Skip to main content
SpringerLink
Log in

Comparative study of recent metaheuristics for solving a multiobjective transonic aeroelastic optimization of a composite wing

  • Original Paper
  • Published:
Acta Mechanica Aims and scope Submit manuscript

Abstract

A transonic aeroelastic optimization approach for composite wing structure is proposed in this study. Aerodynamic influence coefficient matrices are generated using doublet lattice method. The steady state part is then corrected by high fidelity computational fluid dynamic analysis for better accuracy at transonic speed. A mutiobjective transonic aeroelastic optimization problem for composite wing structure is then developed. The objective functions are mass and critical speed while design constraints are structural and aeroelastic limits. A comparative study of eight state-of-the-art algorithms on the problem is performed. Additional results of 21 mechanical optimization problems are evaluated to further investigate performance and versatility of the algorithms. Overall, the Multiobjective Manta Ray Foraging Optimizer is the best algorithm in this study with the best results in the aeroelastic optimization problem and the second-best results in mechanical problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Konstadinopoulos, P., Thrasher, D.F., Mook, D.T., Nayfeh, A.H., Watson, L.: A vortex-lattice method for general, unsteady aerodynamics. J. Aircr. 22, 43–49 (1985). https://doi.org/10.2514/3.45078

    Article  Google Scholar 

  2. Murua, J., Palacios, R., Graham, J.M.R.: Applications of the unsteady vortex-lattice method in aircraft aeroelasticity and flight dynamics. Prog. Aerosp. Sci. 55, 46–72 (2012). https://doi.org/10.1016/J.PAEROSCI.2012.06.001

    Article  Google Scholar 

  3. Lan, C.E.: A quasi-vortex-lattice method in thin wing theory. J. Aircr. 11, 518–527 (1974). https://doi.org/10.2514/3.60381

    Article  Google Scholar 

  4. Patil, M.J., Hodges, D.H.: On the importance of aerodynamic and structural geometrical nonlinearities in aeroelastic behavior of high-aspect-ratio wings. J. Fluids Struct. 19, 905–915 (2004). https://doi.org/10.1016/j.jfluidstructs.2004.04.012

    Article  Google Scholar 

  5. Mahran, M.A., Negm, H.M., Maalawi, K.Y., Elsabbagh, A.M.: Aero-elastic analysis and optimization of composite plate wings, ECCM 2016—Proceeding 17th Eurropean conferences composite materials (2016)

  6. Elham, A., van Tooren, M.J.L.: Coupled adjoint aerostructural wing optimization using quasi-three-dimensional aerodynamic analysis. Struct. Multidiscip. Optim. 54, 889–906 (2016). https://doi.org/10.1007/s00158-016-1447-9

    Article  MathSciNet  Google Scholar 

  7. Du, X., Amrit, A., Thelen, A., Leifsson, L., Zhang, Y., Han, Z.H., Koziel, S.: Aerodynamic design of a rectangular wing in subsonic inviscid flow by surrogate-based optimization, 35th AIAA. Appl. Aerodyn. Conf. 2017, 1–17 (2017). https://doi.org/10.2514/6.2017-4366

    Article  Google Scholar 

  8. Carrigan, T.J., Dennis, B.H., Han, Z.X., Wang, B.P.: Aerodynamic shape optimization of a vertical-axis wind turbine using differential evolution. ISRN Renew. Energy 2012, 1–16 (2012). https://doi.org/10.5402/2012/528418

    Article  Google Scholar 

  9. Mazur, M.R., Galewski, M.A., Kaliński, K.J.: Estimation of structural stiffness with the use of particle swarm optimization. Lat. Am. J. Solids Struct. 18, 1–18 (2021). https://doi.org/10.1590/1679-78256400

    Article  Google Scholar 

  10. Zhao, Y., Wang, C.: Shape optimization of labyrinth seals to improve sealing performance. Aerospace 8, 92 (2021). https://doi.org/10.3390/AEROSPACE8040092

