Probabilistic Modelling and Validation of Thermal Contact Conductance between Rough Metallic Surfaces

Abstract

In many high-tech applications, efficient thermal dissipation is necessary to maintain components within strict operational temperature limits, ensuring system reliability and performance stability. This is achieved mainly through thermal conduction, which is the dominant mechanism between contacting surfaces. Thermal contact conduction is a function of many variables, such as contact area, material properties, and surface roughness characteristics. Various studies have focused on how these variables affect the heat flow between two contacting geometries; however, the inherent variability of the surface roughness induces a significant amount of uncertainty in the measurements. This paper describes the theory and methodology followed to develop a thermo-mechanical framework that incorporates the inherent stochasticity of the surface roughness of metallic surfaces, through a probabilistic surface representation, to efficiently quantify the heat flow conductance through the calculation of the Thermal Contact Conductance (TCC). The results indicate that the proposed methodology predicts the range of thermal contact conductance with good accuracy when compared to experimental data. Lastly, the 2D implementation showed substantially higher computational efficiency compared to the 3D model while retaining comparable predictive capability.