Thermal conductivity prediction of Al<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si5.svg" display="inline" id="d1e761"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math>O<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si6.svg" display="inline" id="d1e769"><mml:msub><mml:mrow/><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub></mml:math>-doped tetragonal YSZ coatings using deep learning-Reference-Cited by-同舟云学术

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Thermal conductivity prediction of Al2O3-doped tetragonal YSZ coatings using deep learning

Published:2024-09 Issue:12 Volume:44 Page:7254-7264
ISSN:0955-2219
Container-title:Journal of the European Ceramic Society
language:en
Short-container-title:Journal of the European Ceramic Society

Author:

Chen Qiaochuan^ORCID,Han Sifan,Song Xuemei,Zeng Yi,Han Yuexing^ORCID

Publisher

Elsevier BV

Reference39 articles.

1. Thermally sprayed coating systems for surface protection and clearance control applications in aero engines

2. Electronic-structure methods for materials design

3. Mesoscopic and multiscale modelling in materials

4. Materials informatics: From the atomic-level to the continuum

5. Microstructure–mechanical properties correlation in spark plasma sintered Ti–4.8 wt.% TiB2 composites

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