    Article  Google Scholar 

  11. Fahey, T., Muffatti, A., Ogawa, H.: High fidelity multi-objective design optimization of a downscaled cusped field thruster. Aerospace 4, 55 (2017). https://doi.org/10.3390/AEROSPACE4040055

    Article  Google Scholar 

  12. Li, J., Zhu, Z.Q., Chen, Z.M., Li, H.M.: The Euler and Navier-Stokes solutions of a 3-D wing with aileron. Acta Mech. 138, 51–59 (1999). https://doi.org/10.1007/BF01179541/METRICS

    Article  Google Scholar 

  13. Zhu, Z.Q., Wu, Z.C., Li, J., Chen, Z.M.: Flow field analysis of a 3D wing with flap based on N-S solution. Acta Mech. 148, 249–259 (2001). https://doi.org/10.1007/BF01183682/METRICS

    Article  Google Scholar 

  14. Chen, P.C., Gao, X.W., Tang Chen, L.: Overset field-panel method for unsteady transonic aerodynamic influence coefficient matrix generation. AIAA J. 42(9), 1775–1787 (2004). https://doi.org/10.2514/1.4390

    Article  Google Scholar 

  15. Silva, R.G.A., Mello, O.A.F., Azevedo, J.L.F., Chen, P.C., Liu, D.D.: Investigation on transonic correction methods for unsteady aerodynamics and aeroelastic analyses. J. Aircr. 45(6), 1890–1903 (2008). https://doi.org/10.2514/1.33406

    Article  Google Scholar 

  16. Friedewald, D., Thormann, R., Kaiser, C., Nitzsche, J.: Quasi-steady doublet-lattice correction for aerodynamic gust response prediction in attached and separated transonic flow. CEAS Aeronaut. J. 9, 53–66 (2018). https://doi.org/10.1007/S13272-017-0273-0/FIGURES/21

    Article  Google Scholar 

  17. Thormann, R., Dimitrov, D.: Correction of aerodynamic influence matrices for transonic flow. CEAS Aeronaut. J. 5, 435–446 (2014). https://doi.org/10.1007/S13272-014-0114-3/FIGURES/17

    Article  Google Scholar 

  18. Körpe, D.S., Özgen, S.: Multi objective morphing wing optimization for an unmanned air vehicle, In: 7th European conference aeronautics space science (2017), pp. 1–10. https://doi.org/10.13009/EUCASS2017-184

  19. Kenway, G.K.W., Martins, J.R.R.A.: Multipoint aerodynamic shape optimization investigations of the common research model wing. AIAA J. 54, 113–128 (2016). https://doi.org/10.2514/1.J054154

    Article  Google Scholar 

  20. Brooks, T.R., Martins, J.R.R.A., Kennedy, G.J.: High-fidelity multipoint aerostructural optimization of a high aspect ratio tow-steered composite wing, 58th AIAA/ASCE/AHS/ASC. Struct. Struct. Dyn. Mater. Conf. 2017(88), 122–147 (2017). https://doi.org/10.2514/6.2017-1350

    Article  Google Scholar 

  21. Brooks, T.R., Kenway, G.K.W., Martins, J.R.R.A.: Benchmark aerostructural models for the study of transonic aircraft wings. AIAA J. 56, 2840–2855 (2018). https://doi.org/10.2514/1.J056603

    Article  Google Scholar 

  22. Nemec, M., Zingg, D.W., Pulliam, T.H.: Multipoint and multi-objective aerodynamic shape optimization. AIAA J. 42, 1057–1065 (2004). https://doi.org/10.2514/1.10415

    Article  Google Scholar 

  23. Ghommem, M., Collier, N., Niemi, A.H., Calo, V.M.: On the shape optimization of flapping wings and their performance analysis. Aerosp. Sci. Technol. 32, 274–292 (2014). https://doi.org/10.1016/j.ast.2013.10.010

