Surrogate-Assisted Reliability Optimisation of an Aircraft Wing with Static and Dynamic Aeroelastic Constraints
This paper presents a numerical strategy for reliability-based design optimisation of an aircraft wing structure using a surrogate-assisted approach. The design problem is set to minimise aircraft wing mass subject to structural and aeroelastic constraints, while design variables are structural dime...
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| Vydáno v: | International journal of aeronautical and space sciences Ročník 21; číslo 3; s. 723 - 732 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Seoul
The Korean Society for Aeronautical & Space Sciences (KSAS)
01.09.2020
한국항공우주학회 |
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| ISSN: | 2093-274X, 2093-2480 |
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| Abstract | This paper presents a numerical strategy for reliability-based design optimisation of an aircraft wing structure using a surrogate-assisted approach. The design problem is set to minimise aircraft wing mass subject to structural and aeroelastic constraints, while design variables are structural dimensions. The problem has uncertainties in the material properties. The Kriging model is used for estimating the values of design functions. Two strategies of sampling technique are used, i.e., optimum Latin hypercube sampling (OLHS) with and without infill sampling. Uncertainty quantification is achieved by means of optimum normal distribution Latin hypercube sampling. The original design problem is converted to be a multiobjective optimisation problem. Optimum results show that OLHS with infill sampling gives a more accurate surrogate model; however, OLHS without infill sampling results in the better design solutions based on actual function evaluations. |
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| AbstractList | This paper presents a numerical strategy for reliability-based design optimisation of an aircraft wing structure using a surrogate-assisted approach. The design problem is set to minimise aircraft wing mass subject to structural and aeroelastic constraints, while design variables are structural dimensions. The problem has uncertainties in the material properties. The Kriging model is used for estimating the values of design functions. Two strategies of sampling technique are used, i.e., optimum Latin hypercube sampling (OLHS) with and without infill sampling. Uncertainty quantification is achieved by means of optimum normal distribution Latin hypercube sampling. The original design problem is converted to be a multiobjective optimisation problem. Optimum results show that OLHS with infill sampling gives a more accurate surrogate model; however, OLHS without infill sampling results in the better design solutions based on actual function evaluations. This paper presents a numerical strategy for reliability-based design optimisation of an aircraft wing structure using a surrogate-assisted approach. The design problem is set to minimise aircraft wing mass subject to structural and aeroelastic constraints, while design variables are structural dimensions. The problem has uncertainties in the material properties. The Kriging model is used for estimating the values of design functions. Two strategies of sampling technique are used, i.e., optimum Latin hypercube sampling (OLHS) with and without infill sampling. Uncertainty quantification is achieved by means of optimum normal distribution Latin hypercube sampling. The original design problem is converted to be a multiobjective optimisation problem. Optimum results show that OLHS with infill sampling gives a more accurate surrogate model; however, OLHS without infill sampling results in the better design solutions based on actual function evaluations. KCI Citation Count: 13 |
| Author | Panagant, N. Wansaseub, K. Bureerat, S. Pholdee, N. Sleesongsom, S. |
| Author_xml | – sequence: 1 givenname: K. surname: Wansaseub fullname: Wansaseub, K. organization: Sustainable and Infrastructure Development Center, Department of Mechanical Engineering, Faculty of Engineering, KhonKaen University – sequence: 2 givenname: S. orcidid: 0000-0002-6877-3013 surname: Sleesongsom fullname: Sleesongsom, S. email: suwin.se@kmitl.ac.th organization: Department of Aeronautical Engineering, International Academy of Aviation Industry, King Mongkut’s Institute of Technology Ladkrabang – sequence: 3 givenname: N. surname: Panagant fullname: Panagant, N. organization: Sustainable and Infrastructure Development Center, Department of Mechanical Engineering, Faculty of Engineering, KhonKaen University – sequence: 4 givenname: N. surname: Pholdee fullname: Pholdee, N. organization: Sustainable and Infrastructure Development Center, Department of Mechanical Engineering, Faculty of Engineering, KhonKaen University – sequence: 5 givenname: S. surname: Bureerat fullname: Bureerat, S. organization: Sustainable and Infrastructure Development Center, Department of Mechanical Engineering, Faculty of Engineering, KhonKaen University |
