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Imprecise P-Box Sensitivity Analysis of an Aero-Engine Combustor Performance Simulation Model Considering Correlated Variables

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  • Hongjie Tang

    (School of Aerospace Engineering, Xiamen University, Xiamen 361000, China)

  • Shicheng Zhang

    (School of Aerospace Engineering, Xiamen University, Xiamen 361000, China)

  • Jinhui Li

    (School of Aerospace Engineering, Xiamen University, Xiamen 361000, China)

  • Lingwei Kong

    (School of Aerospace Engineering, Xiamen University, Xiamen 361000, China)

  • Baoqiang Zhang

    (School of Aerospace Engineering, Xiamen University, Xiamen 361000, China)

  • Fei Xing

    (School of Aerospace Engineering, Xiamen University, Xiamen 361000, China)

  • Huageng Luo

    (School of Aerospace Engineering, Xiamen University, Xiamen 361000, China)

Abstract

Uncertainties are widely present in the design and simulation of aero-engine combustion systems. Common non-probabilistic convex models are only capable of processing independent or correlated uncertainty variables, while conventional precise probabilistic sensitivity analysis based on ideal conditions also fails due to the presence of uncertainties. Given the above-described problem, an imprecise p-box sensitivity analysis method is proposed in this study in accordance with a multi-dimensional parallelepiped model, comprising independent and correlated variables in a unified framework to effectively address complex hybrid uncertainty problems where the two variables co-exist. The concepts of the correlation angle and correlation coefficient of any two parameters are defined. A multi-dimensional parallelepiped model is built as the uncertainty domain based on the marginal intervals and correlation characteristics of all parameters. The correlated variables in the initial parameter space are converted into independent variables in the affine space by introducing an affine coordinate system. Significant and minor variables are filtered out through imprecise sensitivity analysis using pinching methods based on p-box characterization. The feasibility and accuracy of the method are verified based on the analysis of the numerical example and the outlet temperature distribution factor. As indicated by the results, the coupling between the variables can be significantly characterized using a multi-dimensional parallelepiped model, and a notable difference exists in the sensitivity ranking compared with considering only the independence of the variables, in which input parameters (e.g., inlet and outlet pressure, density, and reference flow rate) are highly sensitive to changes in the outlet temperature distribution factor. Furthermore, the structural parameters of the flame cylinder exert a secondary effect.

Suggested Citation

  • Hongjie Tang & Shicheng Zhang & Jinhui Li & Lingwei Kong & Baoqiang Zhang & Fei Xing & Huageng Luo, 2023. "Imprecise P-Box Sensitivity Analysis of an Aero-Engine Combustor Performance Simulation Model Considering Correlated Variables," Energies, MDPI, vol. 16(5), pages 1-22, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2362-:d:1084712
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    References listed on IDEAS

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    1. Liu, H.B. & Jiang, C. & Jia, X.Y. & Long, X.Y. & Zhang, Z. & Guan, F.J., 2018. "A new uncertainty propagation method for problems with parameterized probability-boxes," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 64-73.
    2. Majidi, M. & Mohammadi-Ivatloo, B. & Soroudi, A., 2019. "Application of information gap decision theory in practical energy problems: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 157-165.
    3. Schöbi, Roland & Sudret, Bruno, 2019. "Global sensitivity analysis in the context of imprecise probabilities (p-boxes) using sparse polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 129-141.
    4. Peng Guan & Yan-Ting Ai & Cheng-Wei Fei, 2019. "An Enhanced Flow-Thermo-Structural Modeling and Validation for the Integrated Analysis of a Film Cooling Nozzle Guide Vane," Energies, MDPI, vol. 12(14), pages 1-20, July.
    5. Ghazanfar Mehdi & Sara Bonuso & Maria Grazia De Giorgi, 2021. "Effects of Nanosecond Repetitively Pulsed Discharges Timing for Aeroengines Ignition at Low Temperature Conditions by Needle-Ring Plasma Actuator," Energies, MDPI, vol. 14(18), pages 1-19, September.
    6. Jianping Zhou & Feilong Song & Shida Xu & Xingkui Yang & Yongjun Zheng, 2022. "Investigation of Rotating Detonation Fueled by Liquid Kerosene," Energies, MDPI, vol. 15(12), pages 1-16, June.
    7. Ilya Molchanov & Anja Mühlemann, 2021. "Nonlinear expectations of random sets," Finance and Stochastics, Springer, vol. 25(1), pages 5-41, January.
    8. Ferson, Scott & Troy Tucker, W., 2006. "Sensitivity analysis using probability bounding," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1435-1442.
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