Nonlocal Modeling and Swarm-Based Design of Heat Sinks

Abstract

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 theory (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 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. A new numerical design tool incorporates the PSO method with a VAT-based heat sink solver. Optimal designs are obtained with this new tool for both types of heat sinks, the performances of the heat sink types are compared, the performance of the PSO method is discussed with reference to the GA method, and it is observed that this new method yields optimal designs much quicker than traditional approaches. This study demonstrates, for the first time, 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. The VAT-based nonlocal modeling method provides heat sink design capabilities, in terms of solution speed and model rigor, that existing modeling methods do not match.