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266 Marco Manzan, Enrico Nobile, Stefano Pieri and Francesco Pinto 53. Steuer, R.E.: Multiple Criteria Optimization: Theory, Computation and Application. John Wiley, New York (1986) 54. Wei, X., Joshi, Y.: Optimization study of stacked micro-channel heat sinks for microelectronic cooling. In: IEEE Trans. Compon., Packag. and Manufact. Technol., vol. 26, pp. 55–61 (2003)
Chapter 9 CFD-based Optimization for a Complete Industrial Process: Papermaking Jari Hämäläinen, Taija Hämäläinen, Elina Madetoja and Henri Ruotsalainen Abstract Development of tailored software tools based on coupling of Computational Fluid Dynamics (CFD) with optimization is presented in this paper. In papermaking, industrial applications deal with fluid dynamics at the wet-end of a paper machine as well as in the entire papermaking process. First, the CFD tools being developed for optimal shape design and optimal control problems at the wet-end (where the paper web is formed) are presented. Different levels of complexity of CFD modeling and a dimension reduction technique are considered in this paper. The reduced CFD model used is validated with a complete model. In addition, optimization of the whole papermaking process being modeled with different modeling techniques is considered. Our approach is based on interactive multi-objective optimization because the papermaking process as well as the produced paper require multiple criteria to be optimized simultaneously. Typically, the objectives are conflicting which means that compromises need to be done. This is illustrated with numerical examples. Finally, a completely new design of decision support tools based on multiobjective optimization and multiphysical modeling of large industrial systems is discussed. 9.1 Introduction Optimal shape design is the best-known example of model-based optimization in which the model is described as a system of partial differential equations. In a sense, optimal shape design is an inverse problem, that is, the Jari Hämäläinen · Taija Hämäläinen · Elina Madetoja · Henri Ruotsalainen Department of Physics, University of Kuopio, P.O.Box 1627, FI-70211 Kuopio, Finland (e-mail: jari.hamalainen@uku.fi,taija.hamalainen@uku.fi,elina.madetoja@uku.fi, henri.ruotsalainen@uku.fi) 267
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Optimization and Computational Flui
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Editors: Prof. Dr.-Ing. Dominique T
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vi Preface Our first research proje
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viii Contents 2.4.1 GoverningEquati
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x Contents 6.2.3 Parameterization..
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xii Contents 9.4.1 Multi-objectiveO
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xiv List of Contributors Gábor JAN
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Part I Generalities and methods
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4 Dominique Thévenin 12 10 8 6 4 2
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6 Dominique Thévenin Objective 10
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8 Dominique Thévenin Objective 10
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10 Dominique Thévenin Parameter 2
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12 Dominique Thévenin three-dimens
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14 Dominique Thévenin Let us come
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16 Dominique Thévenin References 1
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18 Gábor Janiga 2.1 Introduction D
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20 Gábor Janiga y Tinlet = 293 K 0
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22 Gábor Janiga y [mm] 25 20 15 10
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24 Gábor Janiga Table 2.1 Number o
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26 Gábor Janiga A dominates the in
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28 Gábor Janiga be shown later, th
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30 Gábor Janiga INPUT FILE Gambit
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34 Gábor Janiga ∆T ∆P Position
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36 Gábor Janiga ∆P [mPa] 2.2 2.0
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38 Gábor Janiga Table 2.3 Speed-up
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40 Gábor Janiga For all computatio
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42 Gábor Janiga y [mm] 4 2 0 -1 0
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44 Gábor Janiga Air mass flow-rate
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46 Gábor Janiga Temperature variat
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50 Gábor Janiga The modified k-ω
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52 Gábor Janiga Error for Re∗ =
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54 Gábor Janiga Error for Re∗ 20
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56 Gábor Janiga References 1. Ali,
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58 Gábor Janiga 43. Janiga, G., Th
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Chapter 3 Mathematical Aspects of C
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3 Mathematical Aspects of CFD-based
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Chapter 4 Adjoint Methods for Shape
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4 Adjoint Methods for Shape Optimiz
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Part II Specific Applications of CF
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112 Nicolas R. Gauger Nomenclature
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114 Nicolas R. Gauger Fig. 5.1 Cost
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116 Nicolas R. Gauger and the four
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118 Nicolas R. Gauger CL := 1 � C
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120 Nicolas R. Gauger the cost func
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122 Nicolas R. Gauger Table 5.1 An
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124 Nicolas R. Gauger The main adva
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126 Nicolas R. Gauger Spalart-Allma
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128 Nicolas R. Gauger (a) (b) Fig.
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134 Nicolas R. Gauger Fig. 5.10 Plo
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136 Nicolas R. Gauger drag, lift, s
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138 Nicolas R. Gauger solution of t
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140 Nicolas R. Gauger the presence
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142 Nicolas R. Gauger Fig. 5.16 Con
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144 Nicolas R. Gauger optimization
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Chapter 6 Numerical Optimization fo
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6 Numerical Optimization for Advanc
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192 Laurent Dumas 7.1 Introducing A
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194 Laurent Dumas C Fig. 7.2 Wake f
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196 Laurent Dumas Table 7.1 Example
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198 Laurent Dumas Fig. 7.5 Numerica
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200 Laurent Dumas 7.3.1.1 Genetic A
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202 Laurent Dumas START Random init
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204 Laurent Dumas • if ˜ J(x ng
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206 Laurent Dumas Table 7.3 Compari
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208 Laurent Dumas Fig. 7.9 General
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210 Laurent Dumas Table 7.4 Four ex
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212 Laurent Dumas Fig. 7.13 Blades
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214 Laurent Dumas Table 7.5 Converg
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