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Multidisciplinary sensitivity analysis for turbine blade considering thickness uncertainties

  • Fan Yang , Chunyu Zhang , Wenjing Gao and Lei Li EMAIL logo
Published/Copyright: December 22, 2022
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Abstract

This work presents an approach for sensitivity analysis of turbine cooling blade with surface thickness uncertainties, combining mesh deformation method, neural network model and multidisciplinary analysis. Normally, for even tiny shape changes, conventional geometry-based method failed easily during the auto-processing analysis. Therefore, mesh deformation method was utilized to capture the tiny size changes in the multidisciplinary analysis for both the fluid and the structure meshes. The neural network model is constructed by design of experiments to reduce the computational cost. Sensitivity analysis of the multidisciplinary system of blade is performed by numerical difference algorithm with the neural network model. Results showed that the proposed method was effective and practical in engineering.


Corresponding author: Lei Li, Department of Engineering Mechanics, Northwestern Polytechnincal University, Xi’an 710072, China, E-mail:

Funding source: Natural Science Foundation of Jiangsu Province

Award Identifier / Grant number: BK20190424

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 51975471

Funding source: Natural Science Foundation of Shanghai

Award Identifier / Grant number: 21ZR1469300

Funding source: Shaanxi Science Foundation for Distinguished Young Scholars

Award Identifier / Grant number: 2022JC-36

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This work was supported by the Natural Science Foundation of Jiangsu Province (BK20190424), National Natural Science Foundation of China (51975471), Natural Science Foundation of Shanghai (21ZR1469300) and Shaanxi Science Foundation for Distinguished Young Scholars (2022JC-36).

  3. Declaration of conflicting interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Received: 2022-07-06
Accepted: 2022-12-08
Published Online: 2022-12-22

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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