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
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
|
{
"metadata": {
"name": "",
"signature": "sha256:3f70bd8e66ed069013ea8da0a0db103f64597532e63478b727d2e9fd79a10d32"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Radiation: Processes and Properties"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example 12.1 Page 731 "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"\n",
"import math\n",
"\n",
"\n",
"A1 = .001\t\t;#[m^2] Area of emitter\n",
"In = 7000\t\t;#[W/m^2.Sr] Intensity of radiation in normal direction\n",
"A2 = .001\t\t;#[m^2] Area of other intercepting plates\n",
"A3 = A2\t\t\t;#[m^2] Area of other intercepting plates\n",
"A4 = A2\t\t\t;#[m^2] Area of other intercepting plates\n",
"r = .5\t\t\t;#[m] Distance of each plate from emitter\n",
"theta1 = 60.\t;#[deg] Angle between surface 1 normal & direction of radiation to surface 2\n",
"theta2 = 30.\t;#[deg] Angle between surface 2 normal & direction of radiation to surface 1\n",
"theta3 = 45.\t;#[deg] Angle between surface 1 normal & direction of radiation to surface 4\n",
"#calculations\n",
"\n",
"#From equation 12.2\n",
"w31 = A3/(r*r);\n",
"w41 = w31;\n",
"w21 = A2*math.cos(theta2*0.0174532925)/(r*r);\n",
"\n",
"\n",
"#From equation 12.6\n",
"q12 = In*A1*math.cos(theta1*0.0174532925)*w21;\n",
"q13 = In*A1*math.cos(0*math.pi/180.)*w31;\n",
"q14 = In*A1*math.cos(theta3*0.0174532925)*w41;\n",
"#results\n",
"\n",
"print '%s %d %s' %(\"\\n (a) As Intensity of emitted radiation is independent of direction, for each of the three directions I = \",In,\"W/m^2.sr\")\n",
"print '%s' %(\"\\n\\n (b) By the Three Surfaces\\n Solid angles subtended Rate at which radiation is intercepted \\n\")\n",
"print '%s %.2e %s' %(\"w4-1 =\",w41,\" sr\")\n",
"print '%s %.2e %s' %(\"\\t \\t \\t \\t \\t \\t q1-4 =\",q14,\" W\") \n",
"print '%s %.2e %s' %(\"\\nw3-1 = \",w31,\" sr\")\n",
"print '%s %.2e %s' %(\"\\t \\t \\t \\t \\t \\t q1-3 =\",q13,\" W\")\n",
"print '%s %.2e %s' %(\"\\n w2-1 = \",w21,\" sr\")\n",
"print '%s %.2e %s' %(\"\\t \\t \\t \\t \\t \\tq1-2 = \",q12,\" W \");\n",
"#END\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" (a) As Intensity of emitted radiation is independent of direction, for each of the three directions I = 7000 W/m^2.sr\n",
"\n",
"\n",
" (b) By the Three Surfaces\n",
" Solid angles subtended Rate at which radiation is intercepted \n",
"\n",
"w4-1 = 4.00e-03 sr\n",
"\t \t \t \t \t \t q1-4 = 1.98e-02 W\n",
"\n",
"w3-1 = 4.00e-03 sr\n",
"\t \t \t \t \t \t q1-3 = 2.80e-02 W\n",
"\n",
" w2-1 = 3.46e-03 sr\n",
"\t \t \t \t \t \tq1-2 = 1.21e-02 W \n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example 12.2 Page 734"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"import math\n",
"import matplotlib.pyplot as plt\n",
"%pylab inline\n",
"# Total Irradiation\n",
"#calculations\n",
"\n",
"x=([0, 5, 20, 25]);\n",
"y=([0, 1000, 1000, 0]);\n",
"\n",
"plt.plot(x,y);\n",
"plt.xlabel(\"Spectral Distribution\")\n",
"plt.ylabel(\"wavelength (micro-m)\")\n",
"#By Equation 12.4\n",
"G = 1000*(5-0)/2. +1000*(20-5)+1000*(25-20)/2.;\n",
"#results\n",
"\n",
"print '%s %d %s' %(\"\\n G =\",G,\" W/m^2\");\n",
"#END"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n",
"\n",
" G = 20000 W/m^2"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEPCAYAAAC+35gCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X1UVHX+B/D3QJpupqWAFpC05BMww6OkBooPxAJaWT7Q\nCq5rttqpzE61uf22QO1EtKmpdZTNzLOQ/TSzXylIiomPiYqaYhvHDHSGJEB8VlLi+/tj5AYKzgxz\nZ+69M+/XOXPWuczM/TDNzofv9973/eqEEAJERERW8FC6ACIi0g42DSIishqbBhERWY1Ng4iIrMam\nQUREVmPTICIiqzmsaUydOhU9e/aEXq+XttXV1SE+Ph4GgwEJCQk4e/as9LPMzEwEBQVBr9dj06ZN\n0vaSkhKEh4cjODgYL7zwgqPKJSIiKzisafz1r39FQUFBi23p6elITk7G4cOHkZiYiPT0dADmxrBu\n3TocOXIEBQUFmD59Oq5duya9zooVK3D06FGcOHECX3zxhaNKJiIiCxzWNGJjY3H33Xe32Jafn4+0\ntDQAQGpqKvLy8gAAeXl5SElJgaenJ3x9fREcHIzi4mKcPHkSjY2NCA8Pv+k5RETkfE49plFTU4Me\nPXoAALy8vFBdXQ0AqKyshJ+fn/Q4Pz8/mEwmVFZWwt/fX9ru6+sLk8nkzJKJiKgZHggnIiKr3ebM\nnXl7e6O2thZeXl6oqamBj48PAPPIwmg0So8zmUzw9/dvdXvzEUlzDzzwAI4fP+7YX4CIyMUEBgbi\nxx9/tPrxTh1pJCUlITc3FwCQm5uLpKQkafvq1avR0NAAk8mE0tJSREdHw9/fHx4eHjh48CAA