Exploratory machine learning strategies for predicting thermal conductivity of materials from transient plane source measurement

Abstract

This study introduces the application of machine learning to the Hot Disk Transient Plane Source (TPS) method, aimed at enhancing the precision and efficiency of thermal conductivity prediction. Comprising two distinct parts, Part I, the research addresses the prediction of thermal conductivity in low-density/high-insulation materials. Part II is the thermal conductivity measurement under high-temperature conditions with noise. Four prediction algorithms were systematically applied and assessed for accuracy to predict thermal conductivities. Experimental data obtained through the TPS method served as the basis for machine learning training data, augmented with simulated data to make up for insufficient data. The outcomes of this study provide a conclusive response to a critical research question: Can machine learning accurately predict thermal conductivity from transient curves? In Part I, machine learning consistently and accurately predicts thermal conductivity for low-density/high-insulation materials devoid of CL values, underscoring its complementary utility. In Part II, machine learning demonstrates its proficiency in accurately predicting thermal conductivity, even in noisy transient curves at extreme temperatures. However, challenges stemming from insufficient data issues and the absence of reference points introduce variability in accuracy.