Uncertainties in Aerothermodynamics of Aero-engine

Robust optimization method based on adaptive sparse polynomial chaos

  • Zhendong GUO ,
  • Haojie LI ,
  • Liming SONG ,
  • Hualiang ZHANG ,
  • Zhao YIN
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  • 1.School of Energy and Power,Xi’an Jiaotong University,Xi’an 710049,China
    2.Institute of Engineering Thermophysics,Chinese Academy of Sciences,Beijing 100190,China

Received date: 2024-02-01

  Revised date: 2024-02-21

  Accepted date: 2024-03-15

  Online published: 2024-03-25

Supported by

National Science and Technology Major Project(2019-II-0008-0028);National Natural Science Foundation of China(52306048)

Abstract

Traditional polynomial chaos is faced with “curse of dimensionality”, and relies on practical tasks and experience to artificially determine the order of orthogonal polynomial expansion. In this paper, an Uncertainty Quantification (UQ) method based on Adaptive Sparse Polynomial Chaos (ASPC) is developed by using relevance vector machine regression to solve the sparse solution of the expansion coefficient and combining with the cross-validation method. The results of the functional example test show that the proposed method requires fewer samples, and is more accurate than the traditional regression method of polynomial chaos. In addition, a Robust Design Optimization (RDO) framework is established by combining the UQ method based on ASPC, the NSGA-II algorithm and the Kriging model. Considering the influence of manufacturing error uncertainty, the aerodynamic RDO of a power turbine cascade is completed with the objective functions of minimising the mean and the variance of the total pressure loss coefficient of cascade. The optimization results in a reduction in the mean of total pressure loss coefficient and a significant reduction in the degree of sensitivity to manufacturing error uncertainty. The mean and variance of the two representative optimized design individuals decrease by 16.41% and 98.57%, respectively, and the total pressure loss coefficient decreases by 13.43% and 2.82%, respectively. Finally, the flow field is analyzed, and the reasons for improved aerodynamic performance of the optimized design are revealed.

Cite this article

Zhendong GUO , Haojie LI , Liming SONG , Hualiang ZHANG , Zhao YIN . Robust optimization method based on adaptive sparse polynomial chaos[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2024 , 45(19) : 630273 -630273 . DOI: 10.7527/S1000-6893.2024.30273

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