论文:2015,Vol:33,Issue(6):906-912
引用本文:
刘艳, 白俊强, 华俊, 刘南, 王波. 基于StochasticKriging的柔性机翼稳健性优化设计[J]. 西北工业大学学报
Liu Yan, Bai Junqiang, Hua Jun, Liu Nan, Wang Bo. Robust Optimization of Flexible Wing Using Stochastic Kriging Surrogate Model[J]. Northwestern polytechnical university

基于StochasticKriging的柔性机翼稳健性优化设计
刘艳1, 白俊强1, 华俊1,2, 刘南1, 王波3
1. 西北工业大学 航空学院, 陕西 西安 710072;
2. 中国航空研究院, 北京 100012;
3. 中国航天空气动力技术研究院, 北京 100074
摘要:
采用随机代理模型方法对柔性机翼气动外形进行稳健性优化设计。相比确定性优化设计,稳健性设计能够考虑设计变量和参数的扰动,保持设计结果在不确定性影响下的性能稳定。采用高精度的气动/结构耦合求解器(耦合Navier-Stokes方程和结构静力学方程)分析柔性机翼的变形情况和气动效率。为了提高优化效率,建立随机Kriging(Stochastic Kriging, SK)代理模型,将确定性的Kriging代理模型发展到随机空间,通过有限次输入得到数据的固有不确定性。对柔性M6机翼的气动外形进行稳健性优化设计,结果表明:相比确定性代理模型的稳健性优化结果,应用随机代理模型的优化结果的设计点阻力系数减小2.8 counts,在可变马赫数范围内阻力系数均值减小3.2 counts,优化结果具有较高的设计点气动效率和阻力发散特性,并且优化后构型的翼根弯矩有明显减小,体现随机代理模型在稳健性优化设计系统中的优势,同时也说明建立的SK代理模型具有较高的预测精度。
关键词:    柔性机翼    稳健性优化    静气动弹性力学    随机Kriging代理模型    Navier-Stokes方程   
Robust Optimization of Flexible Wing Using Stochastic Kriging Surrogate Model
Liu Yan1, Bai Junqiang1, Hua Jun1,2, Liu Nan1, Wang Bo3
1. College of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. Chinese Aeronautics Establishment, Beijing 100012, China;
3. China Academy of Aerospace Aerodynamics, Beijing 100074, China
Abstract:
The stochastic surrogate model method is applied in the robust optimization design of flexible wing. Comparing with deterministic optimization design, robust design can take into consideration the disturbances of design variables and parameters. Therefore, the performance of design results can be kept stable under uncertainties. A High-fidelity fluid-structure coupling solver (coupled Navier-Stokes equations and static structure equations) is used to analyze the deformation and aerodynamic characteristics of flexible wing. In order to enhance optimization efficiency, Stochastic Kriging (SK) surrogate model, which extends deterministic Kriging (DK) surrogate model into stochastic space, is built. The intrinsic uncertainties of data is acquired by finite number of inputs. The robust optimization of flexible M6 wing illustrates that comparied with robust optimization results of DK, the drag coefficient of optimal result of SK has reduced 2.8 counts and the mean values in variable MachNumberrange have reduced 3.2 counts. The optimal result of SK has higher aerodynamic efficiency in design point and better drag divergence characteristics. It is manifested that stochastic surrogate model is favorable in robust optimizationand the SK surrogate model possesses high prediction accuracy.
Key words:    aeroelasticity    computational fluid dynamics    deformation    design    drag coefficient    elastic deformation    finite element method    flexible wings    flow fields    flow charting    forecasting    genetic algorithms    geometry    Mach number    mean square error    Navier Stokes equations    optimization    pressure distributions    Reynolds numbers    statistics    stochastic models    structural dynamics    robust optimization static aeroelasticity    stochastic Kriging (SK) surrogate model   
收稿日期: 2015-03-31     修回日期:
DOI:
基金项目: 国家"973"计划(2014CB744804)资助
通讯作者:     Email:
作者简介: 刘艳(1988—),西北工业大学博士研究生,主要从事气动弹性分析与多学科优化设计研究。
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参考文献:
[1] Zang T, Hemsch M J, Hilburger M W, et al. Needs and Opportunities for Uncertainty-Based Multi Disciplinary Design Methods for Aerrospace Vehicles[R]. NASA TM-2002-211462
[2] Zeeshan Q, Yunfeng D, Rafque A F, et al. Multidisciplinary Robust Design and Optimization of Multistage Boost Phase Interceptor[R]. AIAA-2010-2920
[3] Yu X, Du X. Reliability-Based Multidisciplinary Optimization for Aircraft Wing Design[J]. Structure and Infrastructure Engineering 2006, 2(3/4): 277-289
[4] Sues R H, Oakley D R, Rhodes G S. MDO of Aeropropulsion Components Considering Uncertainty[R]. AIAA-1996-4062
[5] Queipo N V, Haftka R T, Shyy W, et al. Tucker Surrogate-Based Analysis and Optimization[J]. Progress in Aerospace Sciences, 2005, 41: 1-28
[6] Schuelera G I, Jensen H A. Computational Methods in Optimization Considering Uncertainties—An Overview[J]. Computer Methods in Applied Mechanics and Engineering, 2008, 198(1): 2-13
[7] Andradottir S. A Review of Simulation Optimization Techniques[C]//Proceedings of the 1998 Winter Simulation Conference, IEEE, Piscataway, NJ, 1998: 151-158
[8] Jin R, Du X, Chen W. The Use of Metamodeling Techniques for Optimization Under Uncertainty[J]. J Struct Multidisciplinary Optim, 2003, 25(2): 99-116
[9] Hans Georg Beyer, Bernhard Sendhoff, Robust Optimization—A Comprehensive Survey[J]. Computer Methods in Applied Mechanics and Engineering, 2007, 196(33/34): 3190-3218
[10] Wang B, Bai Junqiang, Hae Chang Gea. Stochastic Kriging for Random Simulation Metamodeling with Finite Sampling[C]//39th Design Automation Conference (DAC), Portland, Oregon, 2013
[11] Liu Y, Bai J Q, Hua J. Static Aeroelasticity Analysis of High Aspect Ratio wing and the Jig-Shape Design Based on Multi-Block Structural Grid[J]. Advanced Engineering and Materials, 2013, 302: 377-383
[12] 刘艳, 白俊强, 华俊, 等. 非线性流固耦合分析方法研究[C]//中国力学大会, 2013 Liu Y, Bai J Q, Hua J, et al. An Approach to the Nonlinear Fluid-Structure Coupling Technical[C]//CCTAM2013, 2013 (in Chinese)
[13] Journel A G, Huijbregts CJ. Mining Geostatistics[M]. London, Academic Proess, UK, 1978
[14] Lamousin H J, Waggenspack W N. NURBS-Based Free-Form Deformations[J]. IEEE Computer Graphics and Applications, 1994,11: 59-65
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