summaryrefslogtreecommitdiff
path: root/Fundamentals_of_Fluid_Mechanics/Ch_7.ipynb
blob: d5266070a9b3fe2b7899ff03ca30ec5b9971f970 (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
{
 "metadata": {
  "name": "Ch 7"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 7 :Similitude ,Dimensional Analysis and Modelling"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.5 Page no.372"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#example 7.5\n",
      "#Determine the model dimension and the velocity  at which the test should be performed.\n",
      "#given\n",
      "D=0.1                            #m\n",
      "H=0.3                            #m\n",
      "v=50.0                           #km/hr\n",
      "Dm=20.0                          #mm\n",
      "T=20.0                           #degree C\n",
      "fm=49.9                          #Hz  frequency for the model\n",
      "#f=func(D,H,V,d,vis)\n",
      "#f=T**(-1) D=l H=L V=L*(T**(-1)) d=M*(L**(-3)) vis=M*(L**(-1))*(T**(-1))\n",
      "#by applying pi theorem,\n",
      "#(f*D/V)=funct((D/H),(d*V*D/vis))\n",
      "#hence Dm/Hm = D/H, dm*Vm*Dm/vism = d*V*D/vis, and (f*D/V)=(fm*Dm/Vm)\n",
      "\n",
      "#calculation\n",
      "Hm=(Dm*H*1000/(D*1000))   #mm\n",
      "V=v*1000/3600             #m/s\n",
      "vism=0.001                #kg/(m*s)\n",
      "vis=1.79*10**-5           #kg/(m*s)\n",
      "d=1.23                        #kg/(m**3)\n",
      "dm=998                        #kg/(m**3)\n",
      "Vm=(vism*d*D*V*1000)/(vis*dm*Dm)        #m/s\n",
      "f=(V/Vm)*(Dm/(D*1000))*fm               #Hz\n",
      "\n",
      "#result\n",
      "print \"The model dimension =\",round(Hm,3),\"mm\"\n",
      "print \"The velocity at which the test should be performed=\",round(Vm,2),\"m/s\"\n",
      "print \"The predicted prototype vortex shredding frequency =\",round(f,1),\"Hz\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The model dimension = 60.0 mm\n",
        "The velocity at which the test should be performed= 4.781 m/s\n",
        "The predicted prototype vortex shredding frequency = 29.0 Hz\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.6 Page no.377"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Example 7.6\n",
      "#What is the required flowrate in the model.\n",
      "#given\n",
      "D=2.0                              #ft\n",
      "Q=30.0                           #cfs\n",
      "Dm=3.0                          #in\n",
      "#Rem=Re hence (Vm*Dm/kvism)=(V*D/kvis) where kvis is kinematic viscosity\n",
      "#kvis=kvism same fluid is used for model and prototype\n",
      "#(Vm/V)=(D/Dm)\n",
      "#Q=VA hence Qm/Q = (Vm*Am)/(V*A)=(Dm/D)\n",
      "\n",
      "#  calculation\n",
      "Qm=(Dm/12)*Q/D          #cfs\n",
      "\n",
      "#result\n",
      "print \"The required flowrate in the model=\",Qm,\"cfs\"\n",
      "\n",
      "#Plot\n",
      "import matplotlib.pyplot as plt\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(111)\n",
      "ax.plot([0.125], [8], 'o')\n",
      "ax.annotate('(0.125,8)', xy=(0.125,8.5))\n",
      "\n",
      "T=[0.04,0.125,0.2,0.6,1]\n",
      "K=[25,8,5,2,1]\n",
      "xlabel(\"Dm/D\") \n",
      "ylabel(\"Vm/V\") \n",
      "plt.xlim((0,1))\n",
      "plt.ylim((0,25))\n",
      "a=plot(T,K)\n",
      "show(a)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The required flowrate in the model= 3.75 cfs\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAELCAYAAAA/cjqaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.7 Page no.380"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#example 7.7\n",
      "#Find (a)The required air pressure in the tunnel.\n",
      "#(b)The corrosponding drag on the prtotype for a 1 lb drag on the model.\n",
      "V=240.0                             #mph\n",
      "ratio=0.1\n",
      "Vair=240.0                          #mph\n",
      "Fm=1.0                               #lb Fm =drag force on model\n",
      "p=14.7                              #psia standard atmospheric pressure\n",
      "#Re=Rem\n",
      "#(d*V*l/vis)=(dm*Vm*lm/vism)\n",
      "#here Vm=V and lm/l=ratio\n",
      "#assumption made is that an increase in pressure does not significantly change viscosity\n",
      "\n",
      "#calculation\n",
      "drat=V/(ratio*Vair)             #where drat=dm/d\n",
      "\n",
      "#for an ideal gas p=d*R*T\n",
      "#T=Tm\n",
      "#hence, pm/p=dm/d pm/p=prat\n",
      "pm=p*drat\n",
      "#F/(0.5*d*(V**2)*(l**2))=Fm/(0.5*dm*(Vm**2)*(lm**2))\n",
      "F=(1/drat)*((V/Vair)**2)*((1/ratio)**2)*Fm\n",
      "\n",
      "#result\n",
      "print \"The required air pressure in the tunnel=\",round(pm,3),\"psia\"\n",
      "print \"The corrosponding drag on the prtotype \\nfor a 1 lb drag on the model=\",round(F,3),\"lb\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The required air pressure in the tunnel= 147.0 psia\n",
        "The corrosponding drag on the prtotype \n",
        "for a 1 lb drag on the model= 10.0 lb\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.8 Page no.384"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Example 7.8\n",
      "#Determine (a)the flowrate and required model width. (b) the operating time.\n",
      "#given\n",
      "w=20.0                           #m\n",
      "Q=125.0                        #(m**3)/s\n",
      "ratio=0.066\n",
      "t=24.0                           #hours\n",
      "wm=ratio*w              #m\n",
      "#Vm/(gm*lm)**0.5 = V/(g*l)**0.5\n",
      "#gm=g\n",
      "#Q=VA and lm/l=1/15\n",
      "#hence Qm/Q = ((lm/l)**0.5)*((lm/l)**2) = ratio**2.5\n",
      "Qm=(ratio**2.5)*Q\n",
      "#V=l/t\n",
      "#tm/t=(V/Vm)*(lm/l)=ratio**0.5\n",
      "tm=(ratio**0.5)*t         #hours\n",
      "\n",
      "#result\n",
      "print \"The required model width=\",round(wm,3),\"m\" \n",
      "print \"The required model flowrate=\",round(Qm,3),\"m**3/s\"\n",
      "print \"The operating time for the model=\",round(tm,1),\"hrs\"\n",
      "\n",
      "#plot\n",
      "import matplotlib.pyplot as plt\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(111)\n",
      "\n",
      "T=[0.02,0.07,0.2,0.4,0.5]\n",
      "K=[4,6.20,10,15,17]\n",
      "xlabel(\"lm/l\") \n",
      "ylabel(\"tm  (hr)\") \n",
      "plt.xlim((0,0.5))\n",
      "plt.ylim((0,20))\n",
      "\n",
      "ax.plot([0.07], [6.20], 'o')\n",
      "ax.annotate('(1/15,6.20hr)', xy=(0.1,6))\n",
      "a=plot(T,K)\n",
      "show(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The required model width= 1.32 m\n",
        "The required model flowrate= 0.14 m**3/s\n",
        "The operating time for the model= 6.2 hrs\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAELCAYAAADURYGZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nu0KszCbRxKoAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 3
    }
   ],
   "metadata": {}
  }
 ]
}