Multiobjective Evolutionary Optimization of Splayed Pin-Fin Heat Sink

Abstract

AbstractThis paper presents the use of a multiobjective evolutionary algorithm, namely population-based incremental learning (PBIL), for the design/optimization of a splayed pin-fin heat sink. An innovative design strategy based on evolutionary optimization is presented for the enhancement of the heat sink's performance. The design problem is to simultaneously minimize the junction temperature and the fan pumping power of the heat sink. Design variables determine the sizes and geometry of the heat sink. The new encoding/decoding process of the design variables, using surface spline interpolation, is detailed. There are 24 design parameters including fin number, fin height, fin width, heat sink base thickness and size, fin bevel, and inlet air velocity. Manufacturing tolerances, such as the heat sink aspect ratio and the device free space, are taken as design constraints. Computational fluid dynamics is used for the objective function evaluation. Numerical results show that PBIL is a powerful tool for the optimal design of a splayed pin-fin heat sink. This new design approach results in heat sinks with variation in fin heights, and it is said to be superior to available commercial splayed pin-fin heat sinks.Keywords: splayed pin-fin heat sinkmultiobjective evolutionary algorithmscomputational fluid dynamicspopulation-based incremental learningthermal performance