Deep learning assisting construction of heat transfer constitutive relationships for porous media

Abstract

Predicting heat transfer in porous materials is challenging due to their complex microstructure, and traditional experimental and theoretical methods often fall short. To address this, we present a machine learning-based approach using a single Descriptor-to-Property Network (D2P-Net) to identify the relationships between the effective thermal conductivity (ETC) of porous media with the structural descriptors. The D2P-Net, an ensemble of decision trees, uses eleven structural descriptors, such as porosity, pore size, and connectivity, derived from four distinct types of three-dimensional porous structures. These structures are computationally generated, and their ETC is computed using the Lattice Boltzmann Method (LBM) to create a robust dataset for training. The model is trained using mean squared error (MSE) employed as the loss function, and achieves an R2 value of 0.994 and a root mean square error (RMSE) of 0.229, indicating high predictive accuracy. Additionally, SHapley Additive exPlanations (SHAP) values are employed to analyze the importance of each structural descriptor, offering valuable insights into their contributions to ETC predictions. This approach provides a reliable and interpretable method for understanding and optimizing the thermal properties of porous materials, with potential applications in areas requiring efficient thermal management, such as hypersonic vehicles.