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| Cites_doi | 10.1109/TEVC.2007.892759 10.1016/j.ast.2018.01.016 10.1016/S1000-9361(08)60044-8 10.1155/2015/730626 10.1155/2017/1592527 10.1016/J.COMPSTRUCT.2014.05.007 10.1016/j.ins.2012.10.008 10.2514/6.2018-3636 10.1007/s11831-018-9264-5 10.1016/J.ESWA.2015.10.039 10.1016/j.ijsolstr.2004.04.028 10.2514/3.44330 10.1155/2013/326496 10.1007/s00158-017-1838-6 10.4028/www.scientific.net/AMM.52-54.308 10.1080/00207721.2013.835003 10.1080/0305215X.2017.1417400 10.1016/S0951-8320(03)00058-9 10.1155/2018/4258020 10.1002/9780470770801 10.1007/s00158-013-0944-3 10.2514/1.C031312 10.1007/s00366-018-0629-z 10.2514/3.58486 10.1155/2015/753042 10.1115/IMECE2002-33623 10.1007/s11831-017-9240-5 10.1016/J.JESTCH.2016.03.006 10.1007/s00158-013-1033-3 10.2514/1.39138 10.1016/j.cma.2018.01.019 10.1080/0305215X.2012.661728 10.2514/6.2007-6309 10.1109/TEVC.2013.2281534 10.1155/2017/8107190 10.1061/(ASCE)AS.1943-5525.0000049 10.1016/0378-3758(94)00035-T |
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| Keywords | Multiobjective evolutionary algorithms Aeroelasticity Aircraft wing Reliability-based design optimisation |
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In: Proceedings of the 23rd conference of mechanical engineering network of Thailand, Chiang Mai, Thailand, AME-004374 HeltonJCDavisFJLatin hypercube sampling and the propagation of uncertainty in analyses of complex systemsReliab Eng Syst Saf200381236910.1016/S0951-8320(03)00058-9 JainHDebKAn evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: handling constraints and extending to an adaptive approachIEEE Trans Evol Comput20141860262210.1109/TEVC.2013.2281534 YuYWangZGuoSEfficient method for aeroelastic tailoring of composite wing to minimize gust responseInt J Aerosp Eng2017201711210.1155/2017/1592527 KefalAOterkusETesslerASpanglerJLA quadrilateral inverse-shell element with drilling degrees of freedom for shape sensing and structural health monitoringEng Sci Technol Int J2016191299131310.1016/J.JESTCH.2016.03.006 PapageorgiouATarkianMAmadoriKÖlvanderJMultidisciplinary design optimization of aerial vehicles: a review of recent advancementsInt J Aerosp Eng2018201812110.1155/2018/4258020 MirjaliliSSaremiSMirjaliliSMdos CoelhoLSMulti-objective grey wolf optimizer: a novel algorithm for multi-criterion optimizationExpert Syst Appl20164710611910.1016/J.ESWA.2015.10.039 SleesongsomSBureeratSEffect of actuating forces on aeroelastic characteristics of a morphing aircraft wingAppl Mech Mater201152–5430831710.4028/www.scientific.net/AMM.52-54.308 HuiFWeijiLAn efficient method for reliability-based multidisciplinary design optimizationChin J Aeronaut20082133534010.1016/S1000-9361(08)60044-8 HarderRLDesmaraisRNInterpolation using surface splinesJ Aircr1972918919110.2514/3.44330 ZhangQingfuLiHuiMOEA/D: a multiobjective evolutionary algorithm based on decompositionIEEE Trans Evol Comput20071171273110.1109/TEVC.2007.892759 SleesongsomSBureeratSMorphing wing structural optimization using opposite-based population-based incremental learning and multigrid ground elementsMath Probl Eng2015201511610.1155/2015/730626 GeorgiouGVioGACooperJEAeroelastic tailoring and scaling using bacterial foraging optimisationStruct Multidiscipl Optim2014508199322393710.1007/s00158-013-1033-3 WuXZhangWSongSUncertainty quantification and sensitivity analysis of transonic aerodynamics with geometric uncertaintyInt J Aerosp Eng2017201711610.1155/2017/8107190 LernerEMarkowitzJAn efficient structural resizing procedure for meeting static aeroelastic design objectivesJ Aircr197916657110.2514/3.58486 Neufeld DJ (2010) Multidisciplinary aircraft conceptual design optimization considering fidelity uncertainties Kurdi M, Lindsley N, Beran P (2007) Uncertainty quantification of the Goland+ wing’s flutter boundary. In: AIAA Atmospheric flight mechanics conference and exhibit. American Institute of Aeronautics and Astronautics, Reston, Virigina Sleesongsom S, Nanthasene