summaryrefslogtreecommitdiff
path: root/Transport_Phenomena/ch6.ipynb
blob: 1d8101ccb3e3006c2b7bd80f12c4ebe1862d5792 (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
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
{
 "metadata": {
  "name": "",
  "signature": "sha256:d94dd77653a1dc6cef649df91fc864517d196845530100e7dee278dbacfebc8a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 6 : Turbulent Flow"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 6.1 - Page No :200\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math \n",
      "\n",
      "\n",
      "# Variables\n",
      "# given\n",
      "q = 50.;        \t\t\t    #[gal/min] - volumetric flow rate\n",
      "d = 2.067/12;  \t    \t\t    #[ft] - diameter\n",
      "A = 0.02330;  \t\t             #[ft**2] - flow area\n",
      "p = 0.99568*62.43;  \t\t\t #[lb/ft**3] - density of water at 86degF\n",
      "mu = 0.8007*6.72*10**-4;  \t\t #[lb/ft*sec] - viscosity of water at 86degF\n",
      "u = q/(60.*7.48*A);\n",
      "\n",
      "# Calculations\n",
      "# using the formula Nre = d*u*p/mu;\n",
      "Nre = round((d*u*p)/mu,-2);\n",
      "\n",
      "# Results\n",
      "print \"Nre = \",Nre\n",
      "print \"Hence the flow is turbulent. Note also that Nre is dimensionless\";\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Nre =  95100.0\n",
        "Hence the flow is turbulent. Note also that Nre is dimensionless\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 6.2 - Page No :202\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# Variables\n",
      "# given\n",
      "p = 0.99568*62.43;  \t\t\t #[lb/ft**3] - density of water at 86degF\n",
      "mu = 0.8007*6.72*10**-4;  \t\t #[lb/ft*sec] - viscosity of water at 86degF\n",
      "u = 4.78;  \t\t\t             #[ft/sec] - free stream velocity \n",
      "Nre = 5.*10**5;  \t\t\t     # the lower limit for the transition reynolds number range is substituted\n",
      "\n",
      "# Calculations\n",
      "x = (Nre*mu)/(p*u);\n",
      "\n",
      "# Results\n",
      "print \"x = %.1f\"%x\n",
      "print \"Thus the transition could star at about %.2f ft. \\\n",
      "\\nThe reynolds number at the  upper end of the transition range is %.0e .\\\n",
      "\\nThe value of x at this location is ten times then the value obtained above i.e %.1f ft\"%(x,Nre*10,x*10)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "x = 0.9\n",
        "Thus the transition could star at about 0.91 ft. \n",
        "The reynolds number at the  upper end of the transition range is 5e+06 .\n",
        "The value of x at this location is ten times then the value obtained above i.e 9.1 ft\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 6.3 - Page No :212\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# Variables\n",
      "# given\n",
      "t = [0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, 0.11, 0.12];\n",
      "Ux = [3.84, 3.50, 3.80, 3.60, 4.20, 4.00, 3.00, 3.20, 3.40,  3.00, 3.50, 4.30, 3.80];\n",
      "Uy = [0.43, 0.21, 0.18, 0.30, 0.36, 0.28, 0.35, 0.27, 0.21, 0.22, 0.23, 0.36, 0.35];\n",
      "Uz = [0.19, 0.16, 0.17, 0.13, 0.09, 0.10, 0.16, 0.15, 0.13, 0.18, 0.17, 0.18, 0.17];\n",
      "# using the formula AREA = [deltat/2]*[U1+U13+2*[U2+U3+U4+U5+U6+U7+U8+U9+U10+U11+U12]]\n",
      "# for Uxmean\n",
      "deltat = 0.01;\n",
      "T = t[12]-t[0];\n",
      "\n",
      "# Calculation and Results\n",
      "AREA = (deltat/2)*(Ux[0]+Ux[12]+2*(Ux[1]+Ux[2]+Ux[3]+Ux[4]+Ux[5]+Ux[6]+Ux[7]+Ux[8]+Ux[9]+Ux[10]+Ux[11]));\n",
      "Uxmean = AREA/T;\n",
      "print \"Uxmean = %.2f m s**-1\"%Uxmean\n",
      "\n",
      "# for Uymean\n",
      "deltat = 0.01;\n",
      "AREA = (deltat/2)*(Uy[0]+Uy[12]+2*(Uy[1]+Uy[2]+Uy[3]+Uy[4]+Uy[5]+Uy[6]+Uy[7]+Uy[8]+Uy[9]+Uy[10]+Uy[11]));\n",
