Optimization of Cylindrical Pin-Fin Heat Sinks Using Genetic Algorithms

In this paper, genetic algorithms (GAs) are applied for the optimization of pin-fin heat sinks. GAs are usually considered as a computational method to obtain optimal solution in a very large solution space. Entropy generation rate due to heat transfer and pressure drop across pin-fins is minimized by using GAs. Analytical/empirical correlations for heat transfer coefficients and friction factors are used in the optimization model, where the characteristic length is used as the diameter of the pin and reference velocity used in Reynolds number and pressure drop is based on the minimum free area available for the fluid flow. Both inline and staggered arrangements are studied and their relative performance is compared on the basis of equal overall volume of heat sinks. It is demonstrated that geometric parameters, material properties, and flow conditions can be simultaneously optimized using GA.