    Article  Google Scholar 

  24. Koreanschi, A., Sugar Gabor, O., Acotto, J., Brianchon, G., Portier, G., Botez, R.M., Mamou, M., Mebarki, Y.: Optimization and design of an aircraft’s morphing wing-tip demonstrator for drag reduction at low speed, part I—aerodynamic optimization using genetic, bee colony and gradient descent algorithms, Chinese. J. Aeronaut. 30, 149–163 (2017). https://doi.org/10.1016/j.cja.2016.12.013

    Article  Google Scholar 

  25. Yu, Y., Lyu, Z., Xu, Z., Martins, J.R.R.A.: On the influence of optimization algorithm and initial design on wing aerodynamic shape optimization. Aerosp. Sci. Technol. 75, 183–199 (2018). https://doi.org/10.1016/j.ast.2018.01.016

    Article  Google Scholar 

  26. Ikonen, T.J., Sóbestery, A.: Ground structure approaches for the evolutionary optimization of aircraft wing structures, 16th AIAA aviation technology integration operations conference (2016), pp. 1–21

  27. Poole, D.J., Allen, C.B., Rendall, T.C.S.: Global optimization of wing aerodynamic optimization case exhibiting multimodality. J. Aircr. 55, 1576–1591 (2018). https://doi.org/10.2514/1.C034718

    Article  Google Scholar 

  28. Ramananarivo, S., Mitchel, T., Ristroph, L.: Improving the propulsion speed of a heaving wing through artificial evolution of shape. Proc. R. Soc. A Math. Phys. Eng. Sci. (2019). https://doi.org/10.1098/rspa.2018.0375

    Article  Google Scholar 

  29. Shrivastava, S., Mohite, P.M., Yadav, T., Malagaudanavar, A.: Multi-objective multi-laminate design and optimization of a carbon fibre composite wing torsion box using evolutionary algorithm. Compos. Struct. 185, 132–147 (2018). https://doi.org/10.1016/j.compstruct.2017.10.041

    Article  Google Scholar 

  30. Obayashi, S., Hirose, N., Sasaki, D., Takeguchi, Y.: Multiobjective evolutionary computation for supersonic wing-shape optimizationdaiyukihiro. IEEE Trans. Evol. Comput. 4, 182–187 (2000)

    Article  Google Scholar 

  31. Stalewski, W., Żółtak, J.: Multi-objective and multidisciplinary optimization of wing for small aircraft, In: 3rd CEAS Air Space Conference 21st AIDAA Congress (2017). https://www.researchgate.net/publication/265050315_Multi-objective_and_multidisciplinary_optimization_of_wing_for_small_aircraft

  32. Attaran, A., Majid, D.L., Basri, S., Mohd Rafie, A.S., Abdullah, E.J.: Structural optimization of an aeroelastically tailored composite flat plate made of woven fiberglass/epoxy. Acta Mech. 196, 161–173 (2008). https://doi.org/10.1007/S00707-007-0488-Y/METRICS

    Article  Google Scholar 

  33. Sabri, F., Elzaabalawy, A., Meguid, S.A.: Aeroelastic behaviour of a flexible morphing wing design for unmanned aerial vehicle. Acta Mech. 233, 851–867 (2022). https://doi.org/10.1007/S00707-021-03138-7/FIGURES/23

    Article  MathSciNet  Google Scholar 

  34. Ahmad, M.F., Isa, N.A.M., Lim, W.H., Ang, K.M.: Differential evolution with modified initialization scheme using chaotic oppositional based learning strategy. Alex. Eng. J. 61, 11835–11858 (2022). https://doi.org/10.1016/J.AEJ.2022.05.028

    Article  Google Scholar 

  35. Xu, B., Gong, D., Zhang, Y., Yang, S., Wang, L., Fan, Z., Zhang, Y.: Cooperative co-evolutionary algorithm for multi-objective optimization problems with changing decision variables. Inf. Sci. 607, 278–296 (2022). https://doi.org/10.1016/J.INS.2022.05.123

    Article  Google Scholar 

  36. Khor, E.F., Tan, K.C., Wang, M.L., Lee, T.H.: Evolutionary algorithm with dynamic population size for multi-objective optimization, In: IECON Proceedings Industrial Electronics Conference, Elsevier, 2000: pp. 2768–2773. https://doi.org/10.1109/IECON.2000.972436