      "Uymean = AREA/T;\n",
      "print \"Uymean = %.2f m s**-1\"%Uymean\n",
      "\n",
      "# for Uzmean\n",
      "AREA = (deltat/2)*(Uz[0]+Uz[12]+2*(Uz[1]+Uz[2]+Uz[3]+Uz[4]+Uz[5]+Uz[6]+Uz[7]+Uz[8]+Uz[9]+Uz[10]+Uz[11]));\n",
      "Uzmean = AREA/T;\n",
      "print \"Uzmean = %.2f m s**-1\"%Uzmean\n",
      "U = (Uxmean**2+Uymean**2+Uzmean**2)**(1./2);\n",
      "print \"U = %.3f m s**-1\"%U\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Uxmean = 3.61 m s**-1\n",
        "Uymean = 0.28 m s**-1\n",
        "Uzmean = 0.15 m s**-1\n",
        "U = 3.624 m s**-1\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 6.4 Page no : 218"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "import math\n",
      "\n",
      "# Variables\n",
      "#Following values are taken from example 6.3\n",
      "U_dash_z2 = 8.917*10**-4   #m**2s**-2  square velocity\n",
      "Uz2 = .02340               #m**2s**-2\n",
      "U_dash_x2 = 8.947 * 10**-4 #m**2s**-2\n",
      "Ux22 = .02409              #m**2s**-2\n",
      "U = 3.634\n",
      "U2x = 3.63                  #m**2s**-2\n",
      "U2x2 = .41                  #m**2s**-2\n",
      "Ix = 11.37                  #percent\n",
      "U2y = .0288                 #m**2s**-2\n",
      "U2y2 = .069                 #m**2s**-2\n",
      "Iy = 1.92                   #percent\n",
      "dt = .01108\n",
      "\n",
      "# Calculation\n",
      "rmsUz = math.sqrt(U_dash_z2)\n",
      "rmsUx = math.sqrt(Uz2)\n",
      "rmsUx2 = math.sqrt(U_dash_x2)\n",
      "rmsUx2 = math.sqrt(Ux22)\n",
      "I = 100*rmsUz/U\n",
      "Ux = 3.84 - 3.61\n",
      "Uy = .43 - .28\n",
      "UxUy = Ux*Uy\n",
      "ratio = dt/(U2x2*U2y2)\n",
      "\n",
      "\n",
      "# Results\n",
      "print \"I = %.2f percent\"%I\n",
      "print \"U'xU'y = %.4f m**2s**-2\"%UxUy\n",
      "print \"The ratio = %.2f \"%ratio"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I = 0.82 percent\n",
        "U'xU'y = 0.0345 m**2s**-2\n",
        "The ratio = 0.39 \n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 6.5 - Page No :232\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "%pylab inline\n",
      "\n",
      "from numpy import *\n",
      "from matplotlib.pyplot import *\n",
      "import math \n",
      "\n",
      "\n",
      "# Variables\n",
      "# given\n",
      "UzmaxbyU = 24.83;\n",
      "roUbyv = 2312.;\n",
      "Re = 100000.;\n",
      "\n",
      "# using the formula Et/v = 95.5*((r/ro)/slope)-1\n",
      "# from fig 6.6 at Re = 100000\n",
      "rbyro = [0, 0.040, 0.100, 0.200, 0.300, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.960, 1]\n",
      "slope = [0, 0.105, 0.112, 0.126, 0.144, 0.168, 0.201, 0.252, 0.336, 0.503, 1.007, 2.517, 94.59]\n",
      "\n",
      "# Calculations\n",
      "Etbyv = zeros(13)\n",
      "for i in range(1,13):\n",
      "    Etbyv[i] = 95.5*((rbyro[i])/slope[i])-1;\n",
      "\n",
      "# Results\n",
      "plot(rbyro,Etbyv);\n",
      "suptitle(\"Eddy viscosity ratio (E,/v) versus dimensionless radius.\")\n",
      "xlabel(\"r/ro\")\n",
      "ylabel(\"Er/v\")\n",
      "\n",
      "show()\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEhCAYAAABlUDcAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XlcVFUfBvBnwH17VRRMMBc0jWHHUBQUNUVcckkEtzeX\nctfMbFErMcutzLSslzIz10A0tVTUStwVRFJcsjQwQBFcEgWR7bx/nBhBB2WZmTszPN/Ph48wy52H\nyzi/e8859xyVEEKAiIjoIRZKByAiIuPEAkFERFqxQBARkVYsEEREpBULBBERacUCQUREWrFAFMPS\n0hJubm6ar8WLFz/ymMjISPTt21fr85s1a4abN2+WK8OVK1cQEBBQrm08SUxMDF599VUAwP79+3H0\n6FG9vM6pU6ewa9cuzc8//vgjFi1aVKpt/PTTTwgODgYABAcHw87Orsjf6Pbt21qf5+/vjytXrhS7\n3W7duuHOnTulymIMCr//yrI/daG871Fd/D/RpeDgYCxZsgQAMGfOHPzyyy8KJ1JWJaUDGKsaNWog\nNja2zM9XqVTlztC4cWNs2rSp3Nt5HA8PD3h4eAAA9u3bh9q1a8PLy6tM28rPz4eFhfZjjtjYWMTE\nxMDf3x8A0Ldv32KLa3GWLFmC77//HoDcv9OnT8f06dMf+