Skip to main content

Advertisement

Log in

Optimal airfoil design through particle swarm optimization fed by CFD and XFOIL

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

The lift coefficient and Reynolds number are usually the main constraints in the aerodynamic platform design during the design process. This work presents the airfoil shape equations, which achieve the best lift-drag ratio fulfilling specific lift coefficient and Reynolds number targets. The particle swarm optimization method is implemented and coupled with XFOIL and the open-source CFD OpenFOAM to optimize the airfoil shape parameterized by the NACA 4-digit equations. Several Optimizations with lift coefficients from 0.2 to 1.8 and Reynolds numbers from 80,000 to 500,000 are employed to be fitted by polynomial regressions, describing the best airfoil shape given the target lift coefficient and Reynolds number condition. The obtained analytical expressions are helpful for straightforwardly getting the optimal airfoil shape during a design process. The results provide insights into the thickness, maximum camber and its position and their relation to aerodynamic efficiency. The fits from XFOIL and CFD are compared and discussed. A linear relationship was found between the target lift coefficient and the maximum camber of the optimal airfoil, in addition to an impact of the target lift coefficient on the maximum achievable aerodynamic efficiency. Finally, the optimal airfoils are compared against the S1223 and E423 airfoils for low and medium Reynolds, showing better aerodynamic characteristics.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Islam MR, Bashar LB, Saha DK, Rafi N (2019) Comparison and selection of airfoils for small wind turbine between naca and nrel’s s series airfoil families. Int J Res Electr Electron Commun Eng 4:1–12

    Google Scholar 

  2. Lissaman P (1983) Low-reynolds-number airfoils. Annu Rev Fluid Mech 15(1):223–239

    Article  MATH  Google Scholar 

  3. Drela M (1992) Transonic low-reynolds number airfoils. J Aircr 29(6):1106–1113

    Article  Google Scholar 

  4. Bravo-Mosquera PD, Botero-Bolivar L, Acevedo-Giraldo D, Cerón-Muñoz HD (2017) Aerodynamic design analysis of a uav for superficial research of volcanic environments. Aerosp Sci Technol 70:600–614

    Article  Google Scholar 

  5. Bravo-Mosquera PD, Botero-Bolivar L, Acevedo-Giraldo D, Cerón-Muñoz HD, Catalano FM Experimental and computational analysis of a uav for superficial volcano surveillance. In: 31st Congress of the International Council of the Aeronautical Sciences, Belo Horizonte

  6. Hoyos J, Jímenez JH, Echavarría C, Alvarado JP (2021) Airfoil shape optimization: Comparative study of meta-heuristic algorithms, airfoil parameterization methods and reynolds number impact. In: IOP Conference Series: Materials Science and Engineering, vol. 1154, p. 012016. IOP Publishing

  7. Hicks RM, Vanderplaats GN (1975) Application of numerical optimization to the design of low speed airfoils. Technical report

  8. Liebeck RH, Ormsbee AI (1970) Optimization of airfoils for maximum lift. J Aircr 7(5):409–416

    Article  Google Scholar 

  9. Lutz T, Würz W, Wagner S (2001) Numerical optimization and wind-tunnel testing of low reynolds number airfoils. Fixed and flapping wing aerodynamics for micro air vehicle applications, pp. 169–190

  10. Drela M (1988) Low-reynolds-number airfoil design for the mit daedalus prototype-a case study. J Aircr 25(8):724–732

    Article  Google Scholar 

  11. Selig MS, Maughmer MD (1992) Multipoint inverse airfoil design method based on conformal mapping. AIAA J 30(5):1162–1170

    Article  MATH  Google Scholar 

  12. Li W, Huyse L, Padula S (2002) Robust airfoil optimization to achieve drag reduction over a range of mach numbers. Struct Multidiscip Optim 24(1):38–50

    Article  Google Scholar 

  13. Pehlivanoglu YV (2009) Representation method effects on vibrational genetic algorithm in 2-d airfoil design. J Aeronaut Space Technol 4(2):7–13

    Google Scholar 

  14. Derksen R, Rogalsky T (2010) Bezier-parsec: An optimized aerofoil parameterization for design. Adv Eng Softw 41(7–8):923–930

    Article  MATH  Google Scholar 

  15. Della Vecchia P, Daniele E, DAmato E (2014) An airfoil shape optimization technique coupling parsec parameterization and evolutionary algorithm. Aerosp Sci Technol 32(1):103–110. https://doi.org/10.1016/j.ast.2013.11.006

    Article  Google Scholar 

  16. Della Vecchia P, Daniele E, DAmato E (2014) An airfoil shape optimization technique coupling parsec parameterization and evolutionary algorithm. Aerosp Sci Technol 32(1):103–110

    Article  Google Scholar 

  17. Periaux J, Lee D, Gonzalez L, Srinivas K (2009) Fast reconstruction of aerodynamic shapes using evolutionary algorithms and virtual nash strategies in a cfd design environment. J Comput Appl Math 232(1):61–71

