Coating thermal diffusivity and effusivity measurement optimization using regression-based sensitivity

Measurements of thermal diffusivity and thermal effusivity are critical to developing a complete description of thermal transport within thermal barrier coating systems. Thermal diffusivity and thermal effusivity of coatings can be measured nondestructively using the phase of photothermal emission analysis experimental measurement. However, the complexity of the regression analysis required in this measurement makes determining the uncertainties associated with the best-fit values nontrivial. The aim of this paper is to develop a framework to carry out this uncertainty analysis and to minimize the uncertainties in fitted parameters. It is shown that the physical model can be used as an effective tool for identifying and removing data points afflicted by excessive bias error, which can occur in the limits of the observational data. It is revealed that this reduction in the dataset offers a tradeoff between increasing agreement between the data and the model while reducing the uniqueness of fitted parameter values. The current analysis demonstrates that this situation can be treated as an optimization problem, whereby uncertainties in fitted parameters can be minimized.