Optimization and Computational Fluid Dynamics - Department of ...

More documents

Recommendations

Info

16 Dominique Thévenin References 1. Bejan, A.: Entropy Generation Minimization. CRC Press, Boca Raton, Florida (1996) 2. Capasso, V., Périaux, J.: Multidisciplinary Methods for Analysis Optimization and Control of Complex Systems. Mathematics in Industry. Springer (2005) 3. Fletcher, R.: Practical Methods of Optimization, 2 edn. John Wiley & Sons, New York (1987) 4. Gunzburguer, M.D.: Perspectives in Flow Control and Optimization. SIAM, Philadelphia (2003) 5. Mohammadi, B., Pironneau, O.: Applied Shape Optimization for Fluids. Numerical Mathematics and Scientific Computation. Oxford University Press (2001) 6. Nelder, J.A., Mead, R.: A simplex method for function minimization. Computer Journal 7, 308–313 (1965)
Chapter 2 A Few Illustrative Examples of CFD-based Optimization Heat Exchanger, Laminar Burner and Turbulence Modeling Gábor Janiga Abstract In this chapter, several multi-objective design optimizations are performed in order to illustrate major issues associated with CFD-based optimization. First, a heat exchanger configuration (Case A) is considered using the coupled solution of the flow/heat transfer processes. The aim of the procedure is to find the positions of the tubes most favorable to simultaneously maximize heat exchange while obtaining a minimum pressure loss. Next, the optimization of the flame shape of a laminar burner is investigated when varying the fuel/air ratio in a primary and a secondary inlet (Case B). The objectives are to reduce the CO emission at a prescribed distance from the injection plane and to obtain the most homogeneous temperature profile at the same position. The flow involving chemical reactions is solved using the in-house Computational Fluid Dynamics (CFD) code UGC + .These two cases are the continuation of our previous studies, introducing new results and new aspects. The last case presented here is a new proposal to optimize the model parameters of an engineering turbulence model (Case C). In all the presented cases, an Evolutionary Algorithm (EA) is applied to find the optimal configurations. An in-house computer package, called Opal, performs the optimization process in a fully automatic manner. The EA relies on a relatively large number of simulations which may result in a considerable computational effort, depending on the configuration. The procedure can thus be performed in parallel on a Linux PC-cluster to reduce user waiting time. Gábor Janiga Lab. of Fluid Dynamics and Technical Flows, University of Magdeburg “Otto von Guericke”, Germany (e-mail: janiga@ovgu.de) 17
Page 1 and 2: Optimization and Computational Flui
Page 3 and 4: Editors: Prof. Dr.-Ing. Dominique T
Page 5 and 6: vi Preface Our first research proje
Page 7 and 8: viii Contents 2.4.1 GoverningEquati
Page 9 and 10: x Contents 6.2.3 Parameterization..
Page 11 and 12: xii Contents 9.4.1 Multi-objectiveO
Page 13 and 14: xiv List of Contributors Gábor JAN
Page 15: Part I Generalities and methods
Page 18 and 19: 4 Dominique Thévenin 12 10 8 6 4 2
Page 20 and 21: 6 Dominique Thévenin Objective 10
Page 22 and 23: 8 Dominique Thévenin Objective 10
Page 24 and 25: 10 Dominique Thévenin Parameter 2
Page 26 and 27: 12 Dominique Thévenin three-dimens
Page 28 and 29: 14 Dominique Thévenin Let us come
Page 32 and 33: 18 Gábor Janiga 2.1 Introduction D
Page 34 and 35: 20 Gábor Janiga y Tinlet = 293 K 0
Page 36 and 37: 22 Gábor Janiga y [mm] 25 20 15 10
Page 38 and 39: 24 Gábor Janiga Table 2.1 Number o
Page 40 and 41: 26 Gábor Janiga A dominates the in
Page 42 and 43: 28 Gábor Janiga be shown later, th
Page 44 and 45: 30 Gábor Janiga INPUT FILE Gambit
Page 48 and 49: 34 Gábor Janiga ∆T ∆P Position
Page 50 and 51: 36 Gábor Janiga ∆P [mPa] 2.2 2.0
Page 52 and 53: 38 Gábor Janiga Table 2.3 Speed-up
Page 54 and 55: 40 Gábor Janiga For all computatio
