Artificial neural network (ANN)-based multi-objective optimization of the vapor chamber with liquid supply layer for high heat flux applications

We developed a multi-objective optimization process using artificial neural networks (ANN) to estimate and enhance the thermal-hydraulic performance of vapor chambers (VCs). A numerical model was employed to evaluate the impact of various components on VC performance, and the resulting data were used to train the ANN model. This approach led to an optimized VC design that significantly reduced thermal resistance while substantially improving critical heat flux (CHF). This improvement was primarily due to the distinct roles of the liquid supply layer (LSL) and the evaporator wick. The optimized VC with liquid supply layer (VC-LSL) exhibited a thermal resistance 1/3 lower and a junction temperature 100 degrees C lower than those of the optimized VC without liquid supply layer (VC-NL) at heat flux of 500 W/cm2. This work demonstrates significant potential for maximizing heat transfer performance by establishing an optimal VC design adaptable to a wide range of heat fluxes.