Cross-cut heatsink optimization design by applying Conditional Generative Adversarial Network

Heatsinks have significantly evolved due to demands for larger, cost-effective, and longer-lasting power electronics. Traditional simulation methods for predicting temperatures are time-consuming, requiring complex computations. Artificial Intelligence (AI), particularly Conditional Generative Adversarial Networks (CGaNet), offers a faster, more accurate alternative. CGaNet demonstrated superior temperature prediction accuracy on various datasets, including those outside its training range. The initial training involved a dataset from a cross-cut heatsink, including parameters like pin dimensions, input power, and fan speed. CGaNet predictions for an external dataset showed high conformity to actual data, with the coeffient of determination (R
²
) of 0.941, surpassing the Feed-forward Neural Network (FNN) in extrapolation ability.