基于自适应随机优化的连续阵风关键载荷预测
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摘要
连续阵风载荷是构成民用飞机设计工况的主要载荷之一,在设计阶段,任意一轮的模型更新都涉及到上万种载荷工况的计算,然而其中仅个别工况构成载荷包线,需进行强度校核。为此发展了一套阵风关键载荷的快速识别方法。首先,采用二水平全因子(2LFF)采样获取得到初始计算工况,基于已计算得到的载荷值,结合多元自适应回归样条(MARS)建立一个可靠的代理模型;然后,在此基础上,开创性地应用自适应随机优化技术,实现对阵风关键工况及载荷的主动搜索;最后,以适航条款规定的侧向连续阵风载荷进行方法验证及参数影响研究。计算结果表明,本文建立的方法可以高效且准确地实现连续阵风关键载荷的预测,针对本文算例,关键载荷的预测值与基准值相比误差小于1%。
Continuous gust load is one of the main loads for commercial airplane.In the design stage,any loop of model update involves the calculation of thousands of gust load cases;however,only a few cases constitute the critical load envelop,and are needed for strength check.Therefore,a rapid identification method for gust critical load is developed in this paper.First of all,using the two-level full factorial(2 LFF)sampling technique,a set of initial calculation cases is obtained.With the calculated load values,a reliable surrogate model is established based on the multivariate adaptive regression spline(MARS).Secondly,on this basis,adaptive stochastic optimization technology is adopted to achieve active search of the critical gust case and load.Finally,using the test case of the lateral continuous gust load,the validation and the investigation on the influence of parameters are carried out.The calculation results show that the proposed method can effectively and accurately predict the gust critical load,and the prediction error of critical load is less than 1%.
Continuous gust load is one of the main loads for commercial airplane.In the design stage,any loop of model update involves the calculation of thousands of gust load cases;however,only a few cases constitute the critical load envelop,and are needed for strength check.Therefore,a rapid identification method for gust critical load is developed in this paper.First of all,using the two-level full factorial(2 LFF)sampling technique,a set of initial calculation cases is obtained.With the calculated load values,a reliable surrogate model is established based on the multivariate adaptive regression spline(MARS).Secondly,on this basis,adaptive stochastic optimization technology is adopted to achieve active search of the critical gust case and load.Finally,using the test case of the lateral continuous gust load,the validation and the investigation on the influence of parameters are carried out.The calculation results show that the proposed method can effectively and accurately predict the gust critical load,and the prediction error of critical load is less than 1%.
引文
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[2]KHODAPARAST H H,GEORGIOU G,COOPER J E,et al.Rapid prediction of worst case gust loads[J].Journal of Aeroelasticity and Structural Dynamics,2012,2(3):33-54.
[3]COOK R,CALDERON D,LOWENBERG M H,et al.Worstcase gust prediction of highly flexible wings[C]∥Proceedings of 58th AIAA/ASCE/AHS/ASC Structures,Structural Dynamics,and Materials Conference.Reston,VA:AIAA,2017:1355-1371.
[4]毕莹,杨超,吴志刚.考虑气动力非线性的柔性飞机阵风响应分析[J].北京航空航天大学学报,2015,41(7):1208-1214.BI Y,YANG C,WU Z G.Gust response analysis of flexible aircraft with aerodynamic nonlinearity[J].Journal of Beijing University of Aeronautics and Astronautics,2015,41(7):1208-1214(in Chinese).
[5]杨超,黄超,吴志刚,等.气动伺服弹性研究的进展与挑战[J].航空学报,2015,36(4):1011-1033.YANG C,HUANG C,WU Z G,et al.Progress and challenges for aeroservoelasticity research[J].Acta Aeronautica et Astronautica Sinica,2015,36(4):1011-1033(in Chinese).
[6]KARR C L,ZEILER T A,MEHROTRA R.Determining worst-case gust loads on aircraft structures using an evolutionary algorithm[J].Applied Intelligence,2004,20(2):135-145.
[7]KHODAPARAST H H,COOPER J E.Rapid prediction of worst case gust loads following structural modification[J].AIAA Journal,2014,52(2):242-254.
[8]CAVAGNA L,RICCI S,RICCOBENE L.Fast-GLP:Afast tool for the prediction of worst case gust loads based on neural networks[C]∥Proceedings of 54th AIAA/ASME/ASCE/AHS/ASC Structures,Structural Dynamics,and Materials Conference.Reston,VA:AIAA,2013:1493-1505.
[9]CASTELLANI M,LEMMENS Y,COOPER J E.Parametric reduced order model approach for rapid dynamic loads prediction[J].Aerospace Science and Technology,2016,52:29-40.
[10]严德,杨超,肖志鹏.弹性飞机平衡的阵风外载荷计算与分析[J].北京航空航天大学学报,2012,38(10):1321-1325.YAN D,YANG C,XIAO Z P.Balanced external gust loads computation and analysis for elastic aircraft[J].Journal of Beijing University of Aeronautics and Astronautics,2012,38(10):1321-1325(in Chinese).
[11]GIUNTA A,WOJTKIEWICZ S,ELDRED M.Overview of modern design of experiments methods for computational simulations[C]∥Proceedings of 41st Aerospace Sciences Meeting and Exhibit.Reston,VA:AIAA,2003:649.
[12]HOSDER S,WATSON L T,GROSSMAN B,et al.Polynomial response surface approximations for the multidisciplinary design optimization of a high speed civil transport[J].Optimization and Engineering,2001,2(4):431-452.
[13]SIMPSON T W,MAUERY T M,KORTE J J,et al.Kriging models for global approximation in simulationbased multidisciplinary design optimization[J].AIAA-Journal,2001,39(12):2233-2241.
[14]MULLUR A A,MESSAC A.Extended radial basis functions:more flexible and effective metamodeling[J].AIAAJournal,2005,43(6):1306-1315.
[15]RAI M M,MADAVAN N K.Aerodynamic design using neural networks[J].AIAA Journal,2000,38(1):173-182.
[16]CHEN G,ZUO Y T,SUN J,et al.Limitcycle oscillation prediction via support vector machine based reduced order model[C]∥Proceedings of 52nd AIAA/ASME/ASCE/AHS/ASC Structures,Structural Dynamics and Materials Conference.Reston,VA:AIAA,2011:1744.
[17]FRIEDMAN J H.Multivariate adaptive regression splines[J].The Annals of Statistics,1991,19(1):1-67.
[18]REGIS P R,SHOEMAKERC A.A stochastic radial basis function method for the global optimization of expensive functions[J].Informs Journal on Computing,2007,19(4):497-509.