Effective heat distribution in electronic circuitry is essential to improve the performance and life of electronic components such as chips. This study presents a numerical analysis of heat transfer on a substrate board populated with an array of discrete heat sources, assumed to be placed in a horizontal air channel for forced convection cooling. The analysis of electronic packages is performed, taking into consideration the effect of thermal contact conductance (TCC) between the heat source (chip) and the substrate board. The dependence of the temperature distribution on the Reynolds number of air at the inlet and the heating power from the heat source is investigated for inlet velocities ranging from 0.6 to 1.4 m/s and observed to be significant. Temperature and heat transfer coefficient are observed to systematically increase with the increase in the heat dissipation from the heat source. Two configurations—inline and staggered—are analyzed, with the staggered configuration showing superior cooling performance. This improvement is attributed to the fact that staggered arrangements expose fewer heat sources to pre‐heated air before it exits the system. Additionally, the location of the heat source reaching the highest temperature is found to be highly dependent on the TCC of the bonding material between the heat source and the substrate. A hybrid optimization strategy is employed, by combining Artificial Neural Network (ANN) and Genetic Algorithm (GA) for optimizing the location of heat sources. ANN is used for predicting the temperature distribution, subsequently followed by GA to minimize the maximum temperature attained by the heat generating source by varying other control variables like TCC thickness, inlet velocity, and heat generation. The thickness of the bonding layer is varied from 0.225 to 0.271 mm and the heat generation is varied from 1000 to 2000 W/m2. Among them, TCC is observed to be an important parameter controlling the optimum location of heat generating sources. The results obtained from the proposed hybrid optimization strategy are compared with the simulation results and observed to be reasonably close.