5x79+7h5s2baNy4cbGPCQoKwtdff/3E\nbZXW4/aHrpVlf+pCed+juvh/8iR5eXmwtLQs0WML55k7d66+IpkMnkGU0rZt29CqVSu0a9cOP/zw\ng+b2tLQ0+Pj4wNXVFWPHjoUQAkIIzJkzB8uWLdM8bvbs2Vi+fHmRbc6cORNffPGF5ueCo5jLly/D\n0dERgDwCb9euHdzc3ODs7IxLly4BAEJCQuDg4AA3NzcMHz4cAHDp0iV06NABLi4u8Pb2RkJCAgBg\n48aNcHJygpubGzp16gTgwVHo5cuXERISgqVLl8Ld3R2HDh1CixYtkJubCwBIT09HixYtkJeXVyT7\nyJEjMX78eHTs2BFvvfUWoqOj4eXlBRcXF3h4eODcuXPIzs7Ge++9h9DQULi5uSEsLAyrV6/GlClT\nHpu3sMTERGRnZ8PGxkZzW0mu8YyMjISvry92796NwYMHF7m94AO1b9++msJTWEhICN58803Nz4Uz\nf/3113BxcYFarcbo0aM1+6lWrVqYMWMG2rZti2PHjuGNN96AWq2Gq6srXn/9dc0+27x5s2a7tWrV\nAgAkJSWhU6dOcHNzg5OTEw4ePPhIpuLef4WzjRw5EhMnToS3tzfs7e0RGRmJUaNGoU2bNhg6dKjm\nOdu3b4eHhwecnJzQr18/zVlUs2bNEBwcDE9PT7Ru3RpnzpwBAPzyyy9Fztju3r2LhIQEODk5AZDF\neMiQIVCr1XBycsLu3bs12QYOHIg+ffqgRYsWmDZtmta/lbZ9mpubixEjRsDJyQnOzs745JNPAABL\nly7V7NfAwMBHtrV69Wq88MIL8PPzQ48ePZCRkYEuXbrAw8MDbdq0KVLU3nvvPbRs2RK+vr64cOGC\npkgU/jsVPtM5ceIEunTponWfmOKZ6GMJ0srS0lK4urpqvsLCwkRmZqZo1KiRiI+PF0IIMWTIENG3\nb18hhBBjx44V8+fPF0IIsXv3bqFSqcSNGzdEQkKCcHd3F0IIkZeXJ+zt7cXNmzeLvFZsbKzo3Lmz\n5mcHBweRlJQk4uPjhaOjoxBCiIkTJ4rQ0FDNdu7duydiYmJEq1atxO3bt4UQQvNv9+7dxYYNG4QQ\nQnz33XeiZ8+emu2mpqYKIYS4e/euEEKIffv2iT59+gghhAgODhZLlizR5Bg1apTYunWrEEKIkJAQ\nMWPGjEf208iRI0X//v01P9+5c0fk5+cLIYTYu3evZturV68WU6ZM0Tyu8M/F5S1s48aNYvLkyZqf\n58yZI2xtbTV/n65duz7yHCGEmDJliti3b5/Izc0VTz/9tMjMzBRCCDF+/Hixfv16zeOaN2+u2ScF\n0tLSRMuWLTU/+/v7i8OHD4vffvtN9O7dW+Tm5gohhJgwYYL4+uuvhRBCqFQqsWXLFiGEENeuXRNq\ntVrz/ILtjxw5UoSHh2tur1WrlhBCiEWLFolFixY98vgCj3v/ffvtt5r989JLL4lhw4YJIYTYtm2b\nqF27tjh//rzIz88XHh4eIjo6WqSkpAgvLy/N/li4cKGYPXu2EEKIZs2aiS+//FIIIcQXX3whXnrp\nJSGEEL169RJRUVFCCCGysrJEbm5ukffohx9+KMaOHSuEEOLPP/8UjRo1Evfu3RPffvutaNGihcjI\nyBBZWVni6aef1vwOzZo1Ezdu3Ch2n0ZFRQl/f/9H9knjxo1Fdna21v1UsD/s7OxEenq6EEKI3Nxc\nkZGRIYSQf9dmzZqJ/Px8ceTIEeHk5CSys7NFRkaGaNmypeb/wMiRI8XmzZuL5BRCiOjoaOHr61vs\nPjEnPIMoRvXq1REbG6v5CggIQFxcHJ555hk0a9YMADBkyBDNUeyhQ4cwZMgQAECPHj1Qr149AEDT\npk1hZWWF3377DXv27IG7u7vmvgKurq5ITU3F1atXcerUKdSrVw+2trZFHuPt7Y0PPvgAixYtwp9/\n/olq1arhl19+QWBgIOrUqQMAmn+PHj2qOVoeMmQIDh8+DADo1KkThg8fjq+++gr37t3T+nuLQkfl\nL7/8Mr799lsA8ohs1KhRWp8zcOBAzfdpaWno06cPHB0dMX36dFy4cEGzXfHQEX/Bz8XlLezvv//G\nU089pfm5oImp4O9TXFvxkSNH4O3tDUtLS/Ts2RPbt29Hbm4udu7ciX79+mkeZ2Njg8TExCLPbdCg\nAVq0aIHjx4/jxo0b+P3339GhQwfs3bsXsbGxaNu2Ldzc3PDrr79qnmtpaYn+/fsDAOrXr4/KlStj\nzJgx2Lx5MypXrqw1YwEvLy+sXLkSc+fOxcmTJ1GzZs0i9z/u/VeYSqVC7969AQCOjo5o1KgR2rRp\nA5VKBbVajcTERBw8eBB//vknOnToADc3N6xZs6ZIP03BvnF3d9f8bp06dcLUqVOxfPlypKamPtJs\nc/jwYc3/gZYtW6JVq1Y4c+YMVCoVunXrhho1aqBq1apQq9VISkrSPE8IUew+bdWqFS5evIipU6di\n586dqFGjBgDA2dkZw4cPx9q1a4vdBz169EDt2rUBADk5OZg2bRocHR3RvXt3pKam4sqVKzh48CAG\nDhyIypUro0aNGnjhhRce+zd62JP2ialjgSiFh9uTC78xVSpVsU0eBR+0q1evxujRo7U+JiAgAOHh\n4QgLC0NQUNAj9w8ZMgTbtm1DzZo10bdvX+zbt6/Y1yyuXffLL7/EvHnzcPXqVXh4eDyxc7BDhw5I\nSEhAZGQk8vLy4ODgoPVxBf9pAdmE1rt3b5w5cwY//vgjcnJyit1+Qc6StkMXV2CK89dff6FJkyao\nVEl2tQUFBSEsLAz79u1D27Zti3wACyG05ih4zpYtW4oUwjFjxmiK0++//65pr65WrZpmO5UqVcLx\n48cxaNAg7Nq1Cz179gQg30f5+fkAZD9FdnY2AMDHxwcHDhyAnZ0dXn75ZaxZs6ZIlse9/x5WpUoV\nzXOqVq1aZBsFr+3v76/5Hc6ePYtVq1ZpHlfwHEtLS83j33rrLaxcuRL379+Ht7e3pvg/LlPBviic\nofA2C9O2T+vWrYvffvsNvr6+WLlyJcaMGQMA2LFjByZMmIBTp07hueeee6TpEyj6vlyzZg3S09MR\nFxeH2NhYWFtbIzc3FxYWFkUyF7dPC++3rKwsze0l2SemjAWiFNRqNf744w9cvnwZABAaGqq5z9vb\nW/Pz3r17cevWLc19AwYMQEREBE6cOAE/Pz+t2w4MDMTGjRsRHh6udVTI5cuX0bx5c0yePBn9+vVD\nbGwsunXrhrCwMM3onYJ/O3TogLCwMADA999/Dx8fHwBAQkICPD09MWfOHNjY2DzS1l+9enVkZmYW\nue2///0vhg0bVmxhe1hWVhYaNWoEAEU+4GrUqFFk24XPKIrLW1jTpk2RkpLyxNdPTk7G888/DwDY\ntWuXplMcADp37oyTJ0/i66+/1hzpFrh27Rrs7Owe2d6AAQOwdetWbNy4UVO4u3fvjrCwMM3fOD09\nvcgRcYGMjAzcuXMH/v7+WLJkCU6ePAkAsLOzQ0xMDAD5QVdQRJOSkmBtbY0xY8ZgzJgxOHHiRJHt\nPe79VxoqlQo+Pj7Yt28f/v77bwDy71bQr1WchIQEqNVqvPHGG/D09MTZs2eL3O/j46PJdOnSJfz5\n559wdHR8YiFXqVTF7tObN28iPz8fAwcOxPvvv4/o6GgIIZCcnAxfX18sWLAA6enpj4xge/g1s7Ky\nYG1tDZVKhQMHDuDy5ctQqVTw9vbG1q1bkZ2djczMTPz0009aM9rZ2Wn+HoX7fp60T0wdC0Qx7t27\nV6TzadasWahevTpCQkLw/PPPo127dmjYsKHmCGnevHnYuXMnXF1dER4ejqZNm2q2VblyZXTt2hWD\nBw8u9mjZwcEBd+/ehZ2dXZGO2ILHr1+/XtPBfPbsWbz00ktwc3PD66+/jvbt28PNzQ1Tp04FAKxY\nsQKff/45nJ2dERISghUrVgAAXnvtNbi4uMDJyQnt27eHu7s7VCqV5jX69u2LDRs2wNXVFYcOHQIA\nDB06FLdu3XrkA7Wwwr/TjBkzMGPGDDz33HPIzs7W3NelSxfExMTAxcUFYWFhRV63uLyFdezYUfMB\nW2Dp0qVF/kaXL1/G1atXNU05ERERmqN2QB4F9unTBxEREejTp4/m9pSUFFhZWT3SpAMAdevWhYOD\nA/7++2+0bdsWAODi4oKZM2dqBiX4+vpqmmcK74v09HT07NkTbm5u8PHxwdKlSwEA48ePx+7du+Hm\n5oYjR45oOql//vlnuLi4wN3dHWFhYZrhxwUe9/4rvD8fzqHtPWdjY4OvvvoKL7zwAlxdXeHp6Ylz\n58498rjCz//444/h7OwMFxcXVKpUSdOMVXD/tGnTcPv2bajVavTv3x/fffcdqlat+kg2bYrbp4mJ\nifDx8YGbmxtGjBiBhQsXIi8vD0FBQXB1dYW7uzsmTZqE+vXrP5K58GsOGzYMR44cgYuLC7777js8\n++yzAID27dujf//+cHBwQK9eveDp6ak135w5czBhwgS0b98eFhYWT9wnbm5uj/19TYVKPKm8U7kJ\nIeDh4YGwsDC0bNlS6TilsmXLFvzwww9Yu3at0lHQtWtXrF+/vkhfxMNWrFiBpk2bokePHujYsSOi\no6OfuN2vvvoKGRkZeO2113QZl8jksUDo2blz5zBgwAD07t1bM0TPVEydOhV79+7Ftm3b8Mwzzygd\nBzt37sTx48d1Pj69W7du2LZtm+ZInogkFggiItKKfRBERKQVCwQREWnFAkFERFqxQBARkVYsEERE\npBULBBERacUCQUREWumtQCQmJqJTp05wcnJC69atNSuyPbwSWOFVxhYsWAAHBwc4OTlhz549+opG\nREQloLcL5a5du4a0tDQ4Ojri7t27cHd3x6ZNm7B161bUrl37kdW7YmJiMH78eBw7dgwpKSmamREL\nZqUkIiLD0tsZhI2NjWY1tFq1asHZ2RnJyckAtE+pu2PHDgQFBcHS0hK2trZQq9WIiorSVzwiInoC\ng/RBJCQkIDo6WjON84oVK/Dss89i+PDhmjUJkpOTi0y3bGdnp3UKZSIiMgy9F4i7d+8iICAAy5Yt\nQ+3atTFp0iRcunQJ586dg729vWaKaiIiMi6V9LnxnJwcvPjiixg6dKhmGcYGDRpo7h83bpxm8W87\nO7siSz4mJSWhSZMmj2yzZcuWT1zYhIiIirK3t8fFixdL9Ry9nUEIITBmzBg4ODgUmWc/NTVV8/3m\nzZuhVqsBAL169UJoaChyc3ORlJSEM2fOaF2849KlS5rVyCr615w5cxTPYCxf3BfcF9wXj/8qy4G1\n3s4gDh8+jHXr1sHZ2VmzutL8+fOxYcMGnD59GtnZ2WjatCm++eYbAICHhwcGDBgAZ2dnWFhYICQk\n5ImLvBMRkf7orUB4e3trXZi88BrBD5s1axZmzZqlr0hERFQKvJLahPn6+iodwWhwXzzAffEA90X5\nmNyKciqVCiYWmYhIcWX57OQZBBERacUCQUREWrFAEBGRViwQRESkFQsEERFppdepNojMWX4+cPo0\nsHcvsGcPcOUK0KUL0L27/LdOHaUTEpUPh7kSlcKVKw8Kws8/yyLQvTvQowdgawvs2yfvO34ccHGR\nt3fvDjz3HFCJh2OkoLJ8drJAED1GRgZw4ID80N+7VxaIrl0ffPA3b679eZmZwMGDD4pJYqI8q+jR\nQ361aGHY34OIBYKonPLzgdjYBx/sUVGAu/uDgtC2LWBpWfrtXr0qzzgKtluz5oNtdu0K1K2r+9+F\nqDAWCKIySEx88MH9yy9Agwbyg7t7d8DXF6hdW7evJwRw5syDs5LDhwFHxwcFo107gPNUkq6xQBCV\nwJ07wP79Dz6g09KA559/8AGtZRkSvcrKkkWiIM9ffwGdOz/I06oVoFIZNhOZHxYIIi3y8oATJ+SH\n7969QEwM4On54APYzQ2wMKIB36mp8kymoGBUqvSgI7xbN6B+faUTkiligSD6V3z8g2ajX38FGjd+\nUBA6dZJ9AKZACOD8+Qe/y8GDQJs2DwqGlxdQpYrSKckUsEBQhXX79oMhpnv3AunpD/oRnn9eDkE1\nB/fvA0ePPigYFy7IgldQMNq0YXMUaccCQRXO1avA2LFAZKQ8mi74oHRyMq5mI325cUM2RxUUjPx8\n4OOPgcBApZORsWGBoApl3z5g2DBZIN56C6heXelEyhJCXqA3bBjQsyfwySdA1apKpyJjwQJBFUJ+\nPjB/PrBiBbBmjTxroAdu3wZGjwYuXwY2bSr+Yj6qWLhgEJm9tDSgVy/ZnBITw+KgzX/+A4SHAyNG\nyGsqtm1TOhGZKhYIMhmHD8urmt3cHoxMIu1UKuDVV4Ht2+W/r78O5OQonYpMDZuYyOgJASxZAnz0\nEbBqFdC7t9KJTMuNG8BLLwE3bwKhoYa/EJCMA5uYyOzcugX07y+bTKKiWBzKwspKnkn06ydnld21\nS+lEZCpYIMhoRUfLJqUWLeSMqk2bKp3IdFlYyJFeYWHAK68A77wD5OYqnYqMHZuYyOgIIUcovf8+\n8OWXwIsvKp3IvKSmyqGwubnAhg3AU08pnYgMgU1MZPLS04GgINnXcPQoi4M+WFsDERFyploPD9nh\nT6QNCwQZjVOn5HoL9eoBR44A9vZKJzJflpbAnDnyOpJhw4B58+T1JUSFsYmJFCcE8M03wMyZwKef\nyg8sMpwrV+RZW40awNq1QMOGSicifWATE5mcjAw5BPPTT+VMpSwOhte4sWxmcnOTgwIOH1Y6ERkL\nFghSzPnzcl0GCws5h1CbNkonqrgqVQIWLAD+9z9g4EA54R9P1IlNTKSIdeuA114DFi8GRo1SOg0V\ndvmynA3W2hr47jvZJ0Smj01MZPTu3ZOzr86bJ6epZnEwPk2byutO7O1lk1N0tNKJSCksEGQwFy8C\nHTrIoazR0YCzs9KJqDhVqgBLl8qmpt69gc8/Z5NTRcQCQQYRHi6LwyuvABs3AnXqKJ2ISuLFF+X1\nKKtWyWan9HSlE5EhsUCQXmVny9lE33xTzgE0cSKXxDQ19vbyuhQrK3lh3alTSiciQ2GBIL1JSAB8\nfGSnZ0yM/HAh01Stmpz2ZO5cucb3ypVscqoI9FYgEhMT0alTJzg5OaF169ZYvHgxAODmzZvo3r07\nnJ2d4efnh3/++UfznAULFsDBwQFOTk7Ys2ePvqKRAfz0k1ysJjAQ+OEHjoQxF0OHyutVPv1UXr+S\nkaF0ItInvQ1zvXbtGtLS0uDo6Ii7d+/C3d0dmzZtwsqVK2Fvb49p06bh008/RXx8PJYtW4aYmBiM\nHz8ex44dQ0pKCry9vXHhwgVUqVKlaGAOczVqOTlyptCNG4Hvv5f9DmR+MjKASZPkYINNmwAHB6UT\n0ZMY1TBXGxsbODo6AgBq1aoFZ2dnJCcnY+fOnRgxYgQAYPjw4dixYwcAYMeOHQgKCoKlpSVsbW2h\nVqsRFRWlr3ikB8nJQNeuwOnTwMmTLA7mrGZNYPVqYMYMoHNneV0LmR+D9EEkJCQgOjoa3t7eSEtL\ng5WVFQCgQYMGSE1NBQAkJyfDzs5O8xw7OzskJSUZIh7pwN69cqI9f39gxw6gQQOlE5EhjBolr2eZ\nN09e33LvntKJSJcq6fsF7t69i0GDBmHZsmWoo6OxjcHBwZrvfX194evrq5PtUunl5cl1G1aulM1K\n/FNUPM7Osqlp7FjAy0s2ObVqpXQqioyMRGRkZLm2odcCkZOTgxdffBHDhg1D//79AQANGzbE9evX\n0aBBA6SlpcHa2hqAPGNITEzUPDcpKQlNilk8t3CBIOXcugUEBMhpomNigEaNlE5ESqlTRx4gfPkl\n0LGjPGB44QWlU1VsDx88z507t9Tb0FsTkxACY8aMgYODA1577TXN7b169cK6fxss161bh169emlu\nDw0NRW5uLpKSknDmzBl4enrqKx6VU16eHNHSqpVsXmJxIJVKXueyYwfw8svAiRNKJ6Ly0tsopkOH\nDqFTp05wdnaG6t8roxYsWABPT08EBgbi2rVraNSoEcLCwlC3bl0AwPz587Fu3TpYWFhgyZIl8PPz\nezQwRzEZhXfekdNC790rZwIlKmzLFjkZY3S0nPSPlFeWz07O5kqltnUrMHWqPELkf34qzjvvAIcO\nyYOIypWVTkMsEKR3v/8OdOokL4RjCyA9Tl4e0Lcv0Lq1nPiPlGVU10GQ+UlPBwYMkAvLsDjQk1ha\nAuvXAz/+yOskTBXPIKhEhJAzezZsCISEKJ2GTMmZM0CXLsCePXJZU1IGzyBIbxYuBK5eBZYvVzoJ\nmRpHR+CLL+RSptevK52GSoNnEPREe/YAI0fKESm2tkqnIVP19ttyYENEBEe+KYGd1KRz8fFA+/by\n6thOnZROQ6YsLw/o1Uteef3RR0qnqXjYxEQ6lZkpO6Vnz2ZxoPKztJRXW2/eLGf6JePHMwjSSghg\nxAh5deyaNVwFjnTn1Cm56NDPPwMuLkqnqTh4BkE689lnwNmzcsQSiwPpkouLHOwwcCBw86bSaehx\neAZBjzhwABg8WC5W37y50mnIXM2YAcTFATt3yuYn0i+eQVC5JScDQUGyWYnFgfRp4UIgN1dOyUHG\niQWCNO7flxfDTZkC9OihdBoyd5UqAaGhsuN60yal05A2bGIijXHjgLQ0OcqE/Q5kKCdPAn5+wL59\n8qI60g82MVGZrVwp+x5Wr2ZxIMNydwc++UQOqb51S+k0VBjPIAhRUUCfPsDBg3LmTSIlTJsG/PGH\nnNyPnda6xzMIKrXUVGDQIOCrr1gcSFkffSQvzuSKwsaDBaICy80FAgOB//4X+HfJcCLFVK4MhIXJ\nEXQ//KB0GgLYxFShTZ8OnD8vF//hKT0Zi+hooHdvYP9+4NlnlU5jPtjERCW2cSOwbZtc0IXFgYzJ\nc88BixbJs9rbt5VOU7HxDKICOn0a6NaNc+GQcZs0CUhMlGugW/BQttx4BkFPdPOmHE64fDmLAxm3\npUvl+3XePKWTVFw8g6hA8vLkcNZnn5XjzomMXUoK0LYt8L//yfculR0XDKLHevddea3D3r1yxAiR\nKTh2DHjhBeDQIeCZZ5ROY7rYxETF2rYN+O47OYyQxYFMSfv2wIcfyk7rO3eUTlOx8AyiArhwAfDx\nkcNZPT2VTkNUNgVzhYWHs9O6LHgGQY+4c0d2Ss+fz+JApm35cuDqVTlNOBkGzyDMmBByGo0GDeTK\ncESm7soVeZ3EypWAv7/SaUwLzyCoiEWL5AJAy5crnYRINxo3lv1oI0cCFy8qncb88QzCTO3ZI/8T\nRUUBdnZKpyHSrS+/BL74Qi6LW6uW0mlMA4e5EgAgPh7w8pJHWp06KZ2GSPeEAF5+WfaxhYZyDZOS\nYBMTITMTGDgQmDWLxYHMl0oFrFgBJCTIacJJP3gGYUaEkFN3CwGsXcujKjJ/iYlAu3ZyJUSuo/54\nPIOo4D7/HDhzRi7+w+JAFUGTJsD33wMjRgB//aV0GvPDMwgzcfAgEBAgO+2aN1c6DZFhffaZHPp6\n5AhQs6bSaYwTO6krqORkeRHcqlWAn5/SaYgMTwg5ai8nR65xwjPoR7GJqQK6f19eDDd5MosDVVwq\nlZzx9cIFOU046YZeC8To0aNhY2MDJycnzW3BwcGws7ODm5sb3NzcsGvXLs19CxYsgIODA5ycnLBn\nzx59RjMbr74KPPUU8PbbSichUlb16sCWLXJU06+/Kp3GPOi1QIwaNQoRERFFblOpVJg+fTpiY2MR\nGxsL/3+vl4+JicGWLVsQFxeHiIgIjBs3DtnZ2fqMZ/K++Uau27t6NU+piQCgaVNgwwZg6FDg8mWl\n05g+vRYIHx8f1KtX75HbtbWD7dixA0FBQbC0tIStrS3UajWioqL0Gc+kRUcDM2cCP/wA1KmjdBoi\n49GlC/DWW3KSyqwspdOYNkX6IFasWIFnn30Ww4cPx82bNwEAycnJsCs0J4SdnR2SkpKUiGf0srOB\nYcPkdANt2iidhsj4TJsmh8CyP6J8Khn6BSdNmoT33nsPgOyPmDp1KtatW1eqbQQHB2u+9/X1ha+v\nrw4TGr8vvgBatgRefFHpJETGSaUCliyRiw2NHg3Y2CidyPAiIyMRGRlZrm3ofZhrQkIC+vbti7i4\nuEfuu3LlCrp06YILFy5g3rx5qF69OmbMmAEA6NOnD2bOnImOHTsWDVzBh7neuCHXlN6/X/5LRMV7\n/XXg7l1Odw+YyDDX1NRUzfebN2+GWq0GAPTq1QuhoaHIzc1FUlISzpw5A0+ucPOIuXOBwYNZHIhK\n4p13gK1bAS3Hp1QCem1iGjJkCPbv34/r16+jSZMmmDt3Lvbt24fTp08jOzsbTZs2xTfffAMA8PDw\nwIABA+Ds7AwLCwuEhISgMhdPLuL334GNG4Hz55VOQmQa6tUD3n0XmD5dToHP0X6lwyupTUjfvoCv\nrzxtJqKQPNdbAAAY1ElEQVSSyckBnJyATz4BevVSOo1yTKKJicrm55/lmcPkyUonITItlSsDH38s\nD6xycpROY1pYIExAXp48Rf7oI6BqVaXTEJme3r0BW1s50zGVHJuYTMBXX8mrQ/ftYxsqUVmdPg10\n7y7na6pbV+k0hsfZXM1QejrQujWwYwfg7q50GiLT9sorsjhUxFXoWCDM0MyZQEoK8O23SichMn0p\nKYCjI3D8OGBvr3Qaw2KBMDMJCUDbtvLUuHFjpdMQmYf584GTJ4HwcKWTGBYLhJkJDATUauDfmUmI\nSAfu3ZNzmK1bB/j4KJ3GcFggzMiRI0BQkLw4rkYNpdMQmZcNG+REfsePAxYVZCwnr4MwE/n5wGuv\nyVNhFgci3QsKkoVh/Xqlkxi3JxaIkydPGiIHFbJxo1xjd+hQpZMQmScLC3kGMWsWkJmpdBrj9cQm\nJl9fX6SkpCAgIACBgYFwdHQ0VDatzL2JKTNTto9u2AB4eyudhsi8BQbKUU3vvqt0Ev3TWx/E1atX\nERYWhrCwMKSnp2Pw4MF4V6E9au4F4oMP5KilsDClkxCZv/h4OVIwLs78RwrqvZM6Li4OixYtQmho\nKHIUmtTEnAvElSuAs7NcTrR5c6XTEFUMb70FpKUBq1YpnUS/9FIgzp07h7CwMISHh8PKygqBgYEY\nNGgQrK2tyxW2rMy5QIweDVhbAwsXKp2EqOK4fVvOVrBrF+DmpnQa/dFLgfDy8kJgYCACAgJga2tb\nroC6YK4F4uRJOaHYhQtAnTpKpyGqWP73P9ms+8sv5jvfmU4LxNixY+Hv749u3bqhjhF9YpljgRAC\n6NIFGDIEGDdO6TREFU9uLuDqCnz4IdCvn9Jp9EOnBeLYsWPYtWsXfv31V1SuXBl+fn7o2bMnXFxc\ndBK2rMyxQGzdKkdRxMYClfS6xh8RFWf3bmDKFODMGaBKFaXT6J7eOqmvX7+OPXv2ICIiAqdPn4ab\nmxv8/f0xePDgMoctK3MrENnZcjqNL76QUxETkXL8/YGePYFXX1U6ie7pvEDk5+dj8+bNCAgI0Nwm\nhEBMTAx2796N2bNnlz1tGZlbgfjkE9nuuWOH0kmI6OxZ2dz7++9A/fpKp9EtvZxBtGvXDsePHy9X\nMF0ypwJx/Trw7LPAgQPyXyJS3oQJcuXGTz9VOolu6aVAvP3227CxscGgQYNQs2ZNze31FSqv5lQg\npkyR/372mbI5iOiB1FTAwUFOmPnMM0qn0R29FIhmzZpBpWXcV3x8fOnS6Yi5FIjz54FOneSprJWV\n0mmIqLDFi2WB2LpV6SS6w+m+TUjv3kC3bsD06UonIaKHZWXJs4hvvpF9EuZAp9N9L168WPP9pk2b\nitw3a9asUkajwvbsAf74A5g8WekkRKRNtWrAokXyAC4vT+k0yim2QGzcuFHz/fz584vct2vXLv0l\nMnO5ufJN99FH5jnWmshcDBok12NZs0bpJMrhgkEG9s03QMOG5nu1JpG5UKnkMPR33gHu3lU6jTJY\nIAzo9m1gzhz5pjPX+V6IzEm7doCvr+y0roiK7aS2tLREjX/Xu7x37x6qV6+uue/evXvIzc01TMKH\nmHIndUWZVpjInPz9t5zl9bffgCZNlE5TdhzFZMTi44HnnpMLkzz1lNJpiKg03nlHFgpT7o9ggTBi\ngwfLxYDeeUfpJERUWnfuyDUjtm+XK9CZIhYII3XoEDB0qLwo7t9WOyIyMStXAt99J6fGMcU+RJ1e\nB0G6kZ8PvPYasGABiwORKRs1CkhPB7ZsUTqJ4bBA6Nn69YCFhVwMiIhMl6UlsGQJ8OabwP37Sqcx\nDDYx6VFmpmy3DA0FOnRQOg0R6ULfvkDnzsCMGUonKR32QRiZ99+X88uHhiqdhIh05cIFoGNHOeFm\nw4ZKpyk5o+uDGD16NGxsbODk5KS57ebNm+jevTucnZ3h5+eHf/75R3PfggUL4ODgACcnJ+zZs0ef\n0fQuORlYtkzO50JE5qN1aznoZO5cpZPon14LxKhRoxAREVHktjlz5qB37944ffo0/P39MWfOHABA\nTEwMtmzZgri4OERERGDcuHHIzs7WZzy9mj0bGDsWaNZM6SREpGtz5siWgfPnlU6iX3otED4+PqhX\nr16R23bu3IkRI0YAAIYPH44d/661uWPHDgQFBcHS0hK2trZQq9WIiorSZzy9iYmRC6DPnKl0EiLS\nBysr+f/b1PohSsvgo5jS0tJg9e8KOQ0aNEBqaioAIDk5GXZ2dprH2dnZISkpydDxyk0IOVvr3LlA\nnTpKpyEifZk8WU7bb+Kt4Y/FYa469sMPwK1bwJgxSichIn2qUkVO4vf66+a7ZkQlQ79gw4YNcf36\ndTRo0ABpaWmwtrYGIM8YEhMTNY9LSkpCk2JmxgoODtZ87+vrC19fX31GLrH794E33gBCQuSYaSIy\nb/37A59+KifgfOUVpdMUFRkZicjIyHJtQ+/DXBMSEtC3b1/ExcUBAKZMmQJ7e3tMmzYNS5cuRXx8\nPJYvX46YmBiMHz8eR48eRUpKCry9vfHnn3+icuXKRQMb8TDXJUuAyEjgxx+VTkJEhhITA/TpI4e/\nGnOzstFdBzFkyBDs378f169fh42NDd5//33069cPgYGBuHbtGho1aoSwsDDUrVsXgFy5bt26dbCw\nsMCSJUvg5+f3aGAjLRBpaXIN24MHgTZtlE5DRIY0ciTQuDHw0OKbRsXoCoQ+GGuBmDRJNistX650\nEiIytORkOVvzyZNA06ZKp9GOBUIh587JVafOn5fD34io4gkOls1MGzcqnUQ7FgiF+PsDfn7AtGlK\nJyEipWRkyKusN20CvLyUTvMoo5tqoyKIiAAuXQImTlQ6CREpqWZN4MMP5XVQRnYMW2YsEOWQmyvH\nQH/0kRwTTUQV24gRQHY2EBamdBLdYIEoh6+/BmxsgBdeUDoJERkDCwvgk0+At94CsrKUTlN+7IMo\no3/+ke2Nu3cDrq5KpyEiYzJwIODpCbz9ttJJHmAntQG9+SZw4wbwzTdKJyEiY/Pnn7Kj+uxZ2cpg\nDFggDOSvv+TRQVwc8NRTikYhIiM1fbpcVfJ//1M6icQCYSCDBgFubnLNByIibW7dkrMq/PwzUGjN\nNMWwQBjA8eNAQIC8IKZ6dcViEJEJWL5cFojt25VOwgJhEAMHAl27yrngiYge5949oHlz4Ndf5Vxt\nSmKB0LM//gC8vYH4eHlRDBHRk3zwgey3XLVK2RwsEHo2bhzQqFHFWKyciHTj5k2gZUs5qMXWVrkc\nLBB6lJIiTxEvXAAaNjT4yxORCZs27cEKdEphgdCj2bPlxXErVhj8pYnIxF2+DLi7y6am//xHmQws\nEHpy547saDp+HLC3N+hLE5GZGDZMzrrwxhvKvD4LhJ4sXQocOwaEhhr0ZYnIjPz2G9C7tzyLqFrV\n8K/P6b71ICdHFgilqj4RmQdXV8DREdiwQekkJccC8QShoXIEQtu2SichIlP3xhvAxx8D+flKJykZ\nFojHEEKOOnjzTaWTEJE56NZNNi/t3Kl0kpJhgXiM3bvlv35+yuYgIvOgUskDTiWHu5YGC8RjFJw9\nqFRKJyEiczFoEJCYKAe+GDsWiGJERwMXLwKBgUonISJzUqmSnAr8o4+UTvJkHOZajMGD5YIfr72m\n95ciogomI0NeW3XoEPDMM4Z5TV4HoSOXLgHt2slJ+WrX1utLEVEFNWeOnMInJMQwr8cCoSOTJgF1\n6wIffqjXlyGiCiwtTZ49/P67YZYlZYHQgbQ0oHVr4Nw5OXMrEZG+TJwI1K8vpwTXNxYIHZgzB7h6\nFfjqK729BBERADkQxstLNmfXqqXf12KBKKeMDKBZM9lx1Lq1Xl6CiKiIwYOBDh3klOD6xLmYymnV\nKsDHh8WBiAznjTfkfG85OUoneRQLxL9yc4FPPuG0GkRkWM89B7RoAYSFKZ3kUSwQ/woPB5o0Adq3\nVzoJEVU0BdNvGFuDPwsEOCkfESmrZ085w+vevUonKYoFAsAvvwBZWUCvXkonIaKKSKWSfRHGNokf\nCwTkH+WNNwAL7g0iUkhQEHDhAhATo3SSByr8R2JsLHD2LDB0qNJJiKgiq1JFzv1mTJP4KXYdRLNm\nzVCnTh1YWlqicuXKiIqKws2bNxEYGIhr167hqaeeQmhoKOrWrVs0sI6vg1B6IXEiogJ37shJ/KKj\n5b+6ZFIXyjVv3hwxMTGoX7++5rYpU6bA3t4e06ZNw6effor4+HgsW7asyPN0WSASEgAPD7mI+H/+\no5NNEhGVy8yZwN27wGef6Xa7JlcgTpw4ASsrK81t9vb2iIqKgpWVFa5fv4727dvj4sWLRZ6nywLx\n6qty+T9j6xgioorr6lVArQb++ANo0EB32zWpAtGiRQvUrVsXubm5GDt2LCZPnow6deogPT1d85iH\nfwZ0VyBu3ABatgTOnAFsbcu9OSIinXnlFcDOTs4Npytl+eyspLuXL51jx47B2toaaWlp6NmzJ9q0\naVPi5wYHB2u+9/X1ha+vb6lf/4svgAEDWByIyPi8/jrQubPsG61Ro2zbiIyMRGRkZLlyGMVkfQsW\nLAAArFy5EsePH0eDBg2QlpYGLy8vvTQx3bsnJ+Xbtw9wcCjXpoiI9KJ/f6BHDzkluC6YzGR9mZmZ\nyMzMBABkZGQgIiICarUavXr1wrp16wAA69atQy89Xbn23XdyxTgWByIyVm++CSxZAuTlKZdBkTOI\n+Ph49O/fHyqVCpmZmQgKCsL7779fZJhro0aNEBYWpvNhrnl5crbWb7+VM7cSERkrb285mCYgoPzb\nMqlO6rIqb4EID5dV+cgReXk7EZGx2rYNmDdPXhdR3s8rk2liUkrhSflYHIjI2PXtK6+JKGdfc5lV\nqAKxfz9w+zbwwgtKJyEiejILC2Un8atQTUy9esmhra+8ouNQRER6cv++nHYjIgJwdi77dtgH8Rhx\ncXLIWHw8UK2aHoIREenJwoXAuXPAmjVl3wYLxGO89JIcvTRrlh5CERHp0T//yGVJf/sNePrpsm2D\nBaIYiYmAiwtw6RJQr56eghER6dGMGXLVuU8+KdvzWSCK8frrcgRTWXcsEZHSkpLkge7Fi2U70GWB\n0OLWLcDevnynZkRExqA8TeUsEFp8/LFcNW79ej2GIiIygLg4oGdPuZZN5cqley4vlHtIXh6wYgUw\ndarSSYiIys/JCWjVCvjhB8O8nlkXiJ07gYYN5cR8RETmYMoU3a82VxyzLhCffQZMnqx0CiIi3enX\nTzYx/fab/l/LbAvE778Dp04BgwcrnYSISHcqVQImTAA+/1z/r2W2ndRTpgB16gAffmiAUEREBpSa\nKkczXboE1K9fsudwFNO/0tPlinGnTgFNmhgmFxGRIf33v3JuphkzSvZ4jmL615o1QNeuLA5EZL6m\nTJGjNPW54pzZFQghZNvclClKJyEi0p/nngOsreVoTX0xuwLx88/yApJOnZROQkSkX/oe8mp2BaLg\n7IErxhGRuQsIAE6flqM29cGsOqnj44G2bYG//wZq1jRwMCIiBbz7rpwO/ElnEhV+FNObb8oOmyVL\nDByKiEghSUlyNFNCghzaX5wKXSAyM+VsrcePy9lbiYgqioAAwNcXmDSp+MdU6GGuGzcC7duzOBBR\nxTNliux/1fXhvlkUCCFk+xuHthJRReTjI0dv/vKLbrdrFgXi8GHZxNS9u9JJiIgMT6XSz5BXs+iD\nCAwEOnbkug9EVHFlZABNmwInTsiphh5WITupk5MBR0fZg/+f/yiXi4hIaTNmABYWwOLFj95XIQvE\ne+8BN27IOUmIiCqyS5fkYJ3Ll4EaNYreV+FGMd2/D3z11eOHdhERVRT29nIFze+/1832TLpAhIfL\n5iUHB6WTEBEZh4LOal20DZl0gfj8cy4pSkRUWPfussP6yJHyb8tkC8SJE8CVK0DfvkonISIyHhYW\n8sBZF0NeTbaTeuRI4NlngbfeUjoREZFxuX0baN4cOHMGaNxY3lZhRjGlpgo88wxw8SJgZaV0IiIi\n4zNpEtCgATB3rvy5whSIDz8UuHgRWLVK6TRERMbp3DmgWzc55LVKFTMZ5hoREQEnJyc4ODhg0aJF\nWh/z5ZfsnCYiehwHB/m1eXPZt2FUBeL+/fuYMGECIiIicPr0aYSHhyM2NvaRxz39NODurkBAIxMZ\nGal0BKPBffEA98UDFX1flHd+JqMqEMePH4darYatrS0qVaqEwMBA7Nix45HHcdZWqaK/+QvjvniA\n++KBir4v+vSR0xHFxJTt+UZVIJKSktCkSRPNz3Z2dkhKSnrkcQMHGjIVEZFpqlQJmDhRXjNWpufr\nNk75qFSqEj2uShU9ByEiMhNjxgCtWpXxycKIHDhwQPTu3Vvz8+LFi8UHH3xQ5DH29vYCAL/4xS9+\n8asUX/b29qX+TDaqYa5ZWVlo06YNDh8+DGtra3To0AEhISFwZ480EZHBGVUTU7Vq1fDll1/Cz88P\n+fn5GDFiBIsDEZFCjOoMgoiIjIdRjWIqrCQXzE2dOhVqtRru7u5ar5cwF0/aF2vXroWzszOcnJzQ\ntm1bxJR1TJuRK8l7AgCio6NRqVIlbNmyxYDpDKsk+yIyMhKenp5wdXVF586dDZzQcJ60L1JSUtCt\nWzeo1Wq0bt0aISEhCqQ0jNGjR8PGxgZOTk7FPqZUn5vl6lXWk6ysLNGsWTORlJQkcnJyRNu2bcXJ\nkyeLPCY8PFz069dPCCHEyZMnhYuLixJR9a4k++L48eMiPT1dCCHErl27hKurqxJR9aok+0EIIXJz\nc0WXLl1E7969RXh4uAJJ9a8k++Lq1atCrVaLa9euCSGEuHHjhhJR9a4k+2L27Nni7bffFkIIkZaW\nJurWrSuysrKUiKt3Bw4cECdPnhSOjo5a7y/t56ZRnkGU5IK5nTt3YsSIEQAANzc35Obmar1mwtSV\nZF94enqidu3aAICOHTsiOTlZiah6VdKLKD/77DMMGjQIDRs2VCClYZRkX3z//fcIDAyEtbU1AKB+\n/fpKRNW7kuyLJk2aID09HQCQnp6Ohg0bomrVqkrE1TsfHx/Uq1ev2PtL+7lplAWiJBfMlfSiOlNX\n2t8zJCQE/fr1M0Q0gyrJfkhOTsa2bdswYcIEACW/rsbUlGRfXLhwAVeuXIGXlxecnZ2xcuVKQ8c0\niJLsi1deeQVnz55F48aN4eLigmXLlhk6ptEo7eeJUY1iKlDS/9jiof51c/xAKM3vFBkZiVWrVuHw\n4cN6TKSMkuyHadOmYeHChZpZKx9+f5iLkuyLvLw8nDlzBr/++isyMzPRvn17eHl5Qa1WGyCh4ZRk\nX8yfPx+urq6IjIzEpUuX0L17d5w6dUpz1l3RlOZz0yjPIOzs7JCYmKj5OTExsUjV0/aYpKQk2NnZ\nGSyjoZRkXwDA6dOn8fLLL2P79u2PPcU0VSXZDzExMQgKCkLz5s2xefNmTJw4Edu3bzd0VL0ryb54\n+umn0aNHD1SvXh1WVlbo3LkzTp8+beioeleSfXHo0CEEBAQAAOzt7dG8eXOcP3/eoDmNRak/N3Xa\nQ6Ij9+7dE02bNhVJSUkiOztbtG3bVsTExBR5THh4uOjfv78QQoiYmBjh7OysRFS9K8m+uHz5srC3\ntxdHjx5VKKX+lWQ/FDZy5EixefNmAyY0nJLsi5MnT4pu3bqJ3NxckZGRIRwcHERsbKxCifWnJPti\n4sSJIjg4WAghREpKimjUqJGm894cxcfHP7aTujSfm0bZxFTcBXMFw9PGjRuHF198Efv27YNarUbV\nqlXx7bffKpxaP0qyL95//33cunVL0/ZeuXJlREVFKRlb50qyHyqKkuwLNzc39OzZE87OzsjJycHL\nL78MV1dXhZPrXkn2xXvvvYfhw4fDwcEBeXl5+OCDDzSd9+ZmyJAh2L9/P65fv44mTZpg7ty5yMnJ\nAVC2z01eKEdERFoZZR8EEREpjwWCiIi0YoEgIiKtWCCIiEgrFggiItKKBYKIiLRigSAqh4ULF2LD\nhg1KxyDSCxYIojIQQiA/Px979uyBn59fkfvy8/MVSkWkW0Z5JTWRMUpISICfnx+8vLwQGxuLn376\nCdnZ2bCyssLIkSNRrVo1xMXFoWPHjhg0aBDGjRuH3Nxc2NnZYf369WY75TaZL55BEJXCxYsXMWXK\nFJw6dQrR0dF4/vnnNfddu3YNhw8fxuLFizFixAisWLECcXFx8PLywuzZsxVMTVQ2LBBEpdC0aVN4\neHgAAHbv3g1/f38AcsrkgQMHAgBSU1ORlZWFDh06AACGDx+OgwcPKhOYqBxYIIhKoWbNmprvo6Ki\n4Onpqfm5Ro0aWp/D6c7IVLFAEJXB2bNn0aZNG62LrVhbW6N69eo4evQoAGDDhg3o3LmzoSMSlRs7\nqYlKoaAg7Nq1S9O89PB9ALB27VpNJ7WtrS2HwpJJ4nTfRGXQo0cPrF27FjY2NkpHIdIbFggiItKK\nfRBERKQVCwQREWnFAkFERFqxQBARkVYsEEREpBULBBERacUCQUREWv0f7H5+wWyWJFYAAAAASUVO\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x392ca50>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 