Heat source layout optimization in two-dimensional heat conduction using simulated annealing method

Chen, Kai; Xing, Jianwei; Wang, Shuangfeng; Song, Mengxuan

doi:10.1016/j.ijheatmasstransfer.2016.12.007

Cited by 46 publications

(26 citation statements)

References 58 publications

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“…In this section, the mathematical model for the heat conduction optimization problem in a two-dimensional rectangular domain is revised. As previously stated by Chen et al [18][19][20], the temperature field (T) satisfies the following equation at steady state @ @x k @T @x þ @ @y k @T @y þ /ðx; yÞ ¼ 0…”

Section: Mathematical Modeling Of the Problemmentioning

confidence: 91%

“…In this section, the parameters for the layout optimization problem are provided. Since the convex optimization algorithm is proposed as an alternative to the existing methods, the scenarios and design parameters applied by Chen et al [18][19][20] are taken as a reference in the first three cases. In the last case, a Volume-to-Volume (VV) problem is introduced, which has not been investigated before in the existing layout optimization techniques.…”

Section: Parameters Of the Casesmentioning

confidence: 99%

“…Next, the results of the first three cases are compared with the results of the existing layout optimization methods, namely bionic optimization (BO) [18] and simulated annealing (SA) [20]. In Figs.…”

Section: Tablementioning

confidence: 99%

“…For layout optimization problems with large numbers of degrees of freedom, combinatorial algorithms such as genetic algorithm with artificial neural network [13][14][15][16] and particle swarm optimization [17] have been used in the literature. In their recent studies, Chen et al applied bionic optimization [18,19] and simulated annealing [20] methods to find out the optimal source distribution for varying number (up to several tens) of heat sources, which provided significant reduction in the maximum tempera-ture of the domain when compared to the randomly or uniformly distributed heat sources.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by this analogy, the mathematical modeling of the two-dimensional heat conduction problem is performed and the positions of the heat sources are optimized using the convex optimization algorithm iteratively. The performance is tested using three typical cases with various boundary conditions that were previously applied by Chen et al [18][19][20] and an additional case which takes into account the heat emission from the domain surface by convection or radiation while solving the conduction problem.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Heat source layout optimization for two-dimensional heat conduction using iterative reweighted L1-norm convex minimization

Aslan

Puskely

Yarovoy

2018

International Journal of Heat and Mass Transfer

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Optimization of heat source distribution in two dimensional heat conduction for electronic cooling problem is considered. Convex optimization is applied to this problem for the first time by reformulating the objective function and the non-convex constraints. Mathematical analysis is performed to describe the heat source equation and the combinatorial optimization problem. A sparsity based convex optimization technique is used to solve the problem approximately. The performance of the algorithm is tested by several cases with various boundary conditions and the results are compared with a uniformly distributed layout. These results indicate that through proper selection of the number of grid cells for placing the heat sources and a minimum inter-source spacing, the maximum temperature and temperature nonuniformity in the domain can be significantly reduced. To further assess the capabilities of the method, comparisons to the results available in the literature are also performed. Compared to the existing heat source layout optimization methods, the proposed algorithm can be implemented more easily using available convex programming tools and reduces the number of input control parameters and thus computation time and resources while achieving a similar or better cooling performance.

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Section: Mathematical Modeling Of the Problemmentioning

confidence: 91%

Section: Parameters Of the Casesmentioning

confidence: 99%