Contact resistance analysis applied to simultaneous estimation of thermal properties of metals

Abstract

This work aims to present a method for simultaneously estimating the thermal conductivity, k, and specific heat, c p , in samples of stainless steel 304 and cemented carbide by accounting the imperfect thermal contact at the heater-sample interface. A transient one-dimensional heat conduction model with constant thermal properties is used. The metallic sample is placed in the middle of a polyimide heater and a thermal insulator. A constant heat flux is applied on the specimen upside surface and a thermal insulation condition is maintained on the opposing surface, where a type T thermocouple measures the temperature response. Contact resistance is determined and accounted for as a reducing factor on heat flux. Instead of considering a lumped model, a microscopic approach of the contacting regions is used to describe both the surface roughness and the fluid gap. With the intention of guaranteeing simultaneous and reliable estimates for both thermal properties, sensitivity analysis is used to establish heat flux intensity, experiment duration, time step for data acquisition, and other characteristics of the experiments. To simultaneously achieve both thermal properties, the optimization method BFGS (Broyden-Fletcher-Goldfarb-Shanno) is employed to minimize a least-squares objective function comparing experientially measured and numerically calculated temperatures. The numerical solution for the transient conduction problem is determined with COMSOL, which solves the transient heat diffusion problem by discretizing it applying the finite element method (FEM). The linear system of equations is solved using the PARDISO solver and backward differentiation formula (BDF) method. Heat flux is estimated employing the sequential function specification method (SFSM) as a means of verifying the robustness of parameter estimation and experimental procedure. Moreover, uncertainty analysis is performed to confirm the quality of the results obtained. Finally, the uncertainty analysis outcomes and thermal properties estimated are compared with literature.

Improved Bayesian Optimization Framework for Inverse Thermal Conductivity Based on Transient Plane Source Method

“…In this study, only a single-parameter inversion of thermal conductivity was performed. Under this model, the transient average temperature of the probe decreases with the increasing thermal conductivity of the specimen when the experimental conditions are the same [ 57 ]. Therefore, the range evaluation function is defined as follows: …”

Complementary transient thermal models and metaheuristics to simultaneously identify linearly temperature-dependent thermal properties of austenitic stainless steels

“…R c causes a decrease in the amount of heat conducted to the metallic sample, i.e., it represents a reducing factor in the heat flux load (q), supplied by the resistive heater. Further details on the methodology used to assess the thermal contact can be found in Reference [33].…”

“…Embedded thermocouple wires need that some filler material to be introduced into a milled hole, which causes a discontinuity effect [51]. In this context, only the bottom surface can be explored to collect temperature data with wire thermocouples in the 1D thermal model [33,36,43]. Thus, data from the 1D experiment was measured at the bottom surface, i.e., at location x=L.…”

“…Considerable efforts and expertise are needed when designing experiments for thermal characterization. However, it is difficult and expensive to design and carry out experiments for determining the thermal properties of materials, especially metals, in part due to contact resistance effects and thermal sensitivity deficiencies when dealing with these materials [32,33]. Approaches for analyzing metallic materials are somewhat limited; there are not many approaches that can be used in these materials [34,35].…”

“…Approaches for analyzing metallic materials are somewhat limited; there are not many approaches that can be used in these materials [34,35]. Other methods do not consider thermal properties as a function of temperature [33,36,37], which is a relevant issue for metals because they are commonly applied in temperature-varying applications. Some do not simultaneously identify thermal parameters, analyzing only one property, or needing additional steps to determine other parameters [38].…”