Multidisciplinary sensitivity analysis for turbine blade considering thickness uncertainties

Fan Yang, Chunyu Zhang, Wenjing Gao, Lei Li

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
Pages (from-to)S597-S606
JournalInternational Journal of Turbo and Jet Engines
Volume40
Issue numbers1
DOIs
StatePublished - 1 Dec 2023

Keywords

  • cooling turbine blade
  • mesh deformation
  • multidisciplinary sensitivity analysis
  • neural network
  • shape uncertainties

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