scholarly journals High-entropy pyrochlores with low thermal conductivity for thermal barrier coating materials

2019 ◽  
Vol 8 (4) ◽  
pp. 576-582 ◽  
Author(s):  
Fei Li ◽  
Lin Zhou ◽  
Ji-Xuan Liu ◽  
Yongcheng Liang ◽  
Guo-Jun Zhang
Keyword(s):  
Thermal Conductivity ◽  
Thermal Barrier Coating ◽  
Thermal Barrier ◽  
Coating Materials ◽  
Low Thermal Conductivity ◽  
High Entropy ◽  
Barrier Coating

Low-Thermal-Conductivity Rare-Earth Zirconates for Potential Thermal-Barrier-Coating Applications

2004 ◽  
Vol 85 (12) ◽  
pp. 3031-3035 ◽  
Author(s):  
Jie Wu ◽  
Xuezheng Wei ◽  
Nitin P. Padture ◽  
Paul G. Klemens ◽  
Maurice Gell ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Rare Earth ◽  
Thermal Barrier Coating ◽  
Thermal Barrier ◽  
Low Thermal Conductivity ◽  
Barrier Coating

Low-Thermal-Conductivity Rare-Earth Zirconates for Potential Thermal-Barrier-Coating Applications.

ChemInform ◽  
2003 ◽  
Vol 34 (10) ◽  
Author(s):  
Jie Wu ◽  
Xuezheng Wei ◽  
Nitin P. Padture ◽  
Paul G. Klemens ◽  
Maurice Gell ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Rare Earth ◽  
Thermal Barrier Coating ◽  
Thermal Barrier ◽  
Low Thermal Conductivity ◽  
Barrier Coating

scholarly journals Optimization of the thermophysical properties of the thermal barrier coating materials based on GA-SVR machine learning method: illustrated with ZrO2 doped DyTaO4 system

2021 ◽  
Author(s):  
Zhu Wen Jie ◽  
Mengdi Gan ◽  
Bo Ye ◽  
Xin Xiong ◽  
Feng Jing ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thermal Conductivity ◽  
Thermal Expansion ◽  
Thermal Expansion Coefficient ◽  
Thermal Barrier Coating ◽  
Expansion Coefficient ◽  
Thermophysical Properties ◽  
Thermal Barrier ◽  
Coating Materials ◽  
Barrier Coating

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.

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