高效率采样的数据关联融合气动力建模方法

宁晨伽; 王旭; 王文正; 张伟伟

doi:10.7638/kqdlxxb-2021.0425

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

高效率采样的数据关联融合气动力建模方法

研究论文 | 更新时间：2026-05-28

- 高效率采样的数据关联融合气动力建模方法
- Data association and fusion aerodynamic modeling method based on efficient sampling
- 空气动力学学报 2022年40卷第5期页码：39-49
- 作者机构：
  
  1. 西北工业大学航空学院,西安,710072
  2. 电子科技大学航空航天学院,成都,611731
  3. 飞行器集群智能感知与协同控制四川省重点实验室,成都,611731
- 作者简介：
  
  宁晨伽（1998-），女，河南洛阳人，研究方向：主要从事多源气动数据融合与关联建模研究. E-mail：ningjjx@163.com
  王文正*（1968-），男，四川简阳人，博士，研究方向：气动力建模、辨识和融合以及飞行控制. E-mail：wangwz@eustc.edu.cn
- 基金信息：
  
  国家自然科学基金面上项目（12072282）;国家自然科学基金集成项目（92152301）
- DOI：10.7638/kqdlxxb-2021.0425
  中图分类号： O354;V211.5
- 纸质出版：2022
- 稿件说明：
移动端阅览
宁晨伽, 王旭, 王文正, 等. 高效率采样的数据关联融合气动力建模方法[J]. 空气动力学学报, 2022, 40(5): 39−49.

NING Chenjia, WANG Xu, WANG Wenzheng, et al. Data association and fusion aerodynamic modeling method based on efficient sampling[J]. 2022, 40(5): 39-49.
宁晨伽, 王旭, 王文正, 等. 高效率采样的数据关联融合气动力建模方法[J]. 空气动力学学报, 2022, 40(5): 39−49. DOI： 10.7638/kqdlxxb-2021.0425

NING Chenjia, WANG Xu, WANG Wenzheng, et al. Data association and fusion aerodynamic modeling method based on efficient sampling[J]. 2022, 40(5): 39-49. DOI： 10.7638/kqdlxxb-2021.0425.

摘要

飞行器设计阶段的气动分析需要大量的高保真度气动力数据以提高设计性能，但其获取成本十分高昂。为了缓解建模成本与模型精度之间的矛盾，构建了关联不同保真度数据的多保真度气动数据融合模型，并提出了最优关联点选取方法和均匀性增强序贯采样方法，以此实现co-Kriging变可信度模型的高效初始化与最速收敛。作为验证，选用标准数值算例开展建模研究，并结合统计结果对方法精度优劣进行了对比。最后将该建模框架成功应用于NACA0012翼型跨声速气动力工程算例当中。结果表明，与传统模型相比，在仅有的少量高保真度样本下，所采用的方法可以大幅提升变可信度模型收敛精度和建模效率，有效降低了采样成本；相较于高保真度单精度元模型，误差可降低50%以上。

Abstract

Aerodynamic analysis of aircraft design often requires a large amount of high-fidelity (HF) aerodynamic data to improve the performance of aircraft design. However

the acquisition cost is very high. In order to alleviate the contradiction between modeling cost and accuracy

this paper constructs a multi-fidelity aerodynamic data fusion model by associating data with different fidelity. Furthermore

an optimal correlation point selection method and a uniformly enhanced sequential sampling method are proposed to achieve the efficient initialization and fastest convergence of variable-fidelity models based on co-Kriging. As a validation

standard numerical examples are selected to carry out modeling study

and the accuracy of the method is checked by comparing the statistical variables. Finally

the framework is successfully applied in the transonic aerodynamic engineering case of the NACA0012 airfoil. The results show that compared with the traditional model

the proposed method can greatly improve the convergence accuracy and modeling efficiency of the variable-fidelity model with only a small number of high-fidelity samples

which effectively reduces the sampling cost. Compared to the high-fidelity single precision sequence modeling

the error can be reduced by more than a half.

关键词

Keywords

references

方宝瑞. 飞机气动布局设计 [M]. 北京: 航空工业出版社, 1997.

YAN C, YIN Z Y, SHEN X L, et al. Surrogate-based optimization with improved support vector regression for non-circular vent hole on aero-engine turbine disk[J]. Aerospace Science and Technology, 2020, 96: 105332. DOI:10.1016/j.ast.2019.105332

LIU H T, ONG Y S, CAI J F, et al. Cope with diverse data structures in multi-fidelity modeling: a Gaussian process method[J]. Engineering Applications of Artificial Intelligence, 2018, 67: 211-225. DOI:10.1016/j.engappai.2017.10.008

RAJ L P, YEE K, MYONG R S. Sensitivity of ice accretion and aerodynamic performance degradation to critical physical and modeling parameters affecting airfoil icing[J]. Aerospace Science and Technology, 2020, 98: 105659. DOI:10.1016/j.ast.2019.105659

WANG W Z, GUI Y W, HE K F, et al. Aerodynamic data fusion technique exploration[J]. Acta Aerodynamica Sinica, 2009, 27(5): 524-528. (in Chinese)

KEANE A, FORRESTER A, SOBESTER A. Engineering design via surrogate modelling: a practical guide[M]. Washington, DC: AIAA, 2008. doi: 10.2514/4.479557

BANDLER J W, BIERNACKI R M, CHEN S H, et al. Space mapping technique for electromagnetic optimization[J]. IEEE Transactions on Microwave Theory and Techniques, 1994, 42(12): 2536-2544. DOI:10.1109/22.339794