4JNP\nPpGec6Pjx49DCOHWt//7P4GRIwXS09MVr0UtN74XfC9uvOXlCdx3H9+Lpputf2w7bKTx5JNPYtu2\nbaitrYW/vz/mzp2LOXPmYOLEiVixYgV69eqFNWvWAAAiIyMxduxYGAwGeHh4IDs7Gx06dAAAfPzx\nx5g6dSquXr2KkSNH4vHHH3dUyZq3fj0wejTQ7ExmIrrB8OFAVRVw+jRw/RAr2cBhTePTTz9tdfvm\nzZtb3f7aa6/htddeu2l7ZGSkNNKgtjU2Anl5wD/+AeTkKF0NkXp17gwEBAAbNwKpqUpXoz08EO4i\n9u8H7r4bCAwE4uLilC5HNfhe/I7vxe8eeSQOGzYoXYU26YQQLrEIk06ng4v8Ku3yxhvAr78CWVlK\nV0KkfqdOAUFBQHU1cH0m3G3Z+t3JkYaLWL8eGDNG6SqItOGee4A+fYAdO5SuRHvYNFyA0Wi+DRqk\ndCVE2jF6tPmPLbINm4YL2LABSEwEbnNq6oZI28aMMTcNN57Vbhc2DRfAqSki24WFmY8DlpUpXYm2\nsGlo3KVLwM6dQEKC0pUQaYtOxymq9mDT0LjCQiA6GujWTelKiLSnaYqKrMemoXFNKXAist3w4cCh\nQ+Z0OFmHTUPDmlLgPJ5B1D6dO5sbx8aNSleiHWwaGtY8BU5E7TNmDJgOtwGbhoZt2MBRBpG9kpOB\nr78Grq8wTRawaWgYT7Ulsh/T4bZh09AopsCJ5MNTb63HpqFRTIETyYfpcOuxaWgUp6aI5MN0uPXY\nNDSIKXAieTEdbj02DQ1iCpxIfkyHW4dNQ4OYAieSH9Ph1mHT0BimwIkcg+lw67BpaAxT4ESOw3S4\nZWwaGsMUOJHjMB1uGZuGxvBUWyLHYTrcMjYNDWEKnMjxeOrtrbFpaAhT4ESOx3T4rbFpaAinpogc\nj+nwW2PT0AimwImcg+nwW2PT0AimwImch+nwtrFpaART4ETOw3R429g0NIApcCLnYjq8bWwaGsAU\nOJHzMR3eOjYNDWAKnMj5mA5vHZuGBvBUWyLnYzq8dWwaKscUOJFyeOrtzdg0VI4pcCLlMB1+MzYN\nlePUFJFymA6/mSJNIz09HX379kX//v0xbtw4XL58GXV1dYiPj4fBYEBCQgLOnj0rPT4zMxNBQUHQ\n6/XYtGmTEiUrgilwImUxHX4zpzeNH3/8ETk5OSgtLcUPP/wAT09PfPrpp0hPT0dycjIOHz6MxMRE\npKenAwBKSkqwbt06HDlyBAUFBZg+fTquXr3q7LIVwRQ4kfKYDm/J6U2je/fu6NChAy5duoSGhgZc\nvnwZ9913H/Lz85GWlgYASE1NRV5eHgAgLy8PKSkp8PT0hK+vL4KDg7F3715nl60IpsCJlMd0eEuK\nNI2XXnoJ9913H+69917cddddiI+PR01NDXr06AEA8PLyQnV1NQCgsrISfn5+0vP9/PxgMpmcXbbT\nMQVOpA5Mh7fk9HNyjh8/jvfeew8VFRXo1q0bxo8fj9zcXFleOyMjQ/p3XFwc4uLiZHldJTAFTqQe\nTenw1FSlK7FfUVERioqK2v18pzeNvXv3YsiQIdKo4vHHH8euXbvg7e2N2tpaeHl5oaamBj4+PgDM\nIwuj0Sg932Qywd/fv9XXbt40tI4pcCL1SE4GXnnFnA7v0EHpauxz4x/Uc+bMsen5Tp+eeuCBB7Bn\nzx5cuXIFQggUFhYiMDAQSUlJ0ogjNzcXSUlJAICkpCSsXr0aDQ0NMJlMKC0tRXR0tLPLdjqeakuk\nHkyH/87pI42BAwdi3LhxMBgM8PDwQHh4OJ577jlcvnwZEydOxIoVK9CrVy+sWbMGABAZGYmxY8dK\nj8/OzkYHrbd6C5gCJ1KfplNvR4xQuhJl6YRwjayjTqeDi/wqWLoU2L0byMlRuhIianLwIDB+PHDs\nmDm/4Sps/e5kIlyFODVFpD5Mh5uxaagMU+BE6sR0uBmbhsowBU6kXkyHs2moDlPgROrFdDibhqow\nBU6kbkyHs2moClPgROrn7muHs2moCFPgROrn7muHs2moCE+1JVI/d0+Hs2moBFPgRNrhzqfeWryM\nyNGjR7F9+3ZUVFRAp9MhICAAsbGxCA4OdkZ9boNrgRNpx5gx5nT4ggWulQ63RpsjjZycHERHR+Pl\nl19GVVUV/vjHPyIgIACnTp3Cyy+/jIEDB8p2SXPi1BSRlrhzOrzNv2vPnDmDLVu24M4772z15+fP\nn8fKlSsdVZdbaUqBf/qp0pUQkTWap8P791e6GufiBQtV4MsvgSVLzGlwItKG/Hzg7beB7duVrsQ+\ntn53WpxBP3bsGBYtWgSj0YjGxkZpJ1999VX7q6QWmAIn0p7hw4GUFHM6/Pqacm7B4kijX79+ePbZ\nZxESEgIPD/MhEJ1Oh2HDhjmlQGtpdaTR2Aj4+pqnpxjqI9KWRx81HxDX8jKwso80unfvjpkzZ9pV\nFLWNKXAi7XKltcOtZXGkkZOTg4qKCowaNQq33367tD0iIsLhxdlCqyONN94wn4WRlaV0JURkq1On\ngKAgoLpau2uHyz7SOHr0KHJyclBYWChNTwHA1q1b21chtbB+vfkgOBFpT/N0uLssA2txpPHAAw/g\n+++/R8eOHZ1VU7tocaRhNALh4UBVFUN9RFo1dy5w5gywcKHSlbSP7Mu9hoaG4vz583YVRa1jCpxI\n+5oWZtLY36ztZvHrqra2Fn369MHAgQOlYxo85VYe69cDU6YoXQUR2aN5Otwdgn4Wp6eKiop+f/D1\nYQxPubXfpUvm+VCjkUu7EmndM88Af/wj8MorSldiO1u/O21KhK9fvx5jVHqBJK01DabAiVyHltPh\nsh/TaO6NN96wuSBqHVPgRK7DndYO53oaCuBa4ESuxZ3WDrepaWRnZzuqDrfCFDiR63GXtcOtOtlz\n9erV2HF9bcMTJ05g/PjxDi3K1XEtcCLXk5xsPhB+7Zp20+HWsDjSmDVrFpYvX46IiAiEh4dj+fLl\nmDVrljNqc1lccInI9bjL2uEWz54KCgpCaWmpdAmRxsZGBAcH47///a9TCrSWVs6eYgqcyHVpMR3u\nkLOnmifCmQ63D1PgRK7LHdLhFr+6XnnlFYSEhGDUqFEQQuCbb77B3LlznVGbS2IKnMh1uUM6/JbT\nU42NjVi7di0GDRqEPXv2QKfTYdCgQfD393dmjVbRwvQUU+BErk9r6XDZE+EPPvggiouL7S7M0bTQ\nNJgCJ3J9WkuHy35MY/jw4Vi4cCGMRiPq6uqkG9mOKXAi1+fq6XCLI42AgADodLqbtpeXlzusqPZQ\n+0iDa4ETuQ8trR0u+0ijoqIC5eXlN93scfbsWYwfPx6hoaEYMGAA9uzZg7q6OsTHx8NgMCAhIQFn\nz56VHp+ZmYmgoCDo9Xps2rTJrn0rhSlwIvfhyulwi01j8eLFOHfunHT/3LlzeP/99+3a6dNPP43H\nH38c3333HY4ePYqgoCCkp6cjOTkZhw8fRmJiItLT0wEAJSUlWLduHY4cOYKCggJMnz4dV69etWv/\nSmAKnMh9JCcDX39tToe7GotN46OPPkK3Zqf6dOvWDcuXL2/3Dk+fPo1Dhw7hySefNBfg4YGuXbsi\nPz8faWlpAIDU1FTk5eUBAPLy8pCSkgJPT0/4+voiODgYe/fubff+lcIUOJH7cOV0uMWmceNf9UII\n1NfXt3uHx44dg7e3NyZMmICQkBBMnjwZFy5cQE1NDXr06AEA8PLyQnV1NQCgsrISfn5+0vP9/Pxg\nMpnavX8lGI3m26BBSldCRM4yerT5j0VXY7FpjBgxAikpKdiyZQsKCwuRkpKCESNGtHuHjY2N2Ldv\nH1555RWUlpaie/fumDdvXrtfTwuYAidyP66aDrf4NbZo0SIsWbIEC69fTCU+Ph7PPfdcu3fo7+8P\nX19fDBw4EAAwbtw4zJ07Fz4+PqitrYWXlxdqamrg4+MDwDyyMBqN0vNNJlOb4cKMjAzp33FxcYiL\ni2t3nXJiCpzI/ag1HV5UVNRiGW9b2bTcq1yioqKwatUq9O3bFxkZGThz5gwaGxsRGBiIWbNmYeHC\nhSgvL8fixYtRUlKCGTNm4Ntvv0VVVRViYmJw7NgxdLjh2sNqPeWWKXAi96WFdLit351tjjTGjx+P\nzz77DHq9vtWdHD58uH0VwnxwfdKkSbh8+TJ69+6NTz75BEIITJw4EStWrECvXr2wZs0aAEBkZCTG\njh0Lg8EADw8PZGdn39Qw1KywEIiOZsMgckdjxpjT4WpuGrZqc6Tx888/495770VFRUWrTwwICHBg\nWbZT60hj2jQgJATgEiRE7ufKFaBnT6C8HLh+no/qyH7tqSZnz55FY2OjdL979+62V+dAamwaTIET\nkdrT4bInwpcsWQJvb2+EhoYiMjISkZGRiIqKsqtId8EUOBG5WjrcqmtP7d+/H15eXs6qqV3UONJ4\n4w3z2RNZWUpXQkRKOXUKCAoCqqvVuXa47CONAQMGoEuXLnYV5a6YAiciV0uHWxxpHDhwAFOmTMHg\nwYPRsWNH85N0OixevNgpBVpLbSMNrgVORE3UvHa4bKfcNvnb3/6GUaNGQa/Xw8PDA0KIVi+VTi0x\nBU5ETcaMMR8MX7AA0PrXp1VfaQsWLHB0HS6HKXAiaqLWdHh7WDymkZCQgA8//BCnTp3iyn1WunTJ\nfJptQoLSlRCRGuh0rnMBw3at3KfT6fDTTz85tDBbqemYBtcCJ6IbqXXtcIeF+9ROTU2DKXAiupFa\n0+GynXJrzVUQt27davWO3EVjI5CXx1Ntiailzp2B4cOBjRuVrsQ+bR4I37BhA/7+979j1KhRiIqK\nwj333IPGxkZUVVVh//79KCwsxPDhwzF8+HBn1qt6TIETUVua0uFqvaSINW45PXXhwgV8+eWX2LVr\nF06cOAEA6N27N2JiYvDoo4+qKvSnlukppsCJqC1qTIfzmIbCwsPNB8FjYpSuhIjUKDrafEDcjgVQ\nZSX7ZUTIelwLnIgs0fqpt2waMmIKnIgs0fra4WwaMuIFConIkubpcC2yeExDCIFt27bBaDRKizDp\ndDpMnjzZKQVaS+ljGlwLnIispaa1w2W/YOGECRNQWVmJsLAweHp6StvV1jSUxrXAichaWl473OJI\no2/fvigrK1P9lW2VHmlMmwbo9cALLyhWAhFpRH094OOjjnS47GdPRUREoLq62q6iXF1TCnz0aKUr\nISIt6NRJu+nwNqenxlw/onvx4kX069cP0dHRuP322wGYO9NXX33lnAo1gClwIrKVVtPhbTaNl156\nCUDrQxe1T1U5G8+aIiJbJSebj2lcu6aedLg12pyeiouLQ1xcHPLy8qR/N93y8/OdWaPqbdjApkFE\nttHq2uEWj2ls3rz5pm3rtRxnlFlTCnzwYKUrISKtaQr6aUmbTWPp0qXQ6/UoKyuDXq+XboGBgRgw\nYIAza1S1phR4s7ORiYis0nRJES2lw9s85fbcuXM4c+YMZs+ejaysLOm4RufOndGzZ0+nFmkNpU65\nTUoyrwU+YYLTd01EGicEcN99wObNyq0dLvtVbk+fPn3Tge9OnTrhD3/4Q/sqdBAlmgZT4ERkL6XT\n4bLnNCIjI+Hl5YU+ffqgT58+8PLyQmBgIIKDg/Htt9/aVazWMQVORPbS2nENi03j4YcfxqZNm3D6\n9GmcPn0amzdvxiOPPILly5dj+vTpzqhRtXiqLRHZa8QI4NAh4PRppSuxjsWmsXfvXowaNUq6P3Lk\nSBQXF2Pw4MGqWPRIKUyBE5EctJYOt9g0unTpgnfffRcnTpxARUUF5s+fjy5duqCxsRG3ufHCEUyB\nE5FcmtLhWmCxaXz++ecoKytDUlISkpOT8cMPP2Dt2rVoaGjAmjVrnFGjKnFqiojkkpwMfP21OR2u\ndlwjvJ24FjgRyUmptcNlX0+jtLQU77777k2LMH3zzTftr1LjmAInIrk1nUXl7KZhK4sjjX79+mHW\nrFmIiIiQFmHS6XSIjIx0SoHWcuZIY+lSYPduICfHKbsjIjdw8CAwfjxw7BjgzGvCyp7T6NatG555\n5hk8+OCDiIqKQlRUlCwN47fffkN4eLh0Cfa6ujrEx8fDYDAgISEBZ8+elR6bmZmJoKAg6PV6bNq0\nye5924vHM4hIblpZO9xi00hKSsKyZctw6tQp1NXVSTd7LVq0CEFBQVLaPD09HcnJyTh8+DASExOR\nnp4OACgpKcG6detw5MgRFBQUYPr06bh69ard+2+vS5eAnTuBhATFSiAiF6TT/X4tKjWz2DRWrlyJ\nrKwsDBkyBJGRkdLNHiaTCfn5+Zg2bZo0LMrPz0daWhoAIDU1FXl5eQCAvLw8pKSkwNPTE76+vggO\nDsbevXvt2r89mAInIkfRQjrc4oHwiooK2Xf64osv4l//+hfOnz8vbaupqUGP64vlenl5SUvMVlZW\nYkSzI0N+fn4wmUyy12QtTk0RkaOMGAGkpJjT4UqvHd4WiyONCxcu4J///CemTp0KADh+/Lhd62ls\n2LABPj4+CA8P11yinClwInIkLaTDLY40UlNTMWTIEBQXFwMAfH19MXbsWOkAtq12796Nr776Cvn5\n+aivr8f58+eRlpYGb29v1NbWwsvLCzU1NfDx8QFgHlkYjUbp+SaTCf7+/q2+dkZGhvTvplUG5cQU\nOBE5mqPXDi8qKkJRUVH7X0BYEBISIoQQIiwsTNoWGhpq6WlWKSoqEqNHjxZCCPHcc8+JhQsXCiGE\nWLBggXj++eeFEELs379fREVFiWvXrgmj0Sh69+4trl69etNrWfGr2O2f/xTi7393+G6IyI39/LMQ\nd90lRCtfcw5h63enxZFGx44dceXKFen+yZMn29+hWtF09tScOXMwceJErFixAr169ZIuURIZGYmx\nY8fCYDDAw8MD2dnZ6KDQKuwbNphT4EREjtJ87XA1Bv0shvu++uorvP322zh27BgSExOxdetW/Pvf\n/0ZiYqKzarSKo8N9RqP50iG//MKlXYnIsebNA+rqgIULHb8v2VfuA4BffvkFO3bsAADExsa65XKv\nTIETkbM4Mx0uW9MoKSlpscxr08OatkVERNhTp+wc3TS4FjgROYsz1w6XrWnExcXdtDZ4c1u3brW9\nOgdyZNPgWuBE5GzOWjvcIdNTWuDIpvHll+YD4IWFDnl5IqKb5OebL5W+fbtj9yP7BQvlDvdpEVPg\nRORsal073GLTSE1NxZ133tki3Pc///M/Di9MLZgCJyIlqDUdbrFp/PTTT3j11VfRsWNHAECnTp3g\n4WHxaS6DKXAiUooa1w63+O3v6HCf2nFqioiUosa1wy02jfT0dIwcORImkwmTJ0/GQw89hMzMTGfU\npgobNrBpEJEymqfD1cLmcF9MTAx69erl8MJs5Yizp5gCJyKlOTodLvvZU2PGjMGWLVuQmJiIcePG\nqbJhOMqGDUBiIhsGESmnaTU/tYQjLDaNl156CTt27EBQUBDGjRuHtWvXor6+3hm1KY7HM4hIaWpb\nO9zqcF9DQwO2bt2KDz/8EAUFBS1W3VMDuaenmAInIrVwZDpc9ukpALhy5Qo+//xzLFu2DPv27cNf\n/vKXdheoFVwLnIjUQk1rh1scaUyYMAHFxcX405/+hJSUFAwdOhSeKpzkl3ukMW0aoNcDL7wg20sS\nEbVLfT3g4wOUl8u/drjs154qKChAfHy8KhtFc3I2jcZGwNcX2LmToT4iUodHHzVfZXvSJHlf1yEX\nLCwpKUFZWRkaGhqkbZMnT25fhQ4iZ9PYu9d8GfTvv5fl5YiI7LZ8uXna/H//V97Xlb1pzJ49G8XF\nxTh69CiSk5OxceNGxMTEYO3atXYXKyc5m8brrwNXrwJZWbK8HBGR3U6dAoKCgOpqQM4Vr2U/EL5u\n3ToUFhbi3nvvxccff4zS0lJcuHDBriLVjilwIlIbtaTDLTaNbt26wdPTE0IIXLx4ET169MDx48ed\nUZsijEbzbfBgpSshImpJDWdRWWwaEREROH/+PKZMmYKwsDCEh4djsAt/ozIFTkRqpYZ0uE0r95WV\nlaG+vh6hoaGOrKld5DqmwbXAiUitHLF2uOwHwlNTUzFs2DDExsaiv6NXOLeDHE2DKXAiUju50+Gy\nHwifOnUqfv75Zzz//PO4//778cQTT+C9996zq0i1YgqciNRO6eMaVk1PNTQ0YP/+/fjmm2+wbNky\ndO7cGWVquXrWdXKMNJgCJyK1kzsdLvv01MiRI3Hp0iUMHjwYMTExiI2NhY+Pj92Fys3epsEUOBFp\nhZzpcNmnpwwGAzp06IDS0lIcPnwYpaWlLZZ/dRVcC5yItELJKSqrz566cOECVq5ciXfffRdVVVX4\n9ddfHV2bTewdaTAFTkRaIWc63NbvztssPWDJkiXYsWMHSkpKcP/992Pq1KmIjY21q0g12rABWLJE\n6SqIiCxrng4fMcK5+7bYNOrr6/HSSy8hIiICHeS84ImKMAVORFrTNEXl7KZhU7hPzeyZnlq6FNi9\nG8jJkbkoIiIHOXgQGD8eOHYM0Ona/zoOWbnP1XEtcCLSGqXWDnf7pnHpkvk024QEpSshIrKeTvf7\ntaicye2bBlPgRKRVSpx66/ZNg1NTRKRVI0YAhw4Bp087b59u3TQaG4G8PPMQj4hIazp1AoYPBwoK\nnLdPpzcNo9GIoUOHQq/Xo1+/fnjnnXcAAHV1dYiPj4fBYEBCQgLOnj0rPSczMxNBQUHQ6/XYtGmT\nbLUwBU5EWufsKSqnn3L7yy+/oKamBiEhIbh48SIiIiLw2WefYfny5QgMDMSsWbPw3nvvoby8HIsW\nLUJJSQlmzJiBPXv2oKqqCjExMSgrK0PHjh1b/iLtOOWWKXAi0jp70+GqP+W2Z8+eCAkJAQB06dIF\nBoMBlZWVyM/PR1paGgDzGh55eXkAgLy8PKSkpMDT0xO+vr4IDg7G3r17ZamFa4ETkdY5e+1wRY9p\nVFRUYN++fYiJiUFNTQ16XL/Or5eXF6qrqwEAlZWV8PPzk57j5+cHk8lk976ZAiciV+HMKSqLlxFx\nlIsXL2LcuHFYtGgRunbtKstrZmRkSP+Oi4tDXFxcm4/lWuBE5CpGjzanwxcssJwOLyoqQlFRUbv3\npUjTuHbtGp544glMmjQJjz32GADA29sbtbW18PLyQk1NjbRmh5+fH4xGo/Rck8kEf3//Vl+3edOw\nZP1681rgRERa1zwdbmlV7hv/oJ4zZ45N+3L69JQQAk899RSCgoLw4osvStuTkpKQm5sLAMjNzUVS\nUpK0ffXq1WhoaIDJZEJpaSmio6PtqoEpcCJyJc5Mhzv97KmdO3di6NChMBgM0F0fR2VmZiI6OhoT\nJ07EL7/8gl69emHNmjW46667AABvvfUWcnNz4eHhgfnz5yOhlW97W84A+PJL82XQCwvl+72IiJSU\nnw+8/Tawfbttz5N9uVetsOUX51rgRORq2rt2uOpPuVUaU+BE5IqclQ53u6bBFDgRuSpnnHrrdk2D\nFygkIleVnAx8/TVw7Zrj9uF2TYMpcCJyVc5Ih7tV02AKnIhcnaOnqNyqaTAFTkSurimv4ajzYt2q\nafB4BhG5OkevHe42TYMpcCJyB45Oh7tN0+Ba4ETkLhx5XMNtmganpojIXThy7XC3aBpMgRORO3Fk\nOtwtmgZT4ETkbhw1ReUWTYNTU0TkbhyVDneLpsEUOBG5G0elw12+aTAFTkTuyhFTVC7fNJgCJyJ3\n5Yh0uMs3DR7PICJ35Yh0uEs3DabAicidOSId7tJNgylwInJ3ch/XcOmmwakpInJ3cqfDXbZpMAVO\nRCR/OtxlmwZT4EREZnJOUbls0+DUFBGRmZzpcJdtGkyBExGZyZkOd8mmwRQ4EVFLck1RuWTTYAqc\niKgludLhLtk0eDyDiKgludLhLtc0mAInIrqZXOlwl2saTIETEbVOjuMaLtc0ODVFRNQ6OdLhLtU0\nmAInImqbHOlwl2oaTIETEd2avVNULtU0ODVFRHRr9qbDXappMAVORHRr9qbDXappMAVORGSZPVNU\nmmkaBQUF0Ov1CAoKQlZWVquPYQqciMgye9Lhmmgav/76K5555hkUFBTg8OHDWLt2LQ4ePHjT4zg1\nZVZUVKR0CarB9+J3fC9+5+7vhT3pcE00jeLiYgQHB8PX1xe33XYbJk6ciLy8vJsexxS4mbv/H6I5\nvhe/43vxO3d/L+xJh2uiaZhMJvj7+0v3/fz8YDKZbnocU+BERNZp73ENTTQNnU6ndAlERC5lxAjg\nu+/a8URjh95/AAAJuElEQVShAdu3bxfJycnS/XfeeUe8+eabLR4TGBgoAPDGG2+88WbDLTAw0Kbv\nY50Q9l5d3fHq6+vRv39/7Nq1Cz4+PhgyZAiys7MRERGhdGlERG7lNqULsEanTp2wdOlSJCQkoLGx\nEWlpaWwYREQK0MRIg4iI1EETB8JvxZrQn7sICAiAwWBAeHg4oqOjlS7HqaZOnYqePXtCr9dL2+rq\n6hAfHw+DwYCEhAScPXtWwQqdp7X3IiMjA35+fggPD0d4eDgK7LnMqYYYjUYMHToUer0e/fr1wzvv\nvAPAPT8bbb0XNn827D5KraD6+noREBAgTCaTuHbtmoiKihIHDhxQuizFBAQEiNOnTytdhiK2b98u\nDhw4IEJCQqRtzz33nFi4cKEQQoiFCxeKmTNnKlWeU7X2XmRkZIj58+crWJUyqqqqxJEjR4QQQly4\ncEH06dNHHDp0yC0/G229F7Z+NjQ90rA29OdOhJvONsbGxuLuu+9usS0/Px9paWkAgNTUVLf5bLT2\nXgDu+dno2bMnQkJCAABdunSBwWBAZWWlW3422novANs+G5puGtaG/tyFTqeThtzvv/++0uUorqam\nBj169AAAeHl5obq6WuGKlPXBBx9gwIABSE1NRV1dndLlOF1FRQX27duHmJgYt/9sNL0XsbGxAGz7\nbGi6aTD019KePXtw4MABbNmyBR9//DEKCwuVLolU4tlnn8Xx48fx/fffIzAwEDNnzlS6JKe6ePEi\nxo0bh0WLFqFr165Kl6OoixcvYvz48Vi0aBHuvPNOmz8bmm4afn5+MBqN0n2j0dhi5OFufHx8AADe\n3t4YN24c9u3bp3BFyvL29kZtbS0A86ij6f1xR15eXtDpdNDpdJg+fbpbfTauXbuGJ554ApMmTcJj\njz0GwH0/G03vxZ///GfpvbD1s6HppjFw4ECUlpaisrIS165dw5o1a5CYmKh0WYq4fPkyLl++DAC4\ndOkSCgoKEBwcrHBVykpKSkJubi4AIDc3F0lJSQpXpJzm0y+ff/6523w2hBB46qmnEBQUhBdffFHa\n7o6fjbbeC5s/Gw44SO9U+fn5Ijg4WAwYMEC89dZbSpejmJ9++kkYDAYRGhoq+vTpI15//XWlS3Kq\nlJQUcc8994gOHToIPz8/sWLFCnH69GkxatQoodfrRXx8vDhz5ozSZTrFje/FRx99JFJTU4XBYBD9\n+/cXCQkJwmQyKV2mU+zYsUPodDoRGhoqwsLCRFhYmNi4caNbfjZaey/y8/Nt/mww3EdERFbT9PQU\nERE5F5sGERFZjU2DiIisxqZBRERWY9MgIiKrsWkQEZHV2DRItV5//XX069cPoaGhCA0NRXFxsayv\n/9Zbb7XreXFxcSgpKWl1e//+/REZGYnQ0FA8//zzOHfunPTzhx56yK56kpOTcf78eVRUVLS47Lk1\ntm3bhm+//Va6n52djZycHJtegwhg0yCVKioqwpYtW1BaWorvvvsOO3bsQO/evWXdR2ZmZqvbhRC3\nvOpn0yUXWtu+atUqlJSU4MCBA+jRowceffRR6ee7du2yq568vLx2Xzdp69at2L17t3R/+vTp0lVe\niWzBpkGqVFNTA29vb3To0AEA0LVrV/Tq1QuAebGpV199FVFRUQgNDUVZWRkAoKqqCqNHj0ZoaCjC\nwsKwbds2AMCFCxeQkpKC4OBghIaGYu3atfjHP/6BK1euIDw8HGlpaThx4gT69euHKVOmICwsDCaT\nCTNmzMDAgQPRt29fzJ4926q6m5qNp6cnMjIycOrUKRw5cgSA+XLUgPnqzEOHDkV4eDj0ej127NiB\n2bNnW6wnICBAugJpQ0MDJk+ejJCQEIwePVq6hEzzx+zfvx/Dhw/HiRMnkJ2djYULFyI8PBw7d+5E\nRkYG5s+fDwDYu3evVEtiYqL