4.7 Article

An inverse optimization of convection heat transfer in rectangle channels with ribbed surface based on the extremum principle of entransy dissipation

Journal

INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
Volume 130, Issue -, Pages 722-732

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijheatmasstransfer.2018.10.079

Keywords

Inverse optimization problem; Convection heat transfer; Ribbed surface; Entransy dissipation

Funding

  1. National Natural Science Foundation of China [51576059]
  2. Natural Science Foundation of Hebei Province in China [E2015202272]
  3. Science and Technology Development Fund Project of Higher Education of Tianjin in China [20120421]

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The ribbed surface is a widely used convection heat transfer enhancement technology. The geometric parameters of the ribbed surface have great influences on its heat transfer performance. It is crucial to determine the optimum geometric parameters. In the present work, the optimal arrangement of ribs mounted in a rectangular channel is provided by solving the inverse optimization problem with the entransy dissipation as the objective function. The parameter analysis based on the direct solution is firstly carried out to get an approximate range near the optimal pitch ratio which can provide the initial value for the inverse optimization problem; afterwards, the inverse optimization problem is solved by a modified simplified conjugate gradient method, and the accurate optimal pitch ratio is obtained; finally the optimization results are discussed in detail. The results indicate that, (1) the inverse optimization analysis presented in this paper can provide an accurate optimal value of the pitch ratio; (2) taking the entransy dissipation as the objective function could lead to a faster converge process and give a more stable result than taking the temperature data as the objective function; (3) the optimal value under the fixed heat flux boundary condition is slightly larger than that under the fixed temperature boundary condition. (C) 2018 Elsevier Ltd. All rights reserved.

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