summaryrefslogtreecommitdiff
path: root/Fluid_Mechanics/Chapter_23.ipynb
blob: fa71e92d125a41ad234df6c84a6c69b9472775d0 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
{
 "metadata": {
  "name": "",
  "signature": "sha256:109f899855f47f57b7b8d73d8df4dd5290683ba03d6e02690f896cfaa5c8d9e2"
 },
 "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": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['linalg', 'draw_if_interactive', 'random', 'power', 'info', 'fft']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEPCAYAAACk43iMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRHiWAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2dc9f90>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "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": {}
  }
 ]
}