0/Li4OMyePRtDhgzB/fffj2+++caO/6LkMhwdXSdqj3PnzomQkBDR\nv39/MWPGDFFYWCj9LCAgQGRlZQkhhPjkk0/Eww8/LIQQYuzYsWLnzp1CCCFOnDghAgMDhRBCzJw5\nU7z88sstXlsIIbp06SJtKy8vFx4eHmL//v03Pa6hoUHExcVJP4uLixMlJSU31dza9pSUFLFmzZoW\n+8vKypLqF0KIixcvWlVP0yJb5eXlQqfTieLiYiGEEE8//bR0CZ3mC3Ht27dPxMXFCSFuXoSp+f2+\nffuKXbt2CSGEmDNnjpgxY4b0+7z66qtCCPPleoYNG3bT70zu5zalmxZRa7p27YpDhw5h27Zt2L59\nO1JTUzFv3jxMmzYNADBhwgQAwPjx4zFjxgwAQGFhIcrLy6XX+PXXX3H+/Hls2bIFX375ZYvXbk3v\n3r0RGRkp3f/oo4+wcuVK6HQ6/PzzzygrK2vxc2uIVqa5Bg8ejKeeegpXrlzBmDFjEBERYVU9zfn7\n+0tL+j755JN49913ba5FCIHq6mrU19djyJAhAMwLEj3yyCPSY5qm1yIiIlpcUZrcF6enSLU8PT0x\nYsQIZGRk4P3338fnn39+y8frdDrs27cPBw8exMGDB2E0GqUG0dqX943uuOMO6d9lZWX44IMPsGvX\nLhw6dAjJycloaGiw+Xc4dOgQBgwY0GJbbGwstm/fDj8/P0ybNg3/+c9/LNZzo+bHVIQQ0n0PDw80\nNjYCAOrr629ZW2vHZm58n26//XYA5v8WTa9L7o1Ng1Tp2LFjqKiokO4fPHiwxVopa9eulf636a/k\nUaNGYdmyZdJjjh49CgCIj49Hdna2tP38+fMAzF+Ev/32W6v7r6+vR5cuXXDHHXegtrYWGzdutKru\npi/dhoYGzJ07F/fcc4+0xGYTk8kEHx8fPPXUU5g6dSr2799vsZ4bnTx5Ulr3YPXq1YiJiQFgXmOm\n6fW++OIL6fGdO3eWjns0r9Xb2xudO3eWzqxatWoVhg0bZlUN5J7YNEiVmg5e6/V6DBgwAN999x3m\nzZsn/by2thZRUVHIysrC4sWLAQDLli3D5s2bodfrERISgkWLFgEA5s2bh5MnTyIoKAhhYWHYsmUL\nAGDKlCkYMGAA0tLSbvqrOzQ0FHq9Hn369MGkSZOkL2VLJk2ahIiICERERKCmpqbFtFjT6xcWFiI0\nNBQRERH47LPP8MILL1isp/nzAaBfv35YsmQJQkJCUFlZKb1Geno6nnnmGQwaNAgeHh7Sc8aMGYNV\nq1ZJB8Kbv15OTg6effZZGAwG7N69G2+++WarvxtXyiQA4KXRSXPuv/9+lJSUoHv37kqXQuR2ONIg\nzeFfvETK4UiDiIisxpEGERFZjU2DiIisxqZBRERWY9MgIiKrsWkQEZHV2DSIiMhq/w/7Ws1RmQme\nagAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x391dbd0>"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example 12.3 Page 741"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"\n",
"import math\n",
"\n",
"\n",
"T = 2000.\t\t\t\t\t\t\t\t;#[K] temperature of surface\n",
"stfncnstt = 5.67*math.pow(10,-8)\t\t;#[W/m^2.K^4] Stefan-Boltzmann constant\n",
"E = stfncnstt*T*T*T*T; \t\t\t#[W/m^2]\n",
"#calculations\n",
"\n",
"#From Table 12.1 \n",
"constt1 = 2195. ; \t\t\t\t\t#[micro-m.K]\n",
"wl1 = constt1/T;\n",
"#From Table 12.1 \n",
"constt2 = 9382. ; \t\t\t\t\t#[micro-m.K]\n",
"wl2 = constt2/T;\n",
"\n",
"#From Weins Law, wlmax*T = consttmax = 2898 micro-m.K\n",
"consttmax = 2898 \t\t\t\t;#micro-m.K\n",
"wlmax = consttmax/T;\n",
"#from Table 12.1 at wlmax = 1.45 micro-m.K and T = 2000 K\n",
"I = .722*stfncnstt*T*T*T*T*T/10000.;\n",
"Eb = math.pi*I;\n",
"\n",
"G = E; #[W/m^2] Irradiation of any small object inside the enclosure is equal to emission from blackbody at enclosure temperature\n",
"#results\n",
"\n",
"print '%s %.2e %s' %(\"\\n (a) Spectral Emissive Power of a small aperture on the enclosure =\",E,\" W/m^2.Sr for each of the three directions\")\n",
"print '%s %.1f %s' %(\"\\n (b) Wavelength below which 10percent of the radiation is concentrated = \",wl1,\" micro-m \\n\") \n",
"print '%s %.2f %s' %(\" Wavelength above which 10percent of the radiation is concentrated = \",wl2,\" micro-m \\n\")\n",
"print '%s %.2e %s %.2e %s' %(\"(c) Spectral emissive power and wavelength associated with maximum emission is \",Eb,\"micro-m and\",wlmax,\" W/m^2.micro-m respectively\")\n",
"print '%s %.2e %s' %(\"\\n (d) Irradiation on a small object inside the enclosure =\",G,\"W/m^2\");\n",
"#END"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" (a) Spectral Emissive Power of a small aperture on the enclosure = 9.07e+05 W/m^2.Sr for each of the three directions\n",
"\n",
" (b) Wavelength below which 10percent of the radiation is concentrated = 1.1 micro-m \n",
"\n",
" Wavelength above which 10percent of the radiation is concentrated = 4.69 micro-m \n",
"\n",
"(c) Spectral emissive power and wavelength associated with maximum emission is 4.12e+05 micro-m and 1.45e+00 W/m^2.micro-m respectively\n",
"\n",
" (d) Irradiation on a small object inside the enclosure = 9.07e+05 W/m^2\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example 12.4 Page 743 "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"\n",
"import math\n",
"import scipy\n",
"from scipy import integrate\n",
"\n",
"\n",
"T = 1500.\t\t\t\t\t\t\t\t;#[K] temperature of surface\n",
"stfncnstt = 5.67*math.pow(10,-8)\t\t;#[W/m^2.K^4] Stefan-Boltzmann constant\n",
