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Inverse and direct optimization shape of airfoil using hybrid algorithm Big Bang-Big Crunch and Particle Swarm Optimization

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, Big Bang-Big Crunch and Particle Swarm Optimization algorithms are combined and used for the first time to optimize airfoil geometry as a aerodynamic cross section. The optimization process is carried out both in reverse and direct directions. In the reverse approach, the object function is the difference between pressure coefficients of the optimized and target airfoils, which must be minimized. In the direct approach, three objective functions are introduced, the first of which is the drag to lift (D/L) ratio. It is minimized considering four different initial geometries, ultimately, all four geometries converge to the same final geometry. In other cases, maximizing lift the coefficient with the fixed drag coefficient constraint and minimizing the drag coefficient while the lift coefficient is fixed are defined as purposes. The results show that by changing the design parameters of the initial airfoil geometry, the proposed hybrid optimization algorithm as a powerful method satisfies the needs with proper accuracy and finally reaches the desired geometry.
Rocznik
Strony
697--711
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
  • Engineering Department, Khoy, Iran
  • Azad University, Ajabshir, Iran
Bibliografia
  • 1. Alves R., Goncalves L., Aguiar J., Brasil A.C.P. Jr., 2016, Parsec parameterization methodology for enhancing airfoils geometry using PSO algorithm, CILAMCE 2016, Proceedings of the XXXVII Iberian Latin-American Congress on Computational Methods in Engineering, Suzana Moreira Avila (Editor), ABMEC, DF, Brazil, 6-9
  • 2. Ebrahimi M., Jahangirian A.R., 2017, Airfoil shape optimization with adaptive mutation genetic algorithm, Journal of Agricultural Science and Technology (JAST), 11, 1, 47-59
  • 3. Endo M., 2011, Wind turbine airfoil optimization by particle swarm method, M.SC. Thesis, Case Western Reserve University
  • 4. Erol O., Eksin I., 2006, A new optimization method: big bang-big crunch, Advances in Engineering Software, 37, 106-111
  • 5. Giannakoglou K.C., 2002, Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence, Progress in Aerospace Sciences, 38, 43-76
  • 6. Hajek J., 2009, Aerodynamic optimization of airfoils and wings using fast solvers, Ph.D. Thesis, Charles University, Prague
  • 7. Jahangirian A., Shahrokhi A., 2009, Inverse design of transonic airfoils using genetic algorithm and a new parametric shape method, Inverse Problems in Science and Engineering, 17, 5, 681-699
  • 8. Jameson A., Schmidt W., Turkel E., 1981, Numerical solution of the Euler equations by finite volume methods using Runge-Kutta time-stepping schemes, AIAA Meeting Paper, DOI: 10.2514/6.1981-1259
  • 9. Kaveh A., Talatahari S., 2010a, A discrete Big Bang-Big Crunch algorithm for optimal design of skeletal structures, Asian Journal of Civil Engeneering (Building and Housing), 1, 103-122
  • 10. Kaveh A., Talatahari S., 2010b, Optimal design of Schwedler and ribbed domes via hybrid Big Bang-Big Crunch algorithm, Journal of Constructional Steel Research, 66, 412-419
  • 11. Kennedy J., Eberhart R.C., 1995, Particle swarm optimization, Proceedings of the IEEE International Conference on Neural Networks, 4, 1942-1948
  • 12. Khurana M.S., 2008, Application of an hybrid optimization approach in the design of long endurance airfoils, 26th Congress of International Council of the Aeronautical Sciences 2008, ICAS 2008
  • 13. Kumbasar T., Eksin I., Guzelkaya M., Yes¸il E. ¨ , 2008, Big Bang Big Crunch Optimization Method Based Fuzzy Model Inversion, Springer-Verlag Berlin Heidelberg, 732-740
  • 14. Lane K., Marshall D., 2010, Inverse airfoil design utilizing CST parameterization, 48th AIAA Aerospace Sciences Meeting Including The New Horizons Forum and Aerospace Exposition
  • 15. Lee K.S., Geem Z.W., 2005, A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice, Computer Methods in Applied Mechanics and Engineering, 194, 3902-3933
  • 16. Xu Z.H., Xia J., 2016, Aerodynamic optimization based on continuous adjoint method for a flexible wing, International Journal of Aerospace Engineering, Article ID 4706925
  • 17. Wauquiez C., 2000, Shape optimization of low speed airfoils using MATLAB and automatic differentiation, Licentiate’s Thesis
  • 18. Wickramasinghe U.K., Carrese R., Li X., 2010, Designing airfoils using a reference point based evolutionary many objective Particle Swarm Optimization algorithm, WCCI 2010, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 1857-1864
  • 19. Zhang Z., Lum K.Y., 2006, Airfoil optimization design of drag minimization with lift constraint using adjoint equation method, 44th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, 9-12
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7f7a9e94-58e8-46de-8296-3c41a2782c29
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