  37. Holland, J.H.: Adaptation in natural and artificial systems : an introductory analysis with applications to biology, control, and artificial intelligence (1975)

  38. Koza, J.R., Poli, R.: Chapter 5 GENETIC PROGRAMMING, Computer (Long. Beach. Calif) (1983)

  39. Rechenberg, I.: Evolution strategy: nature’s way of optimization, in: 1989. https://doi.org/10.1007/978-3-642-83814-9_6

  40. Kaveh, A., Talatahari, S.: A hybrid particle swarm and ant colony optimization for design of truss structures. ASIAN J. Civ. Eng. (Building Housing) 9, 329–348 (2008)

    Google Scholar 

  41. Kaveh, A., Bakhshpoori, T., Afshari, E.: An efficient hybrid particle swarm and swallow swarm optimization algorithm. Comput. Struct. 143, 40–59 (2014). https://doi.org/10.1016/J.COMPSTRUC.2014.07.012

    Article  Google Scholar 

  42. Hashim, F.A., Hussien, A.G.: Snake optimizer: a novel meta-heuristic optimization algorithm. Knowl. -Based Syst. 242, 108320 (2022). https://doi.org/10.1016/J.KNOSYS.2022.108320

    Article  Google Scholar 

  43. Pan, J.S., Zhang, L.G., Bin Wang, R., Snášel, V., Chu, S.C.: Gannet optimization algorithm: a new metaheuristic algorithm for solving engineering optimization problems. Math. Comput. Simul 202, 343–373 (2022). https://doi.org/10.1016/J.MATCOM.2022.06.007

    Article  MathSciNet  Google Scholar 

  44. Wang, D., Tan, D., Liu, L.: Particle swarm optimization algorithm: an overview. Soft. Comput. 22, 387–408 (2018). https://doi.org/10.1007/s00500-016-2474-6

    Article  Google Scholar 

  45. Kennedy, J., Eberhart, R.: Particle swarm optimization, In: Proceeding ICNN’95-International Conference Neural Networks., Perth, WA, Australia (1995), pp. 1942–1948. https://doi.org/10.1109/ICNN.1995.488968

  46. Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27, 1053–1073 (2016). https://doi.org/10.1007/s00521-015-1920-1

    Article  Google Scholar 

  47. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459–471 (2007). https://doi.org/10.1007/s10898-007-9149-x

    Article  MathSciNet  Google Scholar 

  48. Hashim, F.A., Houssein, E.H., Hussain, K., Mabrouk, M.S., Al-Atabany, W.: Honey badger algorithm: new metaheuristic algorithm for solving optimization problems. Math. Comput. Simul 192, 84–110 (2022). https://doi.org/10.1016/J.MATCOM.2021.08.013

    Article  MathSciNet  Google Scholar 

  49. Su, S., Xiong, D., Yu, H., Dong, X.: A multiple leaders particle swarm optimization algorithm with variable neighborhood search for multiobjective fixed crowd carpooling problem. Swarm Evol. Comput. 72, 101103 (2022). https://doi.org/10.1016/j.swevo.2022.101103

    Article  Google Scholar 

  50. Abbasi, M.S., Al-Sahaf, H., Mansoori, M., Welch, I.: Behavior-based ransomware classification: a particle swarm optimization wrapper-based approach for feature selection. Appl. Soft Comput. 121, 108744 (2022). https://doi.org/10.1016/J.ASOC.2022.108744

    Article  Google Scholar 

  51. Zhong, C., Li, G., Meng, Z.: Beluga whale optimization: a novel nature-inspired metaheuristic algorithm. Knowl. -Based Syst. 251, 109215 (2022). https://doi.org/10.1016/J.KNOSYS.2022.109215

    Article  Google Scholar 

  52. Faramarzi, A., Heidarinejad, M., Stephens, B., Mirjalili, S.: Equilibrium optimizer: a novel optimization algorithm. Knowl. -Based Syst. 191, 105190 (2020). https://doi.org/10.1016/j.knosys.2019.105190

