Layout Design of Conductive Heat Channel by Emulating Natural Branch Systems

Ji, Yidong; Ding, Xuanming; Li, Hao; Xiong, Min

doi:10.1007/s42235-018-0047-3

Cited by 8 publications

(1 citation statement)

References 24 publications

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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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