"#calculations\n",
"\n",
"#From Equation 12.26 Black Body Radiation\n",
"Eb = stfncnstt*T*T*T*T; \t\t\t#[W/m^2]\n",
"\n",
"#From Table 12.1 as wl1*T = 2*1500 (micro-m.K)\n",
"F02 = .273;\n",
"#From Table 12.1 as wl2*T = 4*1500 (micro-m.K)\n",
"F04 = .738;\n",
"def func(x):\n",
"\tfunc = 2*math.cos(x) *math.sin(x)\n",
"\treturn func;\n",
"\n",
"#From equation 12.10 and 12.11\n",
"i1 = scipy.integrate.quad(func,0,math.pi/3.);\n",
"delE = i1[0] *(F04-F02)*Eb;\n",
"#results\n",
"\n",
"print '%s %.2e %s' %(\"\\n Rate of emission per unit area over all directions between 0 degC and 60 degC and over all wavelengths between wavelengths 2 micro-m and 4 micro-m =\",delE,\" W/m^2\");\n",
"#END"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" Rate of emission per unit area over all directions between 0 degC and 60 degC and over all wavelengths between wavelengths 2 micro-m and 4 micro-m = 1.00e+05 W/m^2\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example 12.5 Page 748"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"import math\n",
"\n",
"\n",
"T = 1600.\t\t\t\t\t\t\t\t;#[K] temperature of surface\n",
"wl1 = 2 \t\t\t\t\t\t;#[micro-m] wavelength 1\n",
"wl2 = 5 \t\t\t\t\t\t;#[micro-m] wavelength 2\n",
"stfncnstt = 5.67*math.pow(10,-8);\t\t#[W/m^2.K^4] Stefan-Boltzmann constant\n",
"# From the given graph of emissivities\n",
"e1 = .4;\n",
"e2 = .8;\n",
"#calculations\n",
"\n",
"#From Equation 12.26 Black Body Radiation\n",
"Eb = stfncnstt*T*T*T*T; \t \t\t#[W/m^2]\n",
"\n",
"#Solution (A)\n",
"#From Table 12.1 as wl1*T = 2*1600 (micro-m.K)\n",
"F02 = .318;\n",
"#From Table 12.1 as wl2*T = 5*1600 (micro-m.K)\n",
"F05 = .856;\n",
"#From Equation 12.36\n",
"e = e1*F02 + e2*(F05 - F02);\n",
"\n",
"#Solution (B)\n",
"#From equation 12.35\n",
"E = e*Eb;\n",
"\n",
"#Solution (C)\n",
"#For maximum condition Using Weins Law\n",
"consttmax = 2898. \t\t\t\t;#[micro-m.K]\n",
"wlmax = consttmax/T;\n",
"\n",
"#equation 12.32 with Table 12.1\n",
"E1 = math.pi*e1*.722*stfncnstt*T*T*T*T*T/10000.;\n",
"\n",
"E2 = math.pi*e2*.706*stfncnstt*T*T*T*T*T/10000.;\n",
"#results\n",
"\n",
"print '%s %.3f' %(\"\\n (a) Total hemispherical emissivity =\",e)\n",
"print '%s %d %s' %(\"\\n (b) Total emissive Power =\",E/1000. ,\" kW/m^2\")\n",
"print '%s %.1f %s %.1f %s ' %(\"\\n (c) Emissive Power at wavelength 2micro-m is greater than Emissive power at maximum wavelength \\n i.e.\",E2/1000.,\" kW/m^2 >\",E1/1000.,\" kW/m^2\")\n",
"print '%s %d %s' %(\"\\n Thus, Peak emission occurs at\",wl1,\"micro-m\");\n",
"#END"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" (a) Total hemispherical emissivity = 0.558\n",
"\n",
" (b) Total emissive Power = 207 kW/m^2\n",
"\n",
" (c) Emissive Power at wavelength 2micro-m is greater than Emissive power at maximum wavelength \n",
" i.e. 105.5 kW/m^2 > 53.9 kW/m^2 \n",
"\n",
" Thus, Peak emission occurs at 2 micro-m\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example 12.6 Page 751"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"\n",
"import math\n",
"import scipy\n",
"from scipy import integrate\n",
"\n",
"T = 2000.\t\t\t\t\t\t\t\t;#[K] temperature of surface\n",
"wl = 1 \t\t\t\t\t\t;#[micro-m] wavelength \n",
"stfncnstt = 5.67*math.pow(10,-8);\t\t#[W/m^2.K^4] Stefan-Boltzmann constant\n",
"\n",
"# From the given graph of emissivities\n",
"e1 = .3;\n",
"e2 = .6;\n",
"#calculations\n",
"\n",
"#From Equation 12.26 Black Body Radiation\n",
"Eb = stfncnstt*T*T*T*T; \t\t\t\t\t\t#[W/m^2]\n",
"def func1(x):\n",
"\tfunc1=e1*math.cos(x) *math.sin(x);\n",
"\treturn func1;\n",
"\n",
"def func2(x):\n",
"\tfunc2=e2*math.cos(x) *math.sin(x);\n",
"\treturn func2;\n",
"\n",
"#Equation 12.34\n",
"i1 = scipy.integrate.quad(func1,0,math.pi/3.);\n",
"i2 = scipy.integrate.quad(func2,math.pi/3. ,4*math.pi/9.);\n",
"e = 2*(i1[0]+i2[0]);\n",
"\n",
"# From Table 12.1 at wl = 1 micro-m and T = 2000 K.\n",
"\n",
"I = .493*math.pow(10,-4) * stfncnstt*T*T*T*T*T ;#[W/m^2.micro-m.sr]\n",
"\n",
"In = e1*I;\n",
"\n",
"#Using Equation 12.32 for wl = 1 micro-m and T = 2000 K\n",
"E = e*math.pi*I;\n",
"#results\n",
"\n",
"print '%s %.1f' %('\\n Spectral Normal emissivity en =',e1)\n",
