Optimization of an “area to point” heat conduction problem

Guo, Kai; Qi, Wenzhe; Liu, Botan; Liu, Chunjiang; Huang, Zheqing; Zhu, Guang-ming

doi:10.1016/j.applthermaleng.2015.09.061

Cited by 22 publications

(7 citation statements)

References 39 publications

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“…An overwhelming majority of the designs from the intensive study on the volumeto-point problem are hence tree-like, independent of whether the design approaches are targeted at tree structures or not. Remarks like "it can be seen that the design results resemble natural tree networks" can be found in related literature [1,9,12,19,22,24], implying that tree structures have been accepted as the optimal topologies of volume-to-point structures. However, the study in this paper shows that lamellar needle-like structures instead of tree structures are the true optimal topologies for volume-to-point structures in the context of steady-state heat conduction for minimum thermal compliance and minimum maximum temperature, respectively.…”

Section: Introductionmentioning

confidence: 93%

“…In the extensive literature on this problem, different parameters are used as the metric for the heat conduction performance and different design approaches are proposed, as summarized in Table 1. [18] Entropy generation TO Temperature gradientbased principle [19] T max , T av TO Genetic algorithm [20,21] Temperature gradient difference TO Cellular automaton [22] T av Generative approach Growth algorithm [23] Generative approach Space colonization algorithm and genetic algorithm [24] T max Generative approach Genetic algorithm [25] Tmax : maximum temperature, Tav : average temperature, C th : thermal compliance, Ein : interface energy, TO: topology optimization, SIMP: Solid Isotropic Material with Penalization model, SSM: Stiffness Spreading Method.…”

Section: Introductionmentioning

confidence: 99%

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On the non-optimality of tree structures for heat conduction

Yan

Wang

Sigmund

2018

International Journal of Heat and Mass Transfer

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

On the non-optimality of tree structures for heat conduction

Yan

Wang

Sigmund

2018

International Journal of Heat and Mass Transfer

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“…However, design of a specific fin on the basis of the minimum entropy generation [17] results in different configurations compared to those obtained by the classical optimization methods [16]. This is also true when one is applying the concept of entropy generation to optimize the temperature field in electronic devices [18]. These types of thermodynamic analyses, i.e., the entropy generation and exergy analyses of a system, have been interestingly further extended to exergetic analysis of the human body [19] [20].…”

Section: Introductionmentioning

confidence: 99%

Entropy generation in thermal systems with solid structures – A concise review

Torabi

Zhang

Karimi

et al. 2016

International Journal of Heat and Mass Transfer

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Analysis of thermal systems on the basis of the second law of thermodynamics has recently gained considerable attention. This is, in part, due to the fact that this approach along with the powerful tools of entropy generation and exergy destruction provides a unique method for the analysis of a variety of systems encountered in science and engineering. Further, in recent years there has been a surge of interest in the thermal analysis of conductive media which include solid structures. In this work, the recent advances in the second law analyses of these systems are reviewed with an emphasis on the theoretical and modelling aspects. The effects of including solid components on the entropy generation within different thermal systems are first discussed. The mathematical methods used in this branch of thermodynamics are, then, reviewed. This is followed by the conclusions regarding the existing challenges and opportunities for further research.

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“…Various constructal designs have been derived for volume (area)‐to‐point problems with different constraints, boundary conditions, element shapes, and optimization objectives. [ 3–11 ] Many algorithms, eg, the solid isotropic with material penalization (SIMP) method, [ 12–18 ] rational approximation of material properties method, [ 19 ] evolutionary structural optimization method, [ 20,21 ] level‐set method, [ 22–24 ] cellular automaton algorithm, [ 25,26 ] deep learning approach, [ 27 ] genetic algorithm (GA), [ 28,29 ] simulated annealing (SA), [ 28,30 ] PSO, [ 31 ] bionic optimization, [ 30,32–41 ] and calculus of variations, [ 42,43 ] have been applied to the field of volume (area)‐to‐point heat conduction.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective optimization of area‐to‐point heat conduction structure using binary quantum‐behaved PSO and Tchebycheff decomposition method

et al. 2020

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A multiobjective optimization of area‐to‐point heat conduction to minimize both mean temperature and temperature variance is conducted based on a decomposition‐based multiobjective binary quantum‐behaved particle swarm optimization (PSO) method (MOMBQPSO/D). The MOMBQPSO/D adopts the framework of the multiobjective evolutionary algorithm based on decomposition and modifies the binary quantum‐behaved PSO. In the first step of the MOMBQPSO/D, the multiobjective area‐to‐point problem is divided into a series of subproblems using Tchebycheff decomposition method. Next, all the subproblems are solved simultaneously using the modified binary quantum‐behaved PSO. Finally, a series of Pareto optimal solutions representing the conducting path structures are stepwise selected from the solutions to the subproblems. The features of the Pareto optimality‐based conducting paths and cooling performance are described. In addition, the effects of the conductive material quantity, optimization objective, heat sink location, and heat source distribution on the conducting path structure and cooling performance are discussed.

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Optimization of an “area to point” heat conduction problem

Cited by 22 publications

References 39 publications

On the non-optimality of tree structures for heat conduction

On the non-optimality of tree structures for heat conduction

Entropy generation in thermal systems with solid structures – A concise review

Multiobjective optimization of area‐to‐point heat conduction structure using binary quantum‐behaved PSO and Tchebycheff decomposition method

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