Evaluation of molten sand, dust, and ash infiltrating thermal barrier coatings: Numerical and analytical approaches

物理 热障涂层 热的 机械 复合材料 热力学 图层(电子) 材料科学
作者
Brendon Cavainolo,Ravisankar Naraparaju,Mohammad Rizviul Kabir,Michael Kinzel
出处
期刊:Physics of Fluids [American Institute of Physics]
卷期号:36 (11)
标识
DOI:10.1063/5.0234882
摘要

Calcium-Magnesium-Alumino-Silicate (CMAS) is a category of atmospheric debris in the form of dirt, sand, and ash that damage thermal barrier coatings (TBC) in aircraft engines. The damage is not a direct result of erosion, but rather, CMAS melts in engines and impacts the TBCs. In this state, the CMAS can infiltrate the TBC microstructure which leads to surface damage from secondary stresses associated with thermal loading and expansion in the microstructure. Understanding the fluid dynamic processes of the infiltration is key to develop TBCs that mitigate TBC infiltration damage. The fluidic processes are evaluated using microstructure-resolving, finite-volume, multiphase, volume-of-fluid computational fluid dynamics simulations (CFD). CFD results using experimentally measured temperature-dependent polynomial CMAS viscosity are compared to experiments and analytical models and indicate that feathery-shaped microstructure in TBCs inhibit CMAS infiltration more than rectangular channel TBCs. Such observations are conditional on the Ohnesorge number (Oh). For low Oh values, the rectangular channel reduces infiltration, while the feathery channel is more effective at reducing infiltration for higher Oh values. Three-dimensional CFD results under-predicted experimental and theoretical infiltration depth. A novel infiltration model for feathery channels, the “Feathery Pipe-Network Model” (FPNM) was implemented. FPNM results agree with experiments and other analytical models. Using FPNM in conjunction with the concentric-pipe model achieves a 25% margin-of-error when evaluated against experimental results. This is a 15% reduction in error compared to using the open-pipe and concentric-pipe models as the prediction. This enhanced prediction model can lead to safer and more cost-effective aircraft operation in debris-laden environments.

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