"print '%s %.2f' %('and spectral hemispherical emissivity e = ',e)\n",
"print '%s %.2e %s' %('\\n Spectral normal intensity In =',In,' W/m^2.micro-m.sr')\n",
"print '%s %.1e %s' % ('and Spectral emissive power =',E,' W/m^2.micro-m.sr ');"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" Spectral Normal emissivity en = 0.3\n",
"and spectral hemispherical emissivity e = 0.36\n",
"\n",
" Spectral normal intensity In = 2.68e+04 W/m^2.micro-m.sr\n",
"and Spectral emissive power = 1.0e+05 W/m^2.micro-m.sr \n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example 12.7 Page 759"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"\n",
"import math\n",
"import matplotlib\n",
"from matplotlib import pyplot\n",
"%pylab inline\n",
"\n",
"\n",
"T = 500.\t\t\t\t\t\t\t\t;#[K] temperature of surface\n",
"e = .8;\n",
"stfncnstt = 5.67*math.pow(10,-8);\t\t#[W/m^2.K^4] Stefan-Boltzmann constant\n",
"#calculations\n",
"\n",
"x=([0, 6, 8, 16]);\n",
"y=([.8, .8, 0, 0]);\n",
"\n",
"pyplot.xlabel(\"Spectral Distribution of reflectivity\")\n",
"pyplot.ylabel(\"wavelength (micro-m)\");\n",
"pyplot.plot(x,y);\n",
"pyplot.show();\n",
"\n",
"\n",
"#From equation 12.43 and 12.44\n",
"Gabs = (.2*500/2.*(6.-2.)+500*(.2*(8.-6.)+(1.-.2)*(8.-6.)/2.)+1*500*(12.-8.)+500*(16.-12.)/2.) ;#[w/m^2]\n",
"G = (500*(6.-2.)/2.+500*(12.-6.)+500*(16.-12.)/2.) \t\t\t\t\t\t\t\t\t;#[w/m^2]\n",
"a = Gabs/G;\n",
"\n",
"#Neglecting convection effects net het flux to the surface\n",
"qnet = a*G - e*stfncnstt*T*T*T*T;\n",
"#results\n",
"\n",
"print '%s %.2f' %('\\n Total, hemispherical absorptivity',a)\n",
"print '%s %.2f %s' %('\\n Nature of surface temperature change =',qnet,' W/m^2 \\n Since qnet > 0, the sirface temperature will increase with the time');"
],
"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": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEPCAYAAABCyrPIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuoYFgYiIALAgEBHRNSwIREQEAPh/X35awWPS9asAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x2fec390>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" Total, hemispherical absorptivity 0.76\n",
"\n",
" Nature of surface temperature change = 965.00 W/m^2 \n",
" Since qnet > 0, the sirface temperature will increase with the time\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example 12.8 Page 761"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"import math\n",
"\n",
"T = 5800.\t\t\t\t\t\t\t\t;#[K] temperature of surface\n",
"e = .8;\n",
"stfncnstt = 5.67*math.pow(10,-8);\t\t#[W/m^2.K^4] Stefan-Boltzmann constant\n",
"#calculations and results\n",
"\n",
"#From Table 12.1\n",
"#For wl1 = .3 micro-m and T = 5800 K, At wl1*T = 1740 micro-m.K\n",
"F0wl1 = .0335;\n",
"#For wl1 = .3 micro-m and T = 5800 K, At wl2*T = 14500 micro-m.K\n",
"F0wl2 = .9664;\n",
"\n",
"#Hence from equation 12.29\n",
"t = .90*(F0wl2 - F0wl1);\n",
"\n",
"print '%s %.2f' %('\\n Total emissivity of cover glass to solar radiation =',t);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" Total emissivity of cover glass to solar radiation = 0.84\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example 12.9 Page 766"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"\n",
"import math\n",
"\n",
"Ts = 500.\t\t\t\t\t\t\t;#[K] temperature of brick surface\n",
"Tc = 2000. \t\t\t\t;#[K] Temperature of coal exposed\n",
"stfncnstt = 5.67*math.pow(10,-8)\t;#[W/m^2.K^4] Stefan-Boltzmann constant\n",
"# From the given graph of emissivities\n",
"e1 = .1; \t\t\t\t\t\t#between wavelength 0 micro-m- 1.5 micro-m\n",
"e2 = .5; \t\t\t\t\t\t#between wavelength 1.5 micro-m- 10 micro-m\n",
"e3 = .8; \t\t\t\t\t\t#greater than wavelength 10 micro-m\n",
"#calculations\n",
"\n",
"#From Table 12.1\n",
"#For wl1 = 1.5 micro-m and T = 500 K, At wl1*T = 750 micro-m.K\n",
"F0wl1 = 0;\n",
"#For wl2 = 10 micro-m and T = 500 K, At wl2*T = 5000 micro-m.K\n",
"F0wl2 = .634;\n",
"#From equation 12.36\n",
"e = e1*F0wl1 + e2*F0wl2 + e3*(1-F0wl1-F0wl2);\n",
"\n",
"#Equation 12.26 and 12.35\n",
"E = e*stfncnstt*Ts*Ts*Ts*Ts;\n",
"\n",
"#From Table 12.1\n",
"#For wl1 = 1.5 micro-m and T = 2000 K, At wl1*T = 3000 micro-m.K\n",
