Layered porous media architecture for maximal cooling

Wildi-Tremblay, Philippe; Gosselin, Louis

doi:10.1016/j.ijheatmasstransfer.2006.07.031

Cited by 21 publications

(23 citation statements)

References 34 publications

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“…GAs are optimization tools relying on Darwinian evolution principles, and have been used successfully for the design of several heat transfer and energy systems. In particular, they have been able to determine optimal materials in many systems, e.g., [18][19][20][21]. We will not explain here the details of this algorithm that is becoming more and more common in the domain.…”

Section: Optimization Procedures (Genetic Algorithms)mentioning

confidence: 99%

“…Again, note that the GA may generate thick PCM slabs by placing several adjacent PCM layers in the wall. Considering 6 melting temperatures (i.e., T m ¼ 20, 21,22,23,24, and 25 C for Orlando, and 0, 4, 8, 12, 16, 20 C for Qué bec City), the total number of possible materials is 8 for each layer. There are thus 2 60 possible wall configurations.…”

Section: Wall Optimization Based On Idealized Seasonal Weather Scenariosmentioning

confidence: 99%

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Thermal shielding of multilayer walls with phase change materials under different transient boundary conditions

Mathieu-Potvin

Gosselin

2009

International Journal of Thermal Sciences

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Section: Optimization Procedures (Genetic Algorithms)mentioning

confidence: 99%

Section: Wall Optimization Based On Idealized Seasonal Weather Scenariosmentioning

confidence: 99%

Thermal shielding of multilayer walls with phase change materials under different transient boundary conditions

Mathieu-Potvin

Gosselin

2009

International Journal of Thermal Sciences

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“…Further, others have coupled porous media models with GAs to optimize heat sink designs [9][10][11].…”

Section: Others Have Interfaced Direct Numerical Simulations (Dns) Andmentioning

confidence: 99%

“…Equation (9) calculates the particle's new velocity according to its weighted previous velocity and stochastic functions of the distance of its current position from its own best position and that of the group.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Nonlocal Modeling and Swarm-Based Design of Heat Sinks

Geb¹,

Catton

2013

Journal of Heat Transfer

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Cooling electronic chips to satisfy the ever-increasing heat transfer demands of the electronics industry is a perpetual challenge. One approach to addressing this is through improving the heat rejection ability of air-cooled heat sinks, and nonlocal thermal-fluid-solid modeling based on Volume Averaging Thoery (VAT) has allowed for significant strides in this effort. A number of optimization methods for heat sink designers who model heat sinks with VAT can be envisioned due to VAT's singular ability to rapidly provide solutions, when compared to Direct Numerical Simulation (DNS) and Computational Fluid Dynamics (CFD)approaches. The Particle Swarm Optimization (PSO) method appears to be an attractive multiparameter heat transfer device optimization tool, however it has received very little attention in this field compared to its older population-based optimizer cousin, the Genetic Algorithm (GA).The PSO method is employed here to optimize smooth and scale-roughened straight-fin heat sinks modeled with VAT by minimizing heat sink thermal resistance for a specified pumping power. Optimal designs are obtained with the PSO method for both types of heat sinks, the performances of the heat sink types are compared, and the performance of the PSO method is discussed with reference to the GA method. This study demonstrates the effectiveness of combining a VAT-based nonlocal thermal-fluid-solid model with population-based optimization methods, such as PSO, to design heat sinks for electronics cooling applications.Nonlocal Modeling and Swarm-Based Design of Heat Sinks 2Geb, HT-xx-xxxx

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“…In the region of heat transfer optimization, there are also some successful instances. Based on the local thermal equilibrium model, Wildi-Tremblay and Gosselin [5] studied on structure optimization of a stack of porous layers through which a coolant flows, the optimized factors included the composition, porosity of each layer, total weight and cost of the entire porous structure. Ozkol [6] showed shape optimizations of mono-and two-dimensional structure obtained by layer number e effective a new GA which was improved to exhibit a good convergence toward the optimal solution.…”

Section: Introductionmentioning

confidence: 99%