Skip to main content
SpringerLink
Log in

Aerodynamic Performance Optimization of Multiple Slat Airfoil based on Multi-Objective Genetic Algorithm

  • Research Article-Mechanical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

In this work, the optimization for the optimum position of the secondary slat was investigated with an emphasis on enhancing the aerodynamic performance. The method adopted here merges the computational fluid dynamic (CFD) technique with the response surface method. The multi-objective genetic algorithm was used for the optimization of the positioning of the secondary slat, and the Pareto ranking was done using a non-dominated sorting method. In CFD analysis, the Reynolds average Nervier–Stokes equation is solved using the k-ω shear stress transport model which is very popular due to its accuracy. The obtained numerical result for the primary airfoil NACA 2415 and the airfoil with a single slat is validated with the experimental data. The NACA 22 airfoil profile is selected to serve as a slat to impediment the separation of boundary layer and enhance airfoil characteristics. The addition of slat at the leading edge of the primary slat increases the overall aerodynamic performance of the configuration and enhances the stall angle from 12º to 22º. Further, the addition of slat significantly reduces the boundary layer thickness as a result delays the separation to a higher angle of attack. The method used in this work can be employed as a valuable tool for positioning optimization of the secondary slat at the leading edge of the primary slat of the airfoil.

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
Fig. 2
Fig. 3
Fig. 4
Figure 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig.12

Similar content being viewed by others

References

  1. Genc, S.; Kayank, U.; Lock, G.D.: Flow over an airfoil without and with a leading edge slat at a transitional Reynolds number. J. Aerosp. Eng. Proc. IMechE 223, 217–231 (2009)

    Article  Google Scholar 

  2. Prandtl I (1928) Motion of fluids with very little viscosity. NACA TN NO 452, Washington, DC.

  3. Fatahian, E.; Lohrasbi Nichkoohi, A.; Salarian, H.; Khaleghinia, J.: Effects of the hinge position and suction on flow separation and aerodynamic performance of the NACA 0012 airfoil. J. Braz. Soc. Mech. Sci. Eng. 42, 86 (2020)

    Article  Google Scholar 

  4. Weik FE, Platt RC, (1993) Wind tunnel tests on a model wing with flower flap and specially developed leading edge slot. NACA TN NO 459 Washington DC.

  5. Kruger W (1947) Wind-Tunnel Investigation on a Changed Mustang Profile with Nose Flap Force and Pressure-Distribution measurements, NACA TN NO 1177. Washington, DC.

  6. Yavuz T, Klks B, Akpnar H, Erol O (2011) Performance analysis of a hydro-foil with and without leading edge slat. In: 10th International Conference on Machine Learning and Applications and Workshops Hawai, pp. 281–285.

  7. Soderman PT (1972) Aerodynamic effects of leading-edge serrations on a two-dimensional airfoil, NASA Technical Note, NASA TM X-2643, A-3706

  8. Yavuz, T.; Koc, E.; Kilkis, B.; Erol, O.; Balas, C.; Aydemir, T.: Performance analysis of the airfoil-slat arrangements for hydro and wind turbine application. Renew Energy 74, 414–421 (2015)

    Article  Google Scholar 

  9. Choudhry, A.; Arjomandi, M.; Kelso, R.: A study of long separation bubble on thick airfoils and its consequent effects. Int. J. Heat Fluid Flow 52, 84–96 (2015)

    Article  Google Scholar 

  10. Aftab, S.M.A.; Rafie, A.M.; Razak, N.A.; Ahmad, K.A.: Turbulence model selection for low Reynolds number flows. PLoS ONE 11(4), e0152755 (2016). https://doi.org/10.1371/journal.pone.0153755

    Article  Google Scholar 

  11. Matyushenko, A.A.; Garbaruk, A.V.: Adjustment of the k-ω SST turbulence model for prediction of airfoil characteristics near stall. J. Phys. Conf. Ser. 769, 012082 (2016)

    Article  Google Scholar 

  12. Zhang, C.; Bounds, C.P.; Foster, L.; Uddin, M.: Turbulence modeling effects on the CFD predictions of flow over a detailed full-scale sedan vehicle. Fluids 4(3), 148 (2019). https://doi.org/10.3390/fluids4030148

    Article  Google Scholar 

  13. Siddiqui, N.A.; Chaab, M.A.: Effect of aspect ratio on the recirculation region of 35° Ahmed body. Aust. J. Mech. Eng. (2020). https://doi.org/10.1080/14484846.2020.1842307

    Article  Google Scholar 

  14. Besnard E, Schmitz A, Boscher E, Garcia N, Cebeci T (1998) Two-Dimensional Aircraft High Lift System Design and Optimization. 36th AIAA Aerospace Sciences Meeting and Exhibit, AIAA Paper 98–0123.

  15. Jureczko, M.; Pawlak, M.; Mezyk, A.: A Optimisation of wind turbine blades. J. Mater Process Technol. 167, 463–471 (2005)

    Article  Google Scholar 

  16. Burger C, Hartfield R, (2006) Wind turbine airfoil performance optimization using the vortex lattice method and a genetic algorithm. In: Proceeding of the 4th AIAA Energy Conversion Conference, San Diego, CA, USA, 26–29 June.

  17. Kenway G, Martins JRRA, (2008) Aerostructural shape optimization of wind turbine blades considering site-specific winds. In: Proceeding of the AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Victoria, BC, Canda, 10–12 Sept.

  18. Li, J.Y.; Li, R.; Gao, Y.; Huang, J.: Aerodynamic optimization of wind turbine airfoils using response surface techniques. Proc. Inst. Mech. Eng. Part A J. Power Energy 224, 827–838 (2010)

    Article  Google Scholar 

  19. Ju, Y.P.; Zhang, C.H.: Multi-point robust design optimization of wind turbine airfoil under geometric uncertainty. Proc. Inst. Mech. Eng. Part A J. Power Energy 226, 245–261 (2012)

    Article  Google Scholar 

  20. Liang, C.; Li, H.: Effects of optimized airfoil on vertical axis wind turbine aerodynamic performance. J. Braz. Soc. Mech. Sci. Eng. 40, 88 (2018)

    Article  Google Scholar 

  21. Reis, C.J.B.; Manzanares-Filho, N.; de Lima, A.M.G.: Robust optimization of aerodynamic loadings for airfoil inverse designs. J Braz Soc. Mech. Sci. Eng. 4, 207 (2019)

    Article  Google Scholar 

  22. Box, G.E.P.: Darper NR. Empirical model building and response surfaces, John Wiley and Sons Inc New York USA (1978)

    Google Scholar 

  23. Box, G.E.P.; Wilson, K.B.: On the experimental attainment of optimum onditions. J. R. Stat. Soc. Ser. B 13, 1–45 (1951)

    MATH  Google Scholar 

  24. Goldberg, D.E.: Genetic algorithm in search, optimization and machine learning, Addison-Wesley. Reading, MA (1989)

    Google Scholar 

  25. Holland, J.: Adaptation in natural and artifical systems. The University of Michigan Press, Ann Arbor MI (1975)

    Google Scholar 

  26. Singh, S.K.; Chowdhury, J.; Ghosh, S.; Kumar, P.K.; Debnath, K.: Kumar P (2019) Experimental and numerical investigation of flow characteristics in an open rectangular cavity. ISH J. Hydraul. Eng. (2019). https://doi.org/10.1080/09715010.2019.1665482

    Article  Google Scholar 

  27. Menter, F.R.: Two-equation eddy-viscosity turbulence models for engