%)    :\",round(n*100)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Blade angle at inlet : 62.1\n",
+        "Efficiency    (%)    : 81.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Example 23.5, Page 844"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import division\n",
+      "import math\n",
+      "\n",
+      "\n",
+      " #Initializing  the  variables\n",
+      "s  =  0.03;\n",
+      "P  =  185*10**3;\n",
+      "rho  =  0.86*10**3;\n",
+      "A  =  2.8*10**-2;\n",
+      "N  =  2250/60;\n",
+      "D  =  0.46;\n",
+      "\n",
+      " #Calculations\n",
+      "R0  =  0.46/2;\n",
+      "Ws_Wp  =  1-s;\n",
+      "n  =  Ws_Wp;\n",
+      "Pf  =  s*P;\n",
+      "Q  =  (2*Pf*A**2/(3.5*rho))**(1/3);\n",
+      "Wp  =  2*math.pi*N;  \n",
+      "Ri  =  ((1/Ws_Wp)*(R0**2  -P/(rho*Q*Wp**2)))**0.5;#  Modified  equation  for  power  transmission.\n",
+      "Di  =  2*Ri;\n",
+      "T  =  P/(rho*Wp**3  *D**5);\n",
+      "   \n",
+      "print \"Mean diameter (mm) :\",round(Di*1000)\n",
+      "print \"Torque Coefficient :\",round(T,4)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Mean diameter (mm) : 326.0\n",
+        "Torque Coefficient : 0.0008\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file