    Article  Google Scholar 

  53. Wansasueb, K., Panmanee, S., Panagant, N., Pholdee, N., Bureerat, S., Riza Yildiz, A.: Hybridised differential evolution and equilibrium optimiser with learning parameters for mechanical and aircraft wing design. Knowl. -Based Syst. 239, 107955 (2022). https://doi.org/10.1016/j.knosys.2021.107955

    Article  Google Scholar 

  54. Kumar, S., Jangir, P., Tejani, G.G., Premkumar, M.: MOTEO: a novel physics-based multiobjective thermal exchange optimization algorithm to design truss structures. Knowl. -Based Syst. 242, 108422 (2022). https://doi.org/10.1016/J.KNOSYS.2022.108422

    Article  Google Scholar 

  55. Sundaram, A.: Multiobjective multi verse optimization algorithm to solve dynamic economic emission dispatch problem with transmission loss prediction by an artificial neural network. Appl. Soft Comput. 124, 109021 (2022). https://doi.org/10.1016/J.ASOC.2022.109021

    Article  Google Scholar 

  56. Nematollahi, A.F., Rahiminejad, A., Vahidi, B.: MOGROM: multiobjective golden ratio optimization algorithm. Multi-Objective Comb. Optim. Probl. Solut. Methods (2022). https://doi.org/10.1016/B978-0-12-823799-1.00005-X

    Article  Google Scholar 

  57. Shahbazi, M.M., Gholipour, Y., Behnia, A.: An improved version of inverse distance weighting metamodel assisted harmony search algorithm for truss design optimization. Lat. Am. J. Solids Struct. 10, 283–300 (2013)

    Article  Google Scholar 

  58. Verij Kazemi, M., Fazeli Veysari, E.: A new optimization algorithm inspired by the quest for the evolution of human society: human felicity algorithm. Expert Syst. Appl. 193, 116468 (2022). https://doi.org/10.1016/J.ESWA.2021.116468

    Article  Google Scholar 

  59. Xu, Z., Zhang, K.: Multiobjective multifactorial immune algorithm for multiobjective multitask optimization problems. Appl. Soft Comput. 107, 107399 (2021). https://doi.org/10.1016/J.ASOC.2021.107399

    Article  Google Scholar 

  60. Fomin, F.V., Fraigniaud, P., Golovach, P.A.: Present-biased optimization. Math. Soc. Sci. 119, 56–67 (2022). https://doi.org/10.1016/J.MATHSOCSCI.2022.06.001

    Article  MathSciNet  Google Scholar 

  61. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014). https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  62. Wansasueb, K., Pholdee, N., Panagant, N., Bureerat, S.: Multiobjective meta-heuristic with iterative parameter distribution estimation for aeroelastic design of an aircraft wing. Eng. Comput. (2020). https://doi.org/10.1007/s00366-020-01077-w

    Article  Google Scholar 

  63. Li, X., Wan, Z., Wang, X., Yang, C.: Aeroelastic optimization design of the global stiffness for a joined wing aircraft. Appl. Sci. 2021(11), 11800 (2021). https://doi.org/10.3390/APP112411800

    Article  Google Scholar 

  64. Khan, K.H., Kapania, R.K., Schetz, J.A., Mallik, W.: Distributed design optimization of large aspect ratio wing aircraft with rapid transonic flutter analysis in linux, AIAA Scitech 2021 Forum (2021), pp. 1–16. https://doi.org/10.2514/6.2021-1354

  65. Wang, Z., Wan, Z., Groh, R.M.J., Wang, X.: Aeroelastic and local buckling optimisation of a variable-angle-tow composite wing-box structure. Compos. Struct. 258, 113201 (2021). https://doi.org/10.1016/J.COMPSTRUCT.2020.113201