"F0wl1c = 0.273;\n",
"#For wl2 = 10 micro-m and T = 2000 K, At wl2*T = 20000 micro-m.K\n",
"F0wl2c = .986;\n",
"ac = e1*F0wl1c + e2*(F0wl2c-F0wl1c) + e3*(1-F0wl2c);\n",
"#results\n",
"\n",
"print '%s %.3f' %('\\n Total hemispherical emissivity of fire brick wall =',e)\n",
"print '%s %d %s' %('\\n Total emissive power of brick wall =',E,'W/m^2.')\n",
"print '%s %.3f' %('\\n Absorptivity of the wall to irradiation from coals =',ac);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" Total hemispherical emissivity of fire brick wall = 0.610\n",
"\n",
" Total emissive power of brick wall = 2160 W/m^2.\n",
"\n",
" Absorptivity of the wall to irradiation from coals = 0.395\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example 12.10 Page 768"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"import math\n",
"\n",
"Ts = 300.;\t\t\t\t\t\t\t#[K] temperature of surface\n",
"Tf = 1200; \t\t\t\t#[K] Temperature of Furnace\n",
"stfncnstt = 5.67*math.pow(10,-8);\t#[W/m^2.K^4] Stefan-Boltzmann constant\n",
"# From the given graph of absorptivities\n",
"a1 = .8; \t\t\t\t\t\t#between wavelength 0 micro-m- 5 micro-m\n",
"a2 = .1; \t\t\t\t\t\t#greater than wavelength 5 micro-m\n",
"#calculations\n",
"\n",
"#From Table 12.1\n",
"#For wl1 = 5 micro-m and T = 1200 K, At wl1*T = 6000 micro-m.K\n",
"F0wl1 = 0.738;\n",
"#From equation 12.44\n",
"a = a1*F0wl1 + a2*(1-F0wl1);\n",
"#From Table 12.1\n",
"#For wl1 = 5 micro-m and T = 300 K, At wl1*T = 1500 micro-m.K\n",
"F0wl1s = 0.014;\n",
"#From equation 12.36\n",
"e = a1*F0wl1s + a2*(1-F0wl1s);\n",
"#results\n",
"\n",
"print' %s %.2f' %('\\n For Initial Condition \\n Total hemispherical absorptivity = ',a)\n",
"print '%s %.2f' %('Emissivity of sphere =',e)\n",
"print '%s' %('\\n\\n Beacuase the spectral characteristics of the coating and the furnace temeprature remain fixed, there is no change in the value of absorptivity with increasing time.')\n",
"print '%s %d %s %.2f' %('\\n Hence, After a sufficiently long time, Ts = Tf = ',Tf,' K and emissivity equals absorptivity e = a = ',a);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" \n",
" For Initial Condition \n",
" Total hemispherical absorptivity = 0.62\n",
"Emissivity of sphere = 0.11\n",
"\n",
"\n",
" Beacuase the spectral characteristics of the coating and the furnace temeprature remain fixed, there is no change in the value of absorptivity with increasing time.\n",
"\n",
" Hence, After a sufficiently long time, Ts = Tf = 1200 K and emissivity equals absorptivity e = a = 0.62\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example 12.11 Page 774"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"\n",
"import math\n",
"\n",
"Ts = 120+273.;\t\t\t\t\t\t\t#[K] temperature of surface\n",
"Gs = 750; \t\t\t\t#[W/m^2] Solar irradiation\n",
"Tsky = -10+273.; \t\t\t\t#[K] Temperature of Sky\n",
"Tsurr = 30+273.; \t \t\t\t#[K] Temperature os surrounding Air\n",
"e = .1 \t\t \t\t\t;# emissivity \n",
"ast = .95 \t\t\t\t;# Absorptivity of Surface\n",
"asky = e \t\t\t\t;# Absorptivity of Sky\n",
"stfncnstt = 5.67*math.pow(10,-8);\t\t#[W/m^2.K^4] Stefan-Boltzmann constant\n",
"#calculations\n",
"\n",
"h = 0.22*math.pow((Ts - Tsurr),.3334);\t#[W/m^2.K] Convective Heat transfer Coeff\n",
"#From equation 12.67\n",
"Gsky = stfncnstt*Tsky*Tsky*Tsky*Tsky; \t#[W/m^2] Irradiadtion from sky\n",
"qconv = h*(Ts-Tsurr); \t\t\t#[W/m^2] Convective Heat transfer\n",
"E = e*stfncnstt*Ts*Ts*Ts*Ts; \t\t#[W/m^2] Irradiadtion from Surface\n",
"\n",
"#From energy Balance\n",
"q = ast*Gs + asky*Gsky - qconv - E;\n",
"\n",
"#Collector efficiency\n",
"eff = q/Gs;\n",
"#results\n",
"\n",
"print '%s %d %s' %('\\n Useful heat removal rate per unit area by Energy Conservation = ',q,'W/m^2')\n",
"print '%s %.2f' %('\\n Collector efficiency defined as the fraction of solar irradiation extracted as useful energy is',eff);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" Useful heat removal rate per unit area by Energy Conservation = 515 W/m^2\n",
"\n",
" Collector efficiency defined as the fraction of solar irradiation extracted as useful energy is 0.69\n"
]
}
],
"prompt_number": 11
}
],
"metadata": {}
}
]
}
|