A machine learning strategy for modeling and optimal design of near-field radiative heat transfer

Abstract

The recent decade has witnessed the advent of near-field radiative heat transfer (NFRHT) in a wide range of applications, including thermal photovoltaics and thermal diodes. However, the design process for these thermal devices has remained complex, often relying on the intuition and expertise of the designer. To address these challenges, a machine learning (ML) strategy based on the combination of an artificial neural network (ANN) and a genetic algorithm (GA) is presented. The ANN is trained to model representative scenarios, viz., NFRHT between metamaterials and NFRHT and thermal rectification between nanoparticles. The influence of different problem complexities, i.e., the number of input variables of function to be fitted, on effectiveness of the trained ANN is investigated. Test results show that ANNs can obtain the radiative heat flow and rectification ratio accurately and rapidly. Subsequently, physical parameters for the largest radiative heat flow and rectification ratio are determined by the utilization of GA on the trained ANN, and underlying mechanisms of deterministic optimum are discussed. Our work shows that data-driven ML methods are a powerful tool, which offers unprecedented opportunities for future NFRHT research.