S, Benjapiyaporn J, Bureerat S (2010) Adaptive wing by using a W-spar concept. In: TSME conference system, TSME international conference on mechanical engineering, Ubon Ratchathani, Thailand PholdeeNBureeratSHybridisation of real-code population-based incremental learning and differential evolution for multiobjective design of trussesInf Sci (Ny)2013223136152299855710.1016/j.ins.2012.10.008 BorelloFCestinoEFrullaGStructural uncertainty effect on classical wing flutter characteristicsJ Aerosp Eng20102332733810.1061/(ASCE)AS.1943-5525.0000049 SleesongsomSBureeratSAerodynamic reduced-order modeling without static correction requirement based on body vorticesJ Eng201320131610.1155/2013/326496 ForresterAIJSóbesterAKeaneAJEngineering design via surrogate modelling2008HobokenWiley10.1002/9780470770801 MananACooperJDesign of composite wings including uncertainties: a probabilistic approachJ Aircr20094660160710.2514/1.39138 Cook RG, Wales C, Gaitonde A et al (2018) Uncertainty quantification of aeroelastic systems with structural or aerodynamic nonlinearities. In: Applied aerodynamics conference. American Institute of Aeronautics and Astronautics, Reston, Virginia YinHYuDXiaBReliability-based topology optimization for structures using fuzzy set modelComput Methods Appl Mech Eng2018333197217377189310.1016/j.cma.2018.01.01907186813 PholdeeNBureeratSAn efficient optimum Latin hypercube sampling technique based on sequencing optimisation using simulated annealingInt J Syst Sci2015461780178910.1080/00207721.2013.8350031332.93254 ZhaoHGaoZXuFZhangYCorrection to: Review of robust aerodynamic design optimization for air vehiclesArch Comput Methods Eng201810.1007/s11831-018-9264-5 YuYLyuZXuZMartinsJRRAOn the influence of optimization algorithm and initial design on wing aerodynamic shape optimizationAerosp Sci Technol20187518319910.1016/j.ast.2018.01.016 MorrisMDMitchellTJExploratory designs for computational experimentsJ Stat Plan Inference19954338140210.1016/0378-3758(94)00035-T0813.62065 PanagantNBureeratSTruss topology, shape and sizing optimization by fully stressed design based on hybrid grey wolf optimization and adaptive differential evolutionEng Optim20185016451661383637310.1080/0305215X.2017.1417400 ScarthCCooperJEWeaverPMSilvaGHCUncertainty quantification of aeroelastic stability of composite plate wings using lamination parametersCompos Struct2014116849310.1016/J.COMPSTRUCT.2014.05.007 ScarthCCooperJEReliability-based aeroelastic design of composite plate wings using a stability marginStruct Multidiscipl Optim2018571695170910.1007/s00158-017-1838-6 SleesongsomSBureeratSNew conceptual design of aeroelastic wing structures by multi-objective optimizationEng Optim201345107122300629910.1080/0305215X.2012.661728 ZuoYChenPFuLAdvanced aerostructural optimization techniques for aircraft designMath Probl Eng2015201511210.1155/2015/753042 ChatterjeeTChakrabortySChowdhuryRA critical review of surrogate assisted robust design optimizationArch Comput Methods Eng201926245274389517510.1007/s11831-017-9240-5 TechasenTWansasuebKPanagantNSimultaneous topology, shape, and size optimization of trusses, taking account of uncertainties using multi-objective evolutionary algorithmsEng Comput20193572174010.1007/s00366-018-0629-z Botez R, Doin A, Cotoi I (2002) Method for flutter aeroservoelastic open loop analysis. In: 5th International Symposium on fluid structure international, aeroeslasticity, and flow induced vibration and noise. ASME, pp 547–558 246_CR2 X Wu (246_CR17) 2017; 2017 C Scarth (246_CR13) 2014; 116 246_CR35 S Sleesongsom (246_CR5) 2013; 2013 246_CR14 Y Yu (246_CR16) 2017; 2017 N Panagant (246_CR37) 2018; 50 246_CR11 RL Harder (246_CR32) 1972; 9 246_CR8 JC Helton (246_CR21) 2003; 81 MD Morris (246_CR36) 1995; 43 H Zhao (246_CR22) 2018 246_CR1 E Lerner (246_CR34) 1979; 16 J Katz (246_CR31) 1991 H Yin (246_CR20) 2018; 333 S Park (246_CR28) 2004; 41 T Techasen (246_CR27) 2019; 35 A Manan (246_CR12) 2009; 46 A Papageorgiou (246_CR19) 2018; 2018 246_CR26 N Pholdee (246_CR25) 2015; 46 P Beran (246_CR10) 2013 N Pholdee (246_CR42) 2013; 223 H Jain (246_CR41) 2014; 18 A Kefal (246_CR30) 2016; 19 C Scarth (246_CR15) 2018; 57 Qingfu Zhang (246_CR39) 2007; 11 S Sleesongsom (246_CR3) 2011; 52–54 G Georgiou (246_CR9) 2014; 50 S Mirjalili (246_CR40) 2016; 47 T Chatterjee (246_CR23) 2019; 26 S Sleesongsom (246_CR7) 2015; 2015 S Sleesongsom (246_CR6) 2013; 48 F Borello (246_CR18) 2010; 23 S Sleesongsom (246_CR4) 2013; 45 Y Zuo (246_CR33) 2015; 2015 F Hui (246_CR24) 2008; 21 Y Yu (246_CR29) 2018; 75 AIJ Forrester (246_CR38) 2008 |
