ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Improved PCE model with coupled double-layer parameter updating for uncertainty analysis in fuel centrifugal pump
Received date: 2024-01-09
Revised date: 2024-02-01
Accepted date: 2024-03-20
Online published: 2024-03-29
Supported by
National Natural Science Foundation of China(52372396);National Science and Technology Major Project of China(J2019-V-0016-0111);Science Center for Gas Turbine Project(P2022-B-V-003-001);Defense Industrial Technology Development Program(JCKY2022607C002);AECC Industry University Cooperation Project(HFZL2022CXY013);Key R&D University Joint Key Project of Shaanxi Province(2021GXLH-01-16)
Blade manufacturing and extreme operating conditions in aircraft engines introduce uncertainty factors that significantly impact the performance and flow field variability of fuel centrifugal pumps. This paper proposes a synchronized analysis method based on CFD mechanistic models and surrogate models. Firstly, based on the Karhuben-Loève (KL) transform theory, uncertainty modeling of three-dimensional blade profile errors in the centrifugal pump was conducted. Secondly, nesting the Least Angle Regression (LAR) algorithm, we applied a double-layer parameter update to the Polynomial Chaos Expansion (PCE) model, constructing a high-precision surrogate model. Finally, employing a specific type of fuel centrifugal pump as the research object, we accomplished uncertainty analysis of the centrifugal pump based on CFD simulations, experimental verification, and the PCE surrogate model. The research demonstrates that the KL transform efficiently describes uncertainty in the three-dimensional blade profile error using only 9 input parameters. The improved PCE model with coupled double-layer parameter updating exhibits an average increase of 27.6% in accuracy metrics across multiple centrifugal pump conditions compared to the unimproved model. Controlling the blade profile error within -0.3 to 0.3 mm at the hub significantly reduces uncertainty in centrifugal pump performance. The blade profile errors at the hub have a greater impact on flow field uncertainty than those at the shroud and midsection, while speed is a crucial operational parameter affecting uncertainty in the fuel pump flow field.
Jiangfeng FU , Shijie ZHONG , Xianwei LIU , Pengfei WEI , Hanting HUANG . Improved PCE model with coupled double-layer parameter updating for uncertainty analysis in fuel centrifugal pump[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2024 , 45(21) : 130126 -130126 . DOI: 10.7527/S1000-6893.2024.30126
1 | 张绍基. 航空发动机控制系统的研发与展望[J]. 航空动力学报, 2004, 19(3): 375-382. |
ZHANG S J. A review of aeroengine control system[J]. Journal of Aerospace Power, 2004, 19(3): 375-382 (in Chinese). | |
2 | 符江锋, 赵志杰, 李华聪, 等. 航空燃油离心泵参数化设计方法研究综述[J]. 推进技术, 2021, 23(3): 189-192. |
FU J F, ZHAO Z J, Li H C, et al. Review on parametric design technology of aviation fuel centrifugal pump[J]. Journal of Propulsion Technology, 2021, 23(3): 189-192 (in Chinese). | |
3 | DILLON J L, MARCUM D C Jr, JOHNSTON P J, et al. Aerodynamic and inlet flow characteristics of several hypersonic airbreathing missile concepts[J]. Journal of Aircraft, 1981, 18(4): 231-237. |
4 | 赵伟国, 强欢欢, 李兴国. 环形喷嘴宽度对高速诱导轮空化特性的影响[J]. 航空学报, 2024, 45(4): 128730. |
ZHAO W G, QIANG H H, LI X G. Effect of annular nozzle breadth on cavitation characteristics of high-speed inducer[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(4): 128730 (in Chinese). | |
5 | LIU X W, FU J F, YANG J J, et al. Numerical simulation research on multiphase flow of aviation centrifugal pump based on OpenFOAM[J]. Chinese Journal of Aeronautics, 2024, 37(4): 256-275. |
6 | FU J F, LIU X W, YANG J J, et al. Optimization of cavitation characteristics of aviation fuel centrifugal pump inducer based on surrogate model[J]. Structural and Multidisciplinary Optimization, 2023, 66(11): 241. |
