Aeroelastic Multi-objective Optimisation of an Aircraft Wing Taking into Account Gust Alleviation
Keywords:
Flutter speed, Gust response, OptimisationAbstract
This paper presents a study on aeroelastic multi-objective optimization of an aircraft wing taking into account gust alleviation. The design problem is posed to minimize two objective functions including wing mass and reaction moments due to gust loadings. Design variables include the values of the composite ply thickness and orientations of wing skins, ribs and spars whereas design constraints are flutter, stress and bifurcation buckling. Two multi-objective metaheuristic algorithms are used to solve the optimization problem while the Goland wing is used as a model for design demonstration. In structural analysis, the finite element (FE) method is used along with the double lattice method for unsteady aerodynamics. The wing is subject to discrete gust loadings and steady aerodynamic loads. The results of the study reveal that passive gust alleviation can be achieved through the proposed multi-objective optimization problem. MSC Nastran, a program used for structural analysis in engineering or even the wing of an aircraft structure analysis. It is used as a tool for calculating objective functions consisted of minimum of the wing structure weight and a minimum of the maximum bending moment at wing root subjected to design constraints such as flutter, stress and bifurcation buckling. Optimization using the metaheuristic method was used to design Goland wing. Two algorithms, Hybridisation of real-code population-based incremental learning and differential evolution (RPBILDE) and Multiobjective metaheuristic with new concept of parameter adaptation (MM-IPDE), were compared their searching performance for solving this problem. The results are indicated by the Hypervolume value. From the analysis of structure influenced by gust response founds that H9M strongly affect to bending moment at root chord. Optimal results from optimization consisted of objective functions, design variables and constraints of RPBILDE are shown. The results will be designed in next stage, detail design.
References
Babaei, A.R., Setayandeh, M.R., & Farrokhfal, H. (2018). Aircraft robust multidisciplinary design optimization methodology based on fuzzy preference function. Chinese Journal of Aeronautics, 31(12), 1-12.
Beran, P.S., Strganac, T.W., Kim, K., & Nichkawde, C. (2004). Studies of Store-Induced Limit-Cycle Oscillations Using a Model with Full System Nonlinearities,” Nonlinear Dynamics, 37(4), 323–339.
Bordogna, M.T., Macquart, T., Bettebghor, D., & De Breuker, R. (2016). Aeroelastic Optimization of Variable Stiffness Composite Wing with Blending Constraints. 17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, American Institute of Aeronautics and Astronautics.
Caughey, D.A. (2011). Introduction to Aircraft Stability and Control Course Notes for M & AE 5070. Ithaca, New York: Sibley School of Mechanical & Aerospace Engineering Cornell University.
Caverly, R., Forbes, J.R., Danowsky, B.P., & Suh, P.M. (2017). Gust-Load Alleviation of a Flexible Aircraft using a Disturbance Observer. AIAA Guidance, Navigation, and Control Conference, American Institute of Aeronautics and Astronautics, 1-14.
Champasak, P., Panagant, N., Pholdee, N., Bureerat, S., & Yildiz, A.R. (2020). Self-adaptive many-objective meta-heuristic based on decomposition for many-objective conceptual design of a fixed wing unmanned aerial vehicle. Aerospace Science and Technology, 100, 105783.
Champasak, P., Ungdumrongkool, S., Panagant, N., & Bureerat, S. (2019). Aircraft Conceptual Design using Multi-Objective Evolutionary Optimization: Comparative study. Journal of Science and Technology Mahasarakham University, 38(5), 470–478.
Drela, M., & Youngren, H. (n.d.). Aerodynamic and flight-dynamic analysis of rigid aircraft of arbitrary configuration. Retrieved from http://web.mit.edu/drela/Public/web/avl/
Gao, H., He, A., Gao, Z., NA, Y., & DENG, Y. (2019). Flight dynamics characteristics of canard rotor/wing aircraft in helicopter flight mode. Chinese Journal of Aeronautics, 32(7), 1577–1587.
Goland, M. (2021). The Flutter of a Uniform Cantilever Wing,” Journal of Applied Mechanics., 12(4), A197–A208.