    Article  Google Scholar 

  66. Phiboon, T., Khankwa, K., Petcharat, N., Phoksombat, N., Kanazaki, M., Kishi, Y., Ariyarit, A.: Experiment and computation multi-fidelity multi-objective airfoil design optimization of fixed-wing UAV. J. Mech. Sci. Technol. 35(9), 4065–4072 (2021). https://doi.org/10.1007/S12206-021-0818-3

    Article  Google Scholar 

  67. Salim, M., Bodaghi, M., Kamarian, S., Shakeri, M.: Free vibration analysis and design optimization of SMA/graphite/epoxy composite shells in thermal environments. Lat. Am. J. Solids Struct. 15, 1–16 (2017). https://doi.org/10.1590/1679-78253070

    Article  Google Scholar 

  68. Cardozo, S.D., Awruch, A.M., Gomes, H.M.: Optimization of laminated composite plates and shells using genetic algorithms, neural networks and finite elements. Lat. Am. J. Solids Struct. 8, 413 (2011)

    Article  Google Scholar 

  69. Huo, P., Fan, X., Yang, X., Huang, H., Li, J., Xu, S.: Design and optimization of equal gradient thin-walled tube: bionic application of antler osteon. Lat. Am. J. Solids Struct. (2021). https://doi.org/10.1590/1679-78256406

    Article  Google Scholar 

  70. Zervoudakis, K., Tsafarakis, S.: A mayfly optimization algorithm. Comput. Ind. Eng. 145, 106559 (2020). https://doi.org/10.1016/J.CIE.2020.106559

    Article  Google Scholar 

  71. Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., Mirjalili, S.M.: Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114, 163–191 (2017). https://doi.org/10.1016/J.ADVENGSOFT.2017.07.002

    Article  Google Scholar 

  72. Mirjalili, S.Z., Mirjalili, S., Saremi, S., Faris, H., Aljarah, I.: Grasshopper optimization algorithm for multi-objective optimization problems. Appl. Intell. 48, 805–820 (2018). https://doi.org/10.1007/S10489-017-1019-8/TABLES/9

    Article  Google Scholar 

  73. Mirjalili, S., Jangir, P., Mirjalili, S.Z., Saremi, S., Trivedi, I.N.: Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowl. -Based Syst. 134, 50–71 (2017). https://doi.org/10.1016/J.KNOSYS.2017.07.018

    Article  Google Scholar 

  74. Got, A., Zouache, D., Moussaoui, A.: MOMRFO: multi-objective manta ray foraging optimizer for handling engineering design problems. Knowl. -Based Syst. 237, 107880 (2022). https://doi.org/10.1016/J.KNOSYS.2021.107880

    Article  Google Scholar 

  75. Khodadadi, N., Azizi, M., Talatahari, S., Sareh, P.: Multi-objective crystal structure algorithm (MOCryStAl): introduction and performance evaluation. IEEE Access (2021). https://doi.org/10.1109/ACCESS.2021.3106487

    Article  Google Scholar 

  76. Chou, J.S., Truong, D.N.: Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems. Chaos Solitons Fractals 135, 109738 (2020). https://doi.org/10.1016/J.CHAOS.2020.109738

    Article  MathSciNet  Google Scholar 

  77. Pereira, J.L.J., Oliver, G.A., Francisco, M.B., Cunha, S.S., Jr., Gomes, G.F.: Multi-objective lichtenberg algorithm: a hybrid physics-based meta-heuristic for solving engineering problems. Expert Syst. Appl. 187, 115939 (2022). https://doi.org/10.1016/J.ESWA.2021.115939

    Article  Google Scholar 

  78. Kumar, A., Wu, G., Ali, M.Z., Luo, Q., Mallipeddi, R., Suganthan, P.N., Das, S.: A benchmark-suite of real-world constrained multi-objective optimization problems and some baseline results. Swarm Evol. Comput. (2021). https://doi.org/10.1016/j.swevo.2021.100961

    Article  Google Scholar 

  79. Computational Results|NASA Common Research Model, (n.d.).: https://commonresearchmodel.larc.nasa.gov/computational-results/. Accessed 23 May 2022