| References_xml | – reference: YinHYuDXiaBReliability-based topology optimization for structures using fuzzy set modelComput Methods Appl Mech Eng2018333197217377189310.1016/j.cma.2018.01.01907186813 – reference: ZuoYChenPFuLAdvanced aerostructural optimization techniques for aircraft designMath Probl Eng2015201511210.1155/2015/753042 – reference: WuXZhangWSongSUncertainty quantification and sensitivity analysis of transonic aerodynamics with geometric uncertaintyInt J Aerosp Eng2017201711610.1155/2017/8107190 – reference: MirjaliliSSaremiSMirjaliliSMdos CoelhoLSMulti-objective grey wolf optimizer: a novel algorithm for multi-criterion optimizationExpert Syst Appl20164710611910.1016/J.ESWA.2015.10.039 – reference: Nantasenee S, Sleesongsom S, Bureerat S (2009) Comparing flutter analysis programs for low speed air-vehicles. In: Proceedings of the 23rd conference of mechanical engineering network of Thailand, Chiang Mai, Thailand, AME-004374 – reference: PholdeeNBureeratSAn efficient optimum Latin hypercube sampling technique based on sequencing optimisation using simulated annealingInt J Syst Sci2015461780178910.1080/00207721.2013.8350031332.93254 – reference: PanagantNBureeratSTruss topology, shape and sizing optimization by fully stressed design based on hybrid grey wolf optimization and adaptive differential evolutionEng Optim20185016451661383637310.1080/0305215X.2017.1417400 – reference: SleesongsomSBureeratSNew conceptual design of aeroelastic wing structures by multi-objective optimizationEng Optim201345107122300629910.1080/0305215X.2012.661728 – reference: YuYLyuZXuZMartinsJRRAOn the influence of optimization algorithm and initial design on wing aerodynamic shape optimizationAerosp Sci Technol20187518319910.1016/j.ast.2018.01.016 – reference: KatzJPlotkinALow-speed aerodynamics: from wing theory to panel methods1991SingaporeMcGraw-Hill – reference: ScarthCCooperJEReliability-based aeroelastic design of composite plate wings using a stability marginStruct Multidiscipl Optim2018571695170910.1007/s00158-017-1838-6 – reference: MorrisMDMitchellTJExploratory designs for computational experimentsJ Stat Plan Inference19954338140210.1016/0378-3758(94)00035-T0813.62065 – reference: SleesongsomSBureeratSEffect of actuating forces on aeroelastic characteristics of a morphing aircraft wingAppl Mech Mater201152–5430831710.4028/www.scientific.net/AMM.52-54.308 – reference: YuYWangZGuoSEfficient method for aeroelastic tailoring of composite wing to minimize gust responseInt J Aerosp Eng2017201711210.1155/2017/1592527 – reference: BorelloFCestinoEFrullaGStructural uncertainty effect on classical wing flutter characteristicsJ Aerosp Eng20102332733810.1061/(ASCE)AS.1943-5525.0000049 – reference: HarderRLDesmaraisRNInterpolation using surface splinesJ Aircr1972918919110.2514/3.44330 – reference: Botez R, Doin A, Cotoi I (2002) Method for flutter aeroservoelastic open loop analysis. In: 5th International Symposium on fluid structure international, aeroeslasticity, and flow induced vibration and noise. ASME, pp 547–558 – reference: ChatterjeeTChakrabortySChowdhuryRA critical review of surrogate assisted robust design optimizationArch Comput Methods Eng201926245274389517510.1007/s11831-017-9240-5 – reference: SleesongsomSBureeratSAerodynamic reduced-order modeling without static correction requirement based on body vorticesJ Eng201320131610.1155/2013/326496 – reference: Kurdi M, Lindsley N, Beran P (2007) Uncertainty quantification of the Goland+ wing’s flutter boundary. In: AIAA Atmospheric flight mechanics conference and exhibit. American Institute of Aeronautics and Astronautics, Reston, Virigina – reference: SleesongsomSBureeratSMorphing wing structural optimization using opposite-based population-based incremental learning and multigrid ground elementsMath Probl Eng2015201511610.1155/2015/730626 – reference: KefalAOterkusETesslerASpanglerJLA quadrilateral inverse-shell element with drilling degrees of freedom for shape sensing and structural health monitoringEng Sci Technol Int J2016191299131310.1016/J.JESTCH.2016.03.006 – reference: Cook RG, Wales C, Gaitonde A et al (2018) Uncertainty quantification of aeroelastic systems with structural or aerodynamic nonlinearities. In: Applied aerodynamics conference. 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| Title | Surrogate-Assisted Reliability Optimisation of an Aircraft Wing with Static and Dynamic Aeroelastic Constraints |
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