Investigation of High-Temperature Normal Infrared Spectral Emissivity of ZrO2 Thermal Barrier Coating Artefacts by the Modified Integrated Blackbody Method

Zirconium oxide (ZrO2) is widely used as the thermal barrier coating in turbines and engines. Accurate emissivity measurement of ZrO2 coating at high temperatures, especially above 1000 °C, plays a vital role in thermal modelling and radiation thermometry. However, it is an extremely challenging enterprise, and very few high temperature emissivity results with rigorously estimated uncertainties have been published to date. The key issue for accurately measuring the high temperature emissivity is maintaining a hot surface without reflection from the hot environment, and avoiding passive or active oxidation of material, which will modify the emissivity. In this paper, a novel modified integrated blackbody method is reported to measure the high temperature normal spectral emissivity of ZrO2 coating in the temperature range 1000 °C to 1200 °C and spectral range 8 μm to 14 μm. The results and the associated uncertainty of the measurement were estimated and a relative standard uncertainty better than 7% (k = 2) is achieved.

SHOCK-WAVE COMPACTING OF POWDER EP741NP MODIFIED THERMAL BARRIER COATING

The use of modified powders with a nanofilm of metal oxides surface allow plasticizing the EP741NP alloy granules, and the use of shock-wave compacting to obtain the high-strength compacts with a thermal barrier coating for the manufacture in charge products in aviation and space engineering.

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

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.