7 | WANG X J, DU P C, YAO L C, et al. Uncertainty analysis of measured geometric variations in turbine blades and impact on aerodynamic performance[J]. Chinese Journal of Aeronautics, 2023, 36(6): 140-160. |
8 | 姬田园,楚武利,张振华, 等. 叶片厚度偏差对转子性能影响的不确定性分析[J/OL]. 航空动力学报, (2023-09-04)[2024-01-09]. . |
JI T Y, CHU W L, ZHANG Z H, et al. Uncertainty analysis of impact of blade thickness deviation on rotor performance[J/OL]. Journal of Aerospace Power,(2023-09-04)[2024-01-09]. (in Chinese). | |
9 | 罗佳奇, 陈泽帅, 曾先. 考虑几何设计参数不确定性影响的涡轮叶栅稳健性气动设计优化[J]. 航空学报, 2020, 41(10): 172-184. |
LUO J Q, CHEN Z S, ZENG X. Robust aerodynamic design optimization of turbine cascades considering uncertainty of geometric design parameters[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(10): 172-184 (in Chinese). | |
10 | 陈艺夫, 马宇航, 蓝庆生, 等. 基于多项式混沌法的翼型不确定性分析及梯度优化设计[J]. 航空学报, 2023, 44(8): 67-88. |
CHEN Y F, MA Y H, LAN Q S, et al. Uncertainty analysis and gradient optimization design of airfoil based on polynomial chaos expansion method[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(8): 67-88 (in Chinese). | |
11 | KARIMI M S, SALEHI S, RAISEE M, et al. Probabilistic CFD computations of gas turbine vane under uncertain operational conditions[J]. Applied Thermal Engineering, 2019, 148: 754-767. |
12 | WANG Q Q, CHEN H, HU R, et al. Conditional sampling and experiment design for quantifying manufacturing error of transonic airfoil[C]∥ 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Reston: AIAA, 2011. |
13 | KUMAR D, RAISEE M, LACOR C. An efficient non-intrusive reduced basis model for high dimensional stochastic problems in CFD[J]. Computers & Fluids, 2016, 138: 67-82. |
14 | LIU D S, LITVINENKO A, SCHILLINGS C, et al. Quantification of airfoil geometry-induced aerodynamic uncertainties—Comparison of approaches[J]. ASA Journal on Uncertainty Quantification, 2017, 5(1): 334-352. |
15 | 黄松, 王鹏, 汪洋冰. 压气机叶片几何气动性能优化设计方法综述[J]. 推进技术, 2024, 45(4): 6-24. |
HUANG S, WANG P, WANG Y B, et al. Review of optimization design methods for compressor blade geometry and aerodynamic performance[J]. Journal of Propulsion Technology, 2024, 45(4): 6-24 (in Chinese). | |
16 | 夏立, 邹早建, 王子豪, 等. 应用多项式混沌法求解不确定度量化问题初步研究[C]∥中国力学大会论文集(CCTAM 2019). 北京: 中国力学学会, 2019: 1128-1133. |
XIA L, ZHOU Z J, WANG Z H, et al. Preliminary research on solving uncertainty quantification problems using polynomial chaos method[C]∥CCTAM 2019. Beijing: Chinese Society of Theoretical and Applied Mechanics, 2019: 1128-1133 (in Chinese). | |
17 | BLATMAN G, SUDRET B. Adaptive sparse polynomial chaos expansion based on least angle regression[J]. Journal of Computational Physics, 2011, 230(6): 2345-2367. |
18 | 罗佳奇, 陈泽帅, 邹正平, 等. 低压涡轮铸造叶片几何不确定性统计[J]. 航空学报, 2023, 44(6): 305-320. |
LUO J Q, CHEN Z S, ZOU Z P, et al. Statistics on geometric uncertainties of casting blades in low-pressure turbines[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(6): 305-320 (in Chinese). | |
19 | 中国航空工业总公司. 叶片叶型的标注、公差与叶身表面粗糙度: [S]. 北京:中国航空工业总公司,1998. |
Aviation Industry Corporation of China. Marking,tolerances and surface roughness of the leaf blade: ?[S]. Beijing: Aviation Industry Corporation of China, 1998 (in Chinese). | |
20 | KIM N H, WANG H Y, QUEIPO N V. Efficient shape optimization under uncertainty using polynomial chaos expansions and local sensitivities[J]. AIAA Journal, 2006, 44(5): 1112-1116. |
21 | EFRON B, HASTIE T, JOHNSTONE I, et al. Least angle regression[J]. The Annals of Statistics, 2004, 32(2): 407-499. |
22 | WEI P F, LU Z Z, SONG J W. Variable importance analysis: A comprehensive review[J]. Reliability Engineering & System Safety, 2015, 142: 399-432. |
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