Optimal Thermal Energy Storage Heatsinks for Communication Devices

A thermal response model for designing optimal thermal energy storage (TES) heatsink utilized for transient electronic cooling is developed by combining Artificial Neural Network (ANN) with Genetic Algorithms (GA). The thermal response model is developed to produce the desired TES heatsink configuration such as fin thickness, fin height and number of fins that yields optimal quality factors namely longest stabilization time and lowest maximum operating temperature to average transition temperature difference. Numerical simulations carried out showed good agreement with TES experimental data available in literature. The data from numerical simulation is then used to train ANN and later the trained ANN is embedded in GA to produce the optimal TES configurations