Page 56 and 57: 42 Gábor Janiga y [mm] 4 2 0 -1 0
Page 58 and 59: 44 Gábor Janiga Air mass flow-rate
Page 60 and 61: 46 Gábor Janiga Temperature variat
Page 64 and 65: 50 Gábor Janiga The modified k-ω
Page 66 and 67: 52 Gábor Janiga Error for Re∗ =
Page 68 and 69: 54 Gábor Janiga Error for Re∗ 20
Page 70 and 71: 56 Gábor Janiga References 1. Ali,
Page 72 and 73: 58 Gábor Janiga 43. Janiga, G., Th
Page 75 and 76: Chapter 3 Mathematical Aspects of C
Page 77 and 78: 3 Mathematical Aspects of CFD-based
Page 79 and 80: 3 Mathematical Aspects of CFD-based
Page 81 and 82:
3 Mathematical Aspects of CFD-based
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
Page 93 and 94:
Chapter 4 Adjoint Methods for Shape
Page 95 and 96:
4 Adjoint Methods for Shape Optimiz
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
Page 117 and 118:
Page 119 and 120:
Page 121 and 122:
Page 123:
Part II Specific Applications of CF
Page 126 and 127:
112 Nicolas R. Gauger Nomenclature
Page 128 and 129:
114 Nicolas R. Gauger Fig. 5.1 Cost
Page 130 and 131:
116 Nicolas R. Gauger and the four
Page 132 and 133:
118 Nicolas R. Gauger CL := 1 � C
Page 134 and 135:
120 Nicolas R. Gauger the cost func
Page 136 and 137:
122 Nicolas R. Gauger Table 5.1 An
Page 138 and 139:
124 Nicolas R. Gauger The main adva
Page 140 and 141:
126 Nicolas R. Gauger Spalart-Allma
Page 142 and 143:
128 Nicolas R. Gauger (a) (b) Fig.
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
134 Nicolas R. Gauger Fig. 5.10 Plo
Page 150 and 151:
136 Nicolas R. Gauger drag, lift, s
Page 152 and 153:
138 Nicolas R. Gauger solution of t
Page 154 and 155:
140 Nicolas R. Gauger the presence
Page 156 and 157:
142 Nicolas R. Gauger Fig. 5.16 Con
Page 158 and 159:
144 Nicolas R. Gauger optimization
Page 161 and 162:
Chapter 6 Numerical Optimization fo
Page 163 and 164:
6 Numerical Optimization for Advanc
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
Page 173 and 174:
Page 175 and 176:
Page 177 and 178:
Page 179 and 180:
Page 181 and 182:
Page 183 and 184:
Page 185 and 186:
Page 187 and 188:
Page 189 and 190:
Page 191 and 192:
Page 193 and 194:
Page 195 and 196:
Page 197 and 198:
Page 199 and 200:
Page 201 and 202:
Page 203:
Page 206 and 207:
192 Laurent Dumas 7.1 Introducing A
Page 208 and 209:
194 Laurent Dumas C Fig. 7.2 Wake f
Page 210 and 211:
196 Laurent Dumas Table 7.1 Example
Page 212 and 213:
198 Laurent Dumas Fig. 7.5 Numerica
Page 214 and 215:
200 Laurent Dumas 7.3.1.1 Genetic A
Page 216 and 217:
202 Laurent Dumas START Random init
Page 218 and 219:
204 Laurent Dumas • if ˜ J(x ng
Page 220 and 221:
206 Laurent Dumas Table 7.3 Compari
Page 222 and 223:
208 Laurent Dumas Fig. 7.9 General
Page 224 and 225:
210 Laurent Dumas Table 7.4 Four ex
Page 226 and 227:
212 Laurent Dumas Fig. 7.13 Blades
Page 228 and 229:
214 Laurent Dumas Table 7.5 Converg
Page 231 and 232:
Chapter 8 Multi-objective Optimizat
Page 233 and 234:
8 Multi-objective Optimization in C
Page 235 and 236:
Page 237 and 238:
8 Multi-objective Optimizatio Fig.
Page 239 and 240:
Page 241 and 242:
Page 243 and 244:
Page 245 and 246:
Page 247 and 248:
Page 249 and 250:
Page 251 and 252:
Page 253 and 254:
Page 255 and 256:
Page 257 and 258:
Page 259 and 260:
Page 261 and 262:
Page 263 and 264:
Page 265 and 266:
Page 267 and 268:
Page 269 and 270:
Page 271 and 272:
Page 273 and 274:
Page 275 and 276:
Page 277 and 278:
Page 279 and 280:
Page 281 and 282:
Chapter 9 CFD-based Optimization fo
Page 283 and 284:
9 CFD-based optimization for paperm
Page 285 and 286:
Page 287 and 288:
Page 289 and 290:
Page 291 and 292:
Page 293 and 294:
Page 295 and 296:
Page 297 and 298:
Page 299 and 300:
Page 301 and 302:
Page 303:
Page 306 and 307:
292 Index heat exchanger, 222, 224,
show all

Optimization and Computational Fluid Dynamics - Department of ...

Create successful ePaper yourself

Delete template?

Save as template?