    Article  MATH  Google Scholar 

  18. Pehlivanoglu V (2009) Representation method effects on vibrational genetic algorithm in 2-d airfoil design. J Aeronaut Space Technol 4

  19. Periaux J, Lee D, Gonzalez L, Karkenahalli S (2009) Fast reconstruction of aerodynamic shapes using evolutionary algorithms and virtual nash strategies in a cfd design environment. J Comput Appl Math 232:61–71. https://doi.org/10.1016/j.cam.2008.10.037

    Article  MATH  Google Scholar 

  20. Hauschild M, Pelikan M (2011) An introduction and survey of estimation of distribution algorithms. Swarm Evol Comput 1(3):111–128

    Article  Google Scholar 

  21. Das S, Suganthan PN (2010) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31

    Article  Google Scholar 

  22. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE

  23. Günel O (2016) Comparison of cfd and xfoil airfoil analyses for low reynolds number. Int J Energy Appl Technol 3:83–86

    Google Scholar 

  24. Morgado J, Vizinho R, Silvestre MAR, Páscoa JC (2016) Xfoil vs cfd performance predictions for high lift low reynolds number airfoils. Aerosp Sci Technol 52:207–214. https://doi.org/10.1016/j.ast.2016.02.031

    Article  Google Scholar 

  25. Selig MS, Guglielmo JJ (1997) High-lift low reynolds number airfoil design. J Aircr 34(1):72–79

    Article  Google Scholar 

  26. Eastman N, Kenneth E, Pinkerton R (1935) The characteristics of 78 related airfoil sections from tests in the variable-density wind tunnel. NACA report (460)

  27. Drela M (1989) Low Reynolds Number Aerodynamics ed Mueller TJ. Berlin, Heidelberg: Springer

  28. Hernandez J, Crespo A (1987) Aerodynamic calculation of the performance of horizontal axis wind turbines and comparison with experimental results. Wind Engineering, pp. 177–187

  29. Hoyos JD, Jiménez JH, Echavarría C, Alvarado JP, Urrea G (2022) Aircraft propeller design through constrained aero-structural particle swarm optimization. Aerospace. https://doi.org/10.3390/aerospace9030153

    Article  Google Scholar 

  30. Jasak H, Jemcov A, Tukovic Z, et al. (2007) Openfoam: A c++ library for complex physics simulations. In: International Workshop on Coupled Methods in Numerical Dynamics, vol. 1000, pp. 1–20. IUC Dubrovnik Croatia

  31. White F (2003) Advanced fluid dynamics, 5th edn. McGraw Hill, New York

    Google Scholar 

  32. Salim SM, Cheah S (2009) Wall y strategy for dealing with wall-bounded turbulent flows. In: Proceedings of the International Multiconference of Engineers and Computer Scientists 2:2165–2170

  33. Morgado J, Vizinho R, Silvestre M, Páscoa J (2016) Xfoil vs cfd performance predictions for high lift low reynolds number airfoils. Aerosp Sci Technol 52:207–214

    Article  Google Scholar 

  34. Suvanjumrat C (2017) Comparison of turbulence models for flow past naca0015 airfoil using openfoam. Eng J 21(3):207–221

    Article  Google Scholar 

  35. Wang S, Ingham DB, Ma L, Pourkashanian M, Tao Z (2012) Turbulence modeling of deep dynamic stall at relatively low reynolds number. J Fluids Struct 33:191–209

    Article  Google Scholar 

  36. Sheldahl RE, Klimas PC (1981) Aerodynamic characteristics of seven symmetrical airfoil sections through 180-degree angle of attack for use in aerodynamic analysis of vertical axis wind turbines. Technical report, Sandia National Labs., Albuquerque, NM (USA)

  37. Traub LW, Coffman C (2019) Efficient low-reynolds-number airfoils. J Aircr 56(5):1987–2003. https://doi.org/10.2514/1.C035515

    Article  Google Scholar 

  38. Parsopoulos KE, Vrahatis MN (2010) Particle swarm optimization and intelligence: advances and applications

  39. Winslow J, Otsuka H, Govindarajan B (2018) Chopra I (2018) Basic understanding of airfoil characteristics at low reynolds numbers (104–105). J Aircr 55(3):1050–1061. https://doi.org/10.2514/1.C034415

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Camilo Echavarria, Jose D. Hoyos or Gustavo Suarez.

Additional information

Technical Editor: André Cavalieri.

Publisher's Note

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

Appendix A: Polynomial coefficients for optimal airfoil characteristics

Appendix A: Polynomial coefficients for optimal airfoil characteristics

See Table 1

Table 1 The specific polynomial coefficients and the coefficient of determination \(R^2\) for each variable

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

Echavarria, C., Hoyos, J.D., Jimenez, J.H. et al. Optimal airfoil design through particle swarm optimization fed by CFD and XFOIL. J Braz. Soc. Mech. Sci. Eng. 44, 561 (2022). https://doi.org/10.1007/s40430-022-03866-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40430-022-03866-4

Keywords

Navigation