  80. DPW6 Geometries|NASA Common Research Model, (n.d.).: https://commonresearchmodel.larc.nasa.gov/geometry/dpw6-geometries/. Accessed 23 May 2022

  81. Valencia, E., Alulema, V., Hidalgo, V., Rodriguez, D.: A CAD-free methodology for volume and mass properties computation of 3-D lifting surfaces and wing-box structures. Aerosp. Sci. Technol. 108, 106378 (2021). https://doi.org/10.1016/j.ast.2020.106378

    Article  Google Scholar 

  82. Kefal, A., Oterkus, E., Tessler, A., Spangler, J.L.: 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 (2016)

    Google Scholar 

  83. Kefal, A., Tessler, A., Oterkus, E.: An enhanced inverse finite element method for displacement and stress monitoring of multilayered composite and sandwich structures. Compos. Struct. 179, 514–540 (2017). https://doi.org/10.1016/j.compstruct.2017.07.078

    Article  Google Scholar 

  84. Nguyen-Van, H., Mai-Duy, N., Karunasena, W., Tran-Cong, T.: Buckling and vibration analysis of laminated composite plate/shell structures via a smoothed quadrilateral flat shell element with in-plane rotations. Comput. Struct. 89, 612–625 (2011). https://doi.org/10.1016/j.compstruc.2011.01.005

    Article  Google Scholar 

  85. Figueiras, JDA.: Ultimate load analysis of anisotropic and reinforced concrete plates and shells, Swansea University (1983)

  86. Ünlüsoy, L.: Structural design and analysis of the mission adaptive wings of an unmanned aerial vehicle (2010). https://open.metu.edu.tr/handle/11511/19160. Accessed 7 Aug 2023

  87. ASM Material Data Sheet, (n.d.).: https://asm.matweb.com/search/SpecificMaterial.asp?bassnum=ma2024t4&fbclid=IwAR0odxg-Qs101Xpr5YVA-Xj6mpvcyYP5Nn5uCu_uK77yQy4fIIz8-hobhbw. Accessed 7 Aug 2023

  88. Narayanan, R.M.: microwave nondestructive testing of galvanic corrosion and impact damage in carbon fiber reinforced polymer composites. Int. J. Microwaves Appl. 7, 1–15 (2018). https://doi.org/10.30534/IJMA/2018/01712018

    Article  Google Scholar 

  89. Lerner, E., Markowitz, J.: An efficient structural resizing procedure for meeting static aeroelastic design objectives. J. Aircr. 16, 65–71 (1979). https://doi.org/10.2514/3.58486

    Article  Google Scholar 

  90. Fazelzadeh, S.A., Marzocca, P., Mazidi, A., Rashidi, E.: Divergence and flutter of shear deformable aircraft swept wings subjected to roll angular velocity. Acta Mech. 212, 151–165 (2010). https://doi.org/10.1007/S00707-009-0248-2/METRICS

    Article  Google Scholar 

Download references

Acknowledgements

This work (Grant No. RGNS 63-060) was financially supported by Office of the Permanent Secretary, Ministry of Higher Education, Science, Research and Innovation.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering, Mahasarakham University, Mahasarakham, Thailand

    Kittinan Wansasueb

  2. Sustainable and Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, 40002, Thailand

    Natee Panagant, Sujin Bureerat, Numchoak Sabangban & Nantiwat Pholdee

Authors
  1. Kittinan Wansasueb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Natee Panagant
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sujin Bureerat
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Numchoak Sabangban
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nantiwat Pholdee
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, NP and SB; Investigation, NP and KW; Writing—original draft, KW and NS; Writing—review & editing, NP and SB; Funding Acquisition, NP; Supervision SB and NP.

Corresponding author

Correspondence to Natee Panagant.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wansasueb, K., Panagant, N., Bureerat, S. et al. Comparative study of recent metaheuristics for solving a multiobjective transonic aeroelastic optimization of a composite wing. Acta Mech 235, 391–407 (2024). https://doi.org/10.1007/s00707-023-03756-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00707-023-03756-3

Search

Navigation