Optimization Design of Aerodynamic Performances of Aircraft Engine Fan Blade Profiles Based on Data Driven Methods

SONG Yuanfeng; JIN Yuanhang; TAO Jun

doi:10.21656/1000-0887.460084

Volume 47 Issue 5

May 2026

Turn off MathJax

Article Contents

Article Navigation > Applied Mathematics and Mechanics > 2026 > 47(5): 605-620

SONG Yuanfeng, JIN Yuanhang, TAO Jun. Optimization Design of Aerodynamic Performances of Aircraft Engine Fan Blade Profiles Based on Data Driven Methods[J]. Applied Mathematics and Mechanics, 2026, 47(5): 605-620. doi: 10.21656/1000-0887.460084

Citation:

PDF( 7176 KB)

Optimization Design of Aerodynamic Performances of Aircraft Engine Fan Blade Profiles Based on Data Driven Methods

doi: 10.21656/1000-0887.460084

Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, P.R. China

Received Date: 2025-04-24
Rev Recd Date: 2025-05-04
Publish Date: 2026-05-01

Abstract

Abstract

A flow feature embedding proxy model (embedding flow feature network, EFFN) was proposed, to improve the prediction accuracy of the proxy model by integrating the flow field information into the proxy model, and enable the proxy model to predict flow features. The requirement for the total number of training data samples in the EFFN is consistent or even less than that of traditional surrogate models used for aerodynamic optimization. It has higher prediction accuracy than traditional surrogate models with the same sample size, and can accurately predict flow characteristics, while to some extent solving the problem of poor physical interpretability of surrogate models. Meanwhile, due to the more reliable values predicted by the EFFN, it has better optimization results in aerodynamic optimization design. The results of optimizing the aerodynamic performances of the 2D blade profiles show that, the total pressure loss coefficient of the optimized blade profile based on the DBN model relatively decreases by 17.3%, while the total pressure loss coefficient of the optimized blade profile based on the EFFN model relatively decreases by 18.0%. The loss performance of the optimized blade profile based on the EFFN model was highly improved.
- data driven,
- optimize design,
- neural network,
- blade cascade,
- flow features

(Contributed by TAO Jun, Member of the Youth Editorial Board of AMM)

FullText(HTML)

References(31)