Guo, S., De Los Monteros, J. E., & Liu, Y. (2015). Gust Alleviation of a Large Aircraft with a Passive Twist Wingtip. Aerospace, 2(2). 135–154.
Higor, L.S., Gustavo, J.R., Roberto, M.C.N., André, R.D.C., Alexandre, A.G., Mateus, A.A.C., & Thiago, A.M.G. (2021).
A multidisciplinary design optimization for conceptual design of hybrid-electric aircraft. Structural and Multidisciplinary Optimization, 64, 3505–3526.
Hitzel, S.M., & Osterhuber, R. (2017). Enhanced Maneuverability of a Delta-Canard Combat Aircraft by Vortex Flow Control. Journal of Aircraft, 55(3), 1090–1102.
Hönlinger, H., Zimmermann, H., Sensburg, O., & Becker, J. (1994). Structural Aspects of Active Control Technology. AGARD Flight Mechanics Panel Symposium. Turin, Italy.
Hürlimann, F., Kelm, R., Dugas, M., Oltmann, K., & Kress, G. (2011). Mass estimation of transport aircraft wingbox structures with a CAD/CAE-based multidisciplinary process. Aerospace Science and Technology, 15(4), 323–333, 2011.
IJsselmuiden, S.T., Abdalla, M.M., & Gürdal, Z. (2008). Implementation of Strength-Based Failure Criteria in the Lamination Parameter Design Space. AIAA Journal, 46(7), 1826–1834.
Jesus, T., Sohst, M., do Vale, J.L., & Suleman, A. (2021). Surrogate based MDO of a canard configuration aircraft. Structural and Multidisciplinary Optimization, 64(6), 3747–3771.
Kowalski, M., Goraj, Z.J., & Goliszek, B. (2021). The use of FEA and semi-empirical equations for weight estimation of a passenger aircraft. Aircraft Engineering and Aerospace Technology, 93(9), 1412-1420.
Liu, Q., Li, X., Liu, H., & Guo, Z. (2020). Multi-objective metaheuristics for discrete optimization problems: A review of the state-of-the-art. Applied Soft Computing, 93, 106382.
Panagiotou, P., Kaparos, P., Salpingidou, C., & Yakinthos, K. (2016). Aerodynamic design of a MALE UAV. Aerospace Science and Technology, 50, 127–138.
Pholdee, N., & Bureerat, S. (2013). Hybridisation of real-code population-based incremental learning and differential evolution for multiobjective design of trusses. Information Sciences (Ny), 223, 136–152.
Pholdee, N., & Bureerat, S. (2014). Hybrid real-code population-based incremental learning and approximate gradients for multi-objective truss design. Engineering Optimization, 46(8), 1032–1051.
Robert, T.B., Steven, B.J., & Thomas, D.A. (2000). Aeroservoelastic Analysis of the B-2 Bomber. Journal of Aircraft, 37(5), 745-752.
Sattar, D., & Salim, R. (2021). A smart metaheuristic algorithm for solving engineering problems,” Engineering with Computers, 37(3), 2389–2417.
Singh, V., Sharma, S.K., & Vaibhav, S. (2016). Transport Aircraft Conceptual Design Optimization Using Real Coded Genetic Algorithm. International Journal of Aerospace Engineering, (2016), 1-11.
Tsai, S.W., & Wu, E.M. (1971). A General Theory of Strength for Anisotropic Materials. Journal of Composite Material., 5(1), 58–80.
Wansasueb, K., Pholdee, N., Panagant, N., & Bureerat, S. (2020). Multiobjective meta-heuristic with iterative parameter distribution estimation for aeroelastic design of an aircraft wing. Engineering with Computers, 38, 695–713.
Wildschek, A., Maier, R., Hahn, K.-U., Leissling, D., Press, M., & Zach, A. (2009). Flight Test with an Adaptive Feed-Forward Controller for Alleviation of Turbulence Excited Wing Bending Vibrations. AIAA Guidance, Navigation, and Control Conference, American Institute of Aeronautics and Astronautics.
Xu J., & Kroo, I. (2014). Aircraft Design with Active Load Alleviation and Natural Laminar Flow,” Journal of Aircraft, 51(5), 1532–1545.
