Abstract

Thermal radiation represents a significant portion of the thermal heat transfer from the hot gases of an engine to its metallic components. Thermal barrier coatings (TBCs) are used to protect those components from heat and must, therefore, be effective blockers of radiative heat. The porous microstructure of TBCs causes them to be highly reflective in the visible and near-infrared wavelength ranges through the scattering of light. This microstructure, however, is susceptible to degradation. In this study, we establish a clear link between the porosity of a TBC and its ability to reflect heat through radiation scattering by extracting the scattering coefficient of two coatings with different microstructures and quantifying their pore space. Using deep learning trained image segmentation models on high resolution SEM cross-sections, we identify the proportion of the pore space comprised of pores 2 μm in diameter or smaller and show that these are responsible for most of the scattering. The pore size distribution is also confirmed with mercury infiltration porosimetry. The coatings are then subjected to cyclic heat treatments at 1450 K for a total of 1111 h to induce sintering of the microstructure. This results in a reduction of the scattering coefficient by around 20 % for both samples, which is attributed to a reduction in the space occupied by pores 2 μm and less in diameter. Finally, a model is built for finite-difference time-domain (FDTD) simulations using high resolution SEM cross-sections to calculate the reflectivity and transmission of the TBCs. The results exhibit good agreement with our experimental data, showing that such models could be used in the future to predict the effect of degradation on a TBC's optical properties.

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