6.7 page no : 246"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math\n",
      "\n",
      "# variables\n",
      "do = .0508\n",
      "Uz = .8467                 # cyclohexane\n",
      "aveP = 774.9               # the density of cyclohexane\n",
      "u = 8.892*10**-4           # the viscosity\n",
      "aveF = .00570\n",
      "\n",
      "# Calculation\n",
      "Nre = do*Uz*aveP/u\n",
      "rw = (1./2)*aveP*(Uz**2)*aveF\n",
      "U = math.sqrt(rw/aveP)\n",
      "\n",
      "# for y = .001\n",
      "y001 = (2.54*10**-5)*U*aveP/u\n",
      "ratio = 1 + (U)*(-0.1775)/(1.029)\n",
      "\n",
      "Uz = U*22.77\n",
      "# Results\n",
      "print \"Nre = %.2e \"%Nre\n",
      "print \"The wall shear stress rw = %.3f N m**-2\"%rw\n",
      "print \"The friction velocity U* = %.5f m s*-1\"%U\n",
      "print \"Universal velocity Distribution :\" \n",
      "print \"for y = 0.001 = %d \"%y001\n",
      "print \"Velocity ratio = %.3f\"%ratio\n",
      "print \"Uz = %.3f m s**-1\"%Uz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Nre = 3.75e+04 \n",
        "The wall shear stress rw = 1.583 N m**-2\n",
        "The friction velocity U* = 0.04520 m s*-1\n",
        "Universal velocity Distribution :\n",
        "for y = 0.001 = 1 \n",
        "Velocity ratio = 0.992\n",
        "Uz = 1.029 m s**-1\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 6.8 Pageno :250"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import math\n",
      "\n",
      "# variables\n",
      "Cp = 4184.           # lg-K-=4184Jkg-K-*\n",
      "P = 1000.            # g cm**-3\n",
      "k = 0.628            # wm**-1\n",
      "Nre = 1.2*10**5\n",
      "v = 1*10**-6\n",
      "do = 2*0.05\n",
      "\n",
      "# calculation\n",
      "alpha = k/(P*Cp)\n",
      "qa = (1.7*10**4)/(P*Cp)\n",
      "Uz = Nre*v/(do)\n",
      "U = Uz*math.sqrt(.0045/2)\n",
      "y = (5*v)/U\n",
      "\n",
      "# Results\n",
      "print \"A = %.3e m**2s**-2\"%alpha\n",
      "print \"The average velocity = %.2f m s**-1\"%Uz\n",
      "print \"U* = %.5f m s**-1\"%U\n",
      "print \"the value of y = %.3e cm\"%y\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "A = 1.501e-07 m**2s**-2\n",
        "The average velocity = 1.20 m s**-1\n",
        "U* = 0.05692 m s**-1\n",
        "the value of y = 8.784e-05 cm\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 6.9 - Page No :258\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# Variables\n",
      "p = 0.84*62.4;  \t\t\t #[lbf/ft**3] - density\n",
      "dP = 80.*144;  \t\t\t #[lbf/ft**2] - pressure\n",
      "dz = 2000.;  \t\t\t #[ft] - length of pipe\n",
      "gc = 32.174;  \t\t\t #[(lbm*ft)/(lbf*sec**2)] - gravitational conversion consmath.tant\n",
      "dpbydz = -dP/dz;\n",
      "do = 2.067/12;  \t\t\t #[ft]\n",
      "\n",
      "# Calculations\n",
      "U = 2000*(1./24)*(1./3600)*(42)*(1./7.48)*(1./0.02330);\n",
      "# using the formula f = ((do/2)*(-dp/dz)*gc)/(p*(U)**2)\n",
      "f = ((do/2)*(-dpbydz)*gc)/(p*(U)**2)\n",
      "\n",
      "# Results\n",
      "print \"f = %.5f (dimensionless)\"%f\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "f = 0.00979 (dimensionless)\n"
       ]
      }
     ],
     "prompt_number": 19
    }
   ],
   "metadata": {}
  }
 ]
}