References

[1]	王强, 郑日恒, 陈懋章. 航空发动机科学技术的发展与创新[J]. 科技导报, 2021, 39(3): 59-70. WANG Qiang, ZHENG Riheng, CHEN Maozhang. Development and innovation of aeroengine science and technology[J]. Science & Technology Review, 2021, 39(3): 59-70. (in Chinese)
[2]	周正贵. 压气机/风扇叶片自动优化设计的研究现状和关键技术[J]. 航空学报, 2008, 29(2): 257-266. ZHOU Zhenggui. Current situations and key techniques of automatic aerodynamic design of compressor/fan blades[J]. Acta Aeronautica et Astronautica Sinica, 2008, 29(2): 257-266. (in Chinese)
[3]	CUMPSTY N A. Compressor Aerodynamics[M]. 2nd ed. Cambridge: Cambridge University Press, 2004.
[4]	DENTON J D. Loss mechanisms in turbomachines[R]. New York: American Society of Mechanical Engineers, 1993.
[5]	陈懋章, 刘宝杰. 大涵道比涡扇发动机风扇/压气机气动设计技术分析[J]. 航空学报, 2008, 29(3): 513-526. CHEN Maozhang, LIU Baojie. Fan/compressor aero design technology for high bypass ratio turbofan[J]. Acta Aeronautica et Astronautica Sinica, 2008, 29(3): 513-526. (in Chinese)
[6]	王聪. 多流程数据驱动正/反设计方法研究及其在航空发动机部件气动设计中的应用[D]. 上海: 复旦大学, 2023. WANG Cong. Research on multi-process data-driven direct/inverse design method and its application in aero-engine component aerodynamic design[D]. Shanghai: Fudan University, 2023. (in Chinese)
[7]	HOLMES P, LUMLEY J L, BERKOOZ G. Turbulence, Coherent Structures, Dynamical Systems, and Symmetry[M]. Cambridge: Cambridge University Press, 1996.
[8]	WILLCOX K, PERAIRE J. Balanced model reduction via the proper orthogonal decomposition[J]. AIAA Journal, 2002, 40(11): 2323-2330. doi: 10.2514/2.1570
[9]	TOAL D J J, BRESSLOFF N W, KEANE A J, et al. Geometric filtration using proper orthogonal decomposition for aerodynamic design optimization[J]. AIAA Journal, 2010, 48(5): 916-928. doi: 10.2514/1.41420
[10]	SKINNER S N, ZARE-BEHTASH H. State-of-the-art in aerodynamic shape optimisation methods[J]. Applied Soft Computing, 2018, 62: 933-962. doi: 10.1016/j.asoc.2017.09.030
[11]	刘伟, 徐一冰, 王秋钧, 等. 压气机三维叶片代理辅助优化中无效样本点处理方法研究[C]//第六届空天动力联合会议暨中国航天第三专业信息网第四十二届技术交流会暨2021航空发动机技术发展高层论坛论文集. 成都, 2022: 130-138. LIU Wei, XU Yibing, WANG Qiujun, et al. Research on invalid sample points processing method in surrogate-assisted optimization of 3D compressor blades[C]//China Association for Science and Technology Aero Engine Industry-University Consortium. Proceedings of the 6th Joint Conference on Aerospace Power and the 42nd Technical Exchange Conference of the Third Professional Information Network of China Aerospace. Chengdu, 2022: 130-138. (in Chinese)
[12]	郝书荣. 压气机叶片气动力建模智能化方法研究[D]. 北京: 北京工业大学, 2021. HAO Shurong. Study on the intelligent modeling of the blade aerodynamic force in compressor[D]. Beijing: Beijing University of Technology, 2021. (in Chinese)
[13]	YONDO R, ANDRÉS E, VALERO E. A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses[J]. Progress in Aerospace Sciences, 2018, 96: 23-61.
[14]	BARNHART S A, NARAYANAN B, GUNASEKARAN S. Blown wing aerodynamic coefficient predictions using traditional machine learning and data science approaches[C]//AIAA Scitech 2021 Forum. 2021.
[15]	GOODFELLOW I, BENGIO Y, COURVILLE A. Deep Learning[M]. Cambridge: MIT Press, 2016.
[16]	SUN G, WANG S. A review of the artificial neural network surrogate modeling in aerodynamic design[J]. Proceedings of the Institution of Mechanical Engineers (Part G): Journal of Aerospace Engineering, 2019, 233: 5863-5872. doi: 10.1177/0954410019864485
[17]	SECCO N R, DE MATTOS B S. Artificial neural networks to predict aerodynamic coefficients of transport airplanes[J]. Aircraft Engineering and Aerospace Technology, 2017, 89(2): 211-230. doi: 10.1108/AEAT-05-2014-0069
[18]	DU X, HE P, MARTINS J R. A B-spline-based generative adversarial network model for fast interactive airfoil aerodynamic optimization[C]//AIAA Scitech 2020 Forum. Orlando, 2020.
[19]	DU X, HE P, MARTINS J R R A. Rapid airfoil design optimization via neural networks-based parameterization and surrogate modeling[J]. Aerospace Science and Technology, 2021, 113: 106701. doi: 10.1016/j.ast.2021.106701
[20]	WANG X D, HIRSCH C, KANG S, et al. Multi-objective optimization of turbomachinery using improved NSGA-Ⅱ and approximation model[J]. Computer Methods in Applied Mechanics and Engineering, 2011, 200(9/10/11/12): 883-895.
[21]	LI J C, DU X S, MARTINS J R R A. Machine learning in aerodynamic shape optimization[J]. Progress in Aerospace Sciences, 2022, 134: 100849.
[22]	孙刚, 王聪, 王立悦, 等. 人工智能在气动设计中的应用与展望[J]. 民用飞机设计与研究, 2021(3): 1-9. SUN Gang, WANG Cong, WANG Liyue, et al. Application and prospect of artificial intelligence in aerodynamic design[J]. Civil Aircraft Design & Research, 2021(3): 1-9. (in Chinese)
[23]	HICKS R M, HENNE P A. Wing design by numerical optimization[J]. Journal of Aircraft, 1978, 15(7): 407-412. doi: 10.2514/3.58379
[24]	ZHOU S, ZHOU H, YANG K, et al. Research on blade design method of multi-blade centrifugal fan for building efficient ventilation based on Hicks-Henne function[J]. Sustainable Energy Technologies and Assessments, 2021, 43: 100971. doi: 10.1016/j.seta.2020.100971
[25]	李正, 余华蔚, 尹红顺, 等. 椭圆前缘锐化度对亚声速压气机叶片性能的影响[J]. 燃气涡轮试验与研究, 2018, 31(3): 14-17. LI Zheng, YU Huawei, YIN Hongshun, et al. The influence of ellipse leading edge sharpness on the performance of subsonic compressor blade[J]. Gas Turbine Experiment and Research, 2018, 31(3): 14-17. (in Chinese)
[26]	吕剑波. 前缘几何对高负荷压气机叶型气动性能影响研究[D]. 北京: 中国科学院大学, 2015. LV Jianbo. The influence of leading-edge geometry on aerodynamics of high lift compressor cascades[D]. Beijing: University of Chinese Academy of Sciences, 2015. (in Chinese)
[27]	胡南平, 周正贵. 平面叶栅流场非定常特性分析[J]. 科学技术与工程, 2022, 22(26): 11705-11714. HU Nanping, ZHOU Zhenggui. Analysis of unsteady characteristics of planar cascade flow field[J]. Science Technology and Engineering, 2022, 22(26): 11705-11714. (in Chinese)
[28]	刘红波, 陆刚, 边宽江. 几种实验设计方法的比较[J]. 安徽农业科学, 2007, 35(36): 11738-11739. LIU Hongbo, LU Gang, BIAN Kuanjiang. Comparison among several methods of experimental design[J]. Journal of Anhui Agricultural Sciences, 2007, 35(36): 11738-11739. (in Chinese)
[29]	GIUNTA A A, WOJTKIEWICZ JR S F, ELDRED M S. Overview of modern design of experiments methods for computational simulations[C]//In 41st Aerospace Sciences Meeting and Exhibit. Reno, Nevada, 2003.
[30]	HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554.
[31]	HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.