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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References
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
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
Sleesongsom S, Bureerat S (2011) Effect of actuating forces on aeroelastic characteristics of a morphing aircraft wing. Appl Mech Mater 52–54:308–317. https://doi.org/10.4028/www.scientific.net/AMM.52-54.308
Sleesongsom S, Bureerat S (2013) New conceptual design of aeroelastic wing structures by multi-objective optimization. Eng Optim 45:107–122. https://doi.org/10.1080/0305215X.2012.661728
Sleesongsom S, Bureerat S (2013) Aerodynamic reduced-order modeling without static correction requirement based on body vortices. J Eng 2013:1–6. https://doi.org/10.1155/2013/326496
Sleesongsom S, Bureerat S, Tai K (2013) Aircraft morphing wing design by using partial topology optimization. Struct Multidiscipl Optim 48:1109–1128. https://doi.org/10.1007/s00158-013-0944-3
Sleesongsom S, Bureerat S (2015) Morphing wing structural optimization using opposite-based population-based incremental learning and multigrid ground elements. Math Probl Eng 2015:1–16. https://doi.org/10.1155/2015/730626
Sleesongsom S, Winyangkul S, Bureerat S (2015) Flutter analysis of aircraft wing using an alternative reduced-order modeling method. In: International conference on power electronics and energy engineering, pp 98–101
Georgiou G, Vio GA, Cooper JE (2014) Aeroelastic tailoring and scaling using bacterial foraging optimisation. Struct Multidiscipl Optim 50:81–99. https://doi.org/10.1007/s00158-013-1033-3
Beran P, Stanford B (2013) Uncertainty quantification in aeroelasticity. Springer, Cham, pp 59–103
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
Manan A, Cooper J (2009) Design of composite wings including uncertainties: a probabilistic approach. J Aircr 46:601–607. https://doi.org/10.2514/1.39138
Scarth C, Cooper JE, Weaver PM, Silva GHC (2014) Uncertainty quantification of aeroelastic stability of composite plate wings using lamination parameters. Compos Struct 116:84–93. https://doi.org/10.1016/J.COMPSTRUCT.2014.05.007
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
Scarth C, Cooper JE (2018) Reliability-based aeroelastic design of composite plate wings using a stability margin. Struct Multidiscipl Optim 57:1695–1709. https://doi.org/10.1007/s00158-017-1838-6
Yu Y, Wang Z, Guo S (2017) Efficient method for aeroelastic tailoring of composite wing to minimize gust response. Int J Aerosp Eng 2017:1–12. https://doi.org/10.1155/2017/1592527
Wu X, Zhang W, Song S (2017) Uncertainty quantification and sensitivity analysis of transonic aerodynamics with geometric uncertainty. Int J Aerosp Eng 2017:1–16. https://doi.org/10.1155/2017/8107190
Borello F, Cestino E, Frulla G (2010) Structural uncertainty effect on classical wing flutter characteristics. J Aerosp Eng 23:327–338. https://doi.org/10.1061/(ASCE)AS.1943-5525.0000049
Papageorgiou A, Tarkian M, Amadori K, Ölvander J (2018) Multidisciplinary design optimization of aerial vehicles: a review of recent advancements. Int J Aerosp Eng 2018:1–21. https://doi.org/10.1155/2018/4258020
Yin H, Yu D, Xia B (2018) Reliability-based topology optimization for structures using fuzzy set model. Comput Methods Appl Mech Eng 333:197–217. https://doi.org/10.1016/j.cma.2018.01.019
Helton JC, Davis FJ (2003) Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab Eng Syst Saf 81:23–69. https://doi.org/10.1016/S0951-8320(03)00058-9
Zhao H, Gao Z, Xu F, Zhang Y (2018) Correction to: Review of robust aerodynamic design optimization for air vehicles. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-018-9264-5
Chatterjee T, Chakraborty S, Chowdhury R (2019) A critical review of surrogate assisted robust design optimization. Arch Comput Methods Eng 26:245–274. https://doi.org/10.1007/s11831-017-9240-5