BURGEE S, GIUNTA A A, BALABANOV V, et al. A coarse-grained parallel variable-complexity multidisciplinary optimization paradigm[J]. The International Journal of Supercomputer Applications and High Performance Computing, 1996, 10(4): 269-299. DOI:10.1177/109434209601000402

KENNEDY M C, O'HAGAN A. Bayesian calibration of computer models[J]. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2001, 63(3): 425-464. DOI:10.1111/1467-9868.00294

FORRESTER A I J, SÓBESTER A, KEANE A J. Multi-fidelity optimization via surrogate modelling[J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2007, 463(2088): 3251-3269. DOI:10.1098/rspa.2007.1900

LE GRATIET L, GARNIER J. Recursive co-kriging model for design of computer experiments with multiple levels of fidelity[J]. International Journal for Uncertainty Quantification, 2014, 4(5): 365-386. DOI:10.1615/int.j.uncertaintyquantification.2014006914

HAN Z H, GÖRTZ S. Hierarchical kriging model for variable-fidelity surrogate modeling[J]. AIAA Journal, 2012, 50(9): 1885-1896. DOI:10.2514/1.J051354

YAMAZAKI W, MAVRIPLIS D J. Derivative-enhanced variable fidelity surrogate modeling for aerodynamic functions[J ] . AIAA Journal , 2012, 51(1): 126-137. DOI:10.2514/1.J051633

HUANG L K, GAO Z H, ZHANG D H. Aerodynamic optimization based on multi-fidelity surrogate[J]. Acta Aerodynamica Sinica, 2013, 31(6): 783-788. (in Chinese)

HAN Z H, XU C Z, ZHANG L, et al. Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids[J]. Chinese Journal of Aeronautics, 2020, 33(1): 31-47. DOI:10.1016/j.cja.2019.05.001

LIU H T, ONG Y S, CAI J F. A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design[J]. Structural and Multidisciplinary Optimization, 2018, 57(1): 393-416. DOI:10.1007/s00158-017-1739-8

MCKAY M D, BECKMAN R J, CONOVER W J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code[J]. Technometrics, 2000, 42(1): 55-61. DOI:10.1080/00401706.2000.10485979

PARK C, HAFTKA R T, KIM N H. Remarks on multi-fidelity surrogates[J]. Structural and Multidisciplinary Optimization, 2017, 55(3): 1029-1050. DOI:10.1007/s00158-016-1550-y

LIU H T, XU S L, MA Y, et al. An adaptive Bayesian sequential sampling approach for global metamodeling[J]. Journal of Mechanical Design, 2016, 138(1): 011404. DOI:10.1115/1.4031905

XU S L, LIU H T, WANG X F, et al. A robust error-pursuing sequential sampling approach for global metamodeling based on voronoi diagram and cross validation[J]. Journal of Mechanical Design, 2014, 136(7): 071009. DOI:10.1115/1.4027161

BOOKER A J, DENNIS J E, FRANK P D, et al. A rigorous framework for optimization of expensive functions by surrogates[J]. Structural Optimization, 1999, 17(1): 1-13. DOI:10.1007/BF01197708

FORRESTER A I J, KEANE A J. Recent advances in surrogate-based optimization[J]. Progress in Aerospace Sciences, 2009, 45(1-3): 50-79. DOI:10.1016/j.paerosci.2008.11.001

SASENA M J, PAPALAMBROS P, GOOVAERTS P. Exploration of metamodeling sampling criteria for constrained global optimization[J]. Engineering Optimization, 2002, 34(3): 263-278. DOI:10.1080/03052150211751

COX D D, JOHN S. A statistical method for global optimization[C]//Proceedings of 1992 IEEE International Conference on Systems, Man, and Cybernetics, Chicago, IL, USA. IEEE, 1992: 1241-1246. doi: 10.1109/ICSMC.1992.271617

HAN Z H, XU C Z, QIAO J L, et al. Recent progress of efficient global aerodynamic shape optimization using surrogate-based approach[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(5): 25-65. (in Chinese)

HUANG D, ALLEN T T, NOTZ W I, et al. Sequential kriging optimization using multiple-fidelity evaluations[J]. Structural and Multidisciplinary Optimization, 2006, 32(5): 369-382. DOI:10.1007/s00158-005-0587-0

MEHMANI A, CHOWDHURY S, MESSAC A. Managing variable fidelity models in population-based optimization using adaptive model switching[C]//15th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Atlanta, Reston, Virginia: AIAA, 2014: 2436. doi: 10.2514/6.2014-2436

JONES D R, SCHONLAU M, WELCH W J. Efficient global optimization of expensive black-box functions[J]. Journal of Global Optimization, 1998, 13(4): 455-492. DOI:10.1023/A: 1008306431147

MATLAB A, TOOLBOX K, LOPHAVEN S N, et al. DACE - A MATLAB Kriging toolbox[M]. 2002.

SPALART P, ALLMARAS S. A one-equation turbulence model for aerodynamic flows[C]//30th Aerospace Sciences Meeting and Exhibit, Reno, NV. Reston, Virginia: AIAA, 1992: 439. doi:10.2514/6.1992-439

GAO C Q. Study on some aeroelastic problems in the vicinity of transonic buffet onset[D]. Xi'an: Northwestern Polytechnical University, 2015 (in Chinese).

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于神经网络与遗传算法的热气防冰结构优化

类HyTRV升力体阻尼/交叉动导数特性分析

基于Ac-准则的最优输入设计方法

水滴撞击运动超疏水表面特性及其调控

热防护材料动态烧蚀形貌结构光视觉测量技术