Thickness Prediction of Deposited Thermal Barrier Coatings Using Ray Tracing and Heat Transfer Methods
Abstract
Thermal barrier coatings (TBCs) have been extensively employed as thermal protection in hot sections of gas turbines in aerospace and power generation applications. However, the fabrication of TBCs still needs to improve for better coating quality, such as achieving coating thickness' uniformity. However, several previous studies on the coating thickness prediction and a systematic understanding of the thickness evolution during the deposition process are still missing. This study aims to develop high-fidelity computational models to predict the coating thickness on complex-shaped components. In this work, two types of models, i.e., ray-tracing based and heat transfer based, are developed. For the ray-tracing model, assuming a line-of-sight coating process and considering the shadow effect, validation studies of coating thickness predictions on different shapes, including plate, disc, cylinder, and three-pin components. For the heat transfer model, a heat source following the Gaussian distribution is applied. It has the analogy of the governing equations of the ray-tracing method, thus generating a temperature distribution similar to the ray intensity distribution in the ray-tracing method, with the advantages of high computational efficiency. Then, using a calibrated conversion process, the ray intensity or the temperature profile are converted to the corresponding coating thickness. After validation studies, both models are applied to simulate the coating thickness in a rotary turbine blade. The results show that the simulated validation cases are in good agreement with either the experimental, analytical, or modeling results in the literature. The turbine blade case shows the coating thickness distributions based on rotating speed and deposition time. In summary, the models can simulate the coating thickness in rotary complex-shaped parts, which can be used to design and optimize the coating deposition process.
Degree
M.Sc.
Advisors
Yang, Purdue University.
Subject Area
Marketing|Mathematics|Mechanical engineering|Optics|Thermodynamics
Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server.