Optimization of the thermophysical properties of the thermal barrier coating materials based on GA-SVR machine learning method: illustrated with ZrO₂doped DyTaO₄system

Abstract

It is a critical issue to reduce the thermal conductivity and increase the thermal expansion coefficient of ceramic thermal barrier coating (TBC) materials in the course of their utilization. To synthesize samples with different composition and measure their thermal conductivity by the traditional experimental approaches is time-consuming and expensive. Most classic and empirical models work inefficiently and inaccurately when researchers attempt to predict the thermophysical properties of TBC materials. In this research project, we tentatively exploit a Genetic Algorithm-Support Vector Regression (GA-SVR) machine learning model to study the thermophysical properties, illustrated with the potential TBC materials ZrO2 doped DyTaO4, which has resulted in the lowest thermal conductivity in rare earth tantalates RETaO4 system. Meanwhile, we employ statistical parameters of correlation coefficient (R2) and mean square error (MSE) to evaluate the accuracy and reliability of the model. The results reveal that this model has brought about high correlation coefficients of thermal conductivity and thermal expansion coefficient (99.8% and 99.9%, respectively), while the MSE values are 0.00052 and 0.00019, respectively. The doping concentration of ZrO2 was optimized to reach as low as 0.085-0.095, so as to reduce their thermal conductivity further and increase their thermal expansion. This model provides an accurate and reliable option for researchers to design ceramic thermal barrier coating materials.

Deep learning based analysis of microstructured materials for thermal radiation control

“…Inspired by the biology and architecture of the human brain, the DL methodology is capable of high levels of non-linear abstraction from datasets 31 . DL and Machine Learning (ML) have been used, in a broad setting, to solve complex problems ranging from machine vision for self-driving vehicles 32 to automatic speech recognition 33 and spacecraft system optimization 34 – 37 . In the field of optics, DL has been used recently to predict and model plasmonic behavior 31 , 38 – 42 , grating structures 43 , 44 , ceramic metasurfaces 45 , 46 , chiral materials 47 , 48 , particles and nanosturctures 49 – 51 , and to do inverse design 31 , 41 , 50 – 54 .…”