Improving Initial Aerofoil Geometry Using Aerofoil Particle Swarm Optimisation
Advanced optimisation of the aerofoil wing of a general aircraft is the main subject of this paper. Meta-heuristic optimisation techniques, especially swarm algorithms, were used. Subsequently, a new variant denoted as aerofoil particle swarm optimisation (aPSO) was developed from the original particle swarm optimisation (PSO). A parametric model based on B-spline was used to optimise the initial aerofoil. The simulation software Xfoil was calculating basic aerodynamic features (lift, drag, moment).
Abbott, I. H., and Von Doenhoff, A. E. Theory of wing sections: including a summary of airfoil data. Courier Corporation, 2012.
Anderson, G. R., and Aftosmis, M. J. Adaptive shape parameterization for aerodynamic design. Nat. Aeronaut. Space Admin., Ames Res. Center, Moffett Field, CA, USA, NAS Tech. Rep. NAS-2015-02 (2015).
Derksen, R., and Rogalsky, T. Bezier-parsec: An optimized aerofoil parameterization for design. Advances in engineering software 41, 7-8 (2010), 923–930.
Drela, M., and Youngren, H. Xfoil subsonic airfoil development system. Software Package, available online at http://web.mit. edu/ drela/ Public/ web/ xfoil/ [accessed 2022] (2022).
Gabor, O. S., Koreanschi, A., and Botez, R. M. Low-speed aerodynamic characteristics improvement of atr 42 airfoil using a morphing wing approach. In IECON 2012-38th annual conference on IEEE Industrial Electronics Society (2012), IEEE, pp. 5451–5456.
Hinshaw, T. Analysis and design of a morphing wing tip using multicellular flexible matrix composite adaptive skins. PhD thesis, Virginia Tech, 2009.
Jianghao, W., Chenfang, C., and Yanlai, Z. The changes in structural and flight safety due to flap design of blended-wing-body civil aircraft. Procedia Engineering 17 (2011), 320–327.
Kroo, I. Nonplanar wing concepts for increased aircraft efficiency. VKI lecture series on innovative configurations and advanced concepts for future civil aircraft (2005).
Lampinen, J., and Zelinka, I. Mixed integerdiscrete- continuous optimization by differential evolution. In Proceedings of the 5th international conference on soft computing (1999), vol. 71, p. 76.
Matousek, R., Dobrovsky, L., and Kudela, J. How to start a heuristic? utilizing lower bounds for solving the quadratic assignment problem. International Journal of Industrial Engineering Computations 13, 2 (2022), 151–164.
Matsson, J. E., Voth, J. A., McCain, C. A., and McGraw, C. Aerodynamic performance of the naca 2412 airfoil at low reynolds number. In 2016 ASEE Annual Conference & Exposition (2016).
Muller, J., and Muller, R. Device for continuous and defined change in geometry of airfoil wing shape and curvature, Czech Patent 300 728. Issued 29.7.2009.
Oyama, A., and Fujii, K. Airfoil design optimization for airplane for mars exploration. In J-55, The Third China-Japan-Korea Joint Symposium on Optimization of Structual and Mechanical Systems, CJK-OSM3 (2004), Kanazawa, Ishikawa.
Pankonien, A. M. Smart Material Wing Morphing for Unmanned Aerial Vehicles. PhD thesis, University of Michigan, 2015.
Pedersen, M. E. H. Good parameters for particle swarm optimization. Hvass Lab., Copenhagen, Denmark, Tech. Rep. HL1001 (2010), 1551–3203.
Peerlings, B. A review of aerodynamic flow models, solution methods and solvers and their applicability to aircraft conceptual design. Delft University of Technology: Delft, The Netherlands (2018).
Pluhacek, M., Kazikova, A., Kadavy, T., Viktorin, A., and Senkerik, R. Relation of neighborhood size and diversity loss rate in particle swarm optimization with ring topology. 74–79.
Saleem, A., and Kim, M.-H. Aerodynamic performance optimization of an airfoil-based airborne wind turbine using genetic algorithm. Energy 203 (2020), 117841.
Shi, Y., Mader, C. A., He, S., Halila, G. L., and Martins, J. R. Natural laminar-flow airfoil optimization design using a discrete adjoint approach. AIAA Journal 58, 11 (2020), 4702–4722.
Skinner, S. N., and Zare-Behtash, H. Stateof- the-art in aerodynamic shape optimisation methods. Applied Soft Computing 62 (2018), 933–962.
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