Hui F, Weiji L (2008) An efficient method for reliability-based multidisciplinary design optimization. Chin J Aeronaut 21:335–340. https://doi.org/10.1016/S1000-9361(08)60044-8
Pholdee N, Bureerat S (2015) An efficient optimum Latin hypercube sampling technique based on sequencing optimisation using simulated annealing. Int J Syst Sci 46:1780–1789. https://doi.org/10.1080/00207721.2013.835003
Neufeld DJ (2010) Multidisciplinary aircraft conceptual design optimization considering fidelity uncertainties
Techasen T, Wansasueb K, Panagant N et al (2019) Simultaneous topology, shape, and size optimization of trusses, taking account of uncertainties using multi-objective evolutionary algorithms. Eng Comput 35:721–740. https://doi.org/10.1007/s00366-018-0629-z
Park S, Choi S, Sikorsky C, Stubbs N (2004) Efficient method for calculation of system reliability of a complex structure. Int J Solids Struct 41:5035–5050. https://doi.org/10.1016/j.ijsolstr.2004.04.028
Yu Y, Lyu Z, Xu Z, Martins JRRA (2018) On the influence of optimization algorithm and initial design on wing aerodynamic shape optimization. Aerosp Sci Technol 75:183–199. https://doi.org/10.1016/j.ast.2018.01.016
Kefal A, Oterkus E, Tessler A, Spangler JL (2016) A quadrilateral inverse-shell element with drilling degrees of freedom for shape sensing and structural health monitoring. Eng Sci Technol Int J 19:1299–1313. https://doi.org/10.1016/J.JESTCH.2016.03.006
Katz J, Plotkin A (1991) Low-speed aerodynamics: from wing theory to panel methods. McGraw-Hill, Singapore
Harder RL, Desmarais RN (1972) Interpolation using surface splines. J Aircr 9:189–191. https://doi.org/10.2514/3.44330
Zuo Y, Chen P, Fu L et al (2015) Advanced aerostructural optimization techniques for aircraft design. Math Probl Eng 2015:1–12. https://doi.org/10.1155/2015/753042
Lerner E, Markowitz J (1979) An efficient structural resizing procedure for meeting static aeroelastic design objectives. J Aircr 16:65–71. https://doi.org/10.2514/3.58486
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
Morris MD, Mitchell TJ (1995) Exploratory designs for computational experiments. J Stat Plan Inference 43:381–402. https://doi.org/10.1016/0378-3758(94)00035-T
Panagant N, Bureerat S (2018) Truss topology, shape and sizing optimization by fully stressed design based on hybrid grey wolf optimization and adaptive differential evolution. Eng Optim 50:1645–1661. https://doi.org/10.1080/0305215X.2017.1417400
Forrester AIJ, Sóbester A, Keane AJ (2008) Engineering design via surrogate modelling. Wiley, Hoboken
Zhang Qingfu, Li Hui (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11:712–731. https://doi.org/10.1109/TEVC.2007.892759
Mirjalili S, Saremi S, Mirjalili SM, dos Coelho LS (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119. https://doi.org/10.1016/J.ESWA.2015.10.039
Jain H, Deb K (2014) An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: handling constraints and extending to an adaptive approach. IEEE Trans Evol Comput 18:602–622. https://doi.org/10.1109/TEVC.2013.2281534
Pholdee N, Bureerat S (2013) Hybridisation of real-code population-based incremental learning and differential evolution for multiobjective design of trusses. Inf Sci (Ny) 223:136–152. https://doi.org/10.1016/j.ins.2012.10.008
Acknowledgements
The authors are grateful for the financial support provided by King Mongkut’s Institute of Technology Ladkrabang, the Thailand Research Fund (RTA6180010), and the Post-doctoral Program from Research Affairs, Graduate School, KhonKaen University (58225).
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Wansaseub, K., Sleesongsom, S., Panagant, N. et al. Surrogate-Assisted Reliability Optimisation of an Aircraft Wing with Static and Dynamic Aeroelastic Constraints. Int. J. Aeronaut. Space Sci. 21, 723–732 (2020). https://doi.org/10.1007/s42405-019-00246-6
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DOI: https://doi.org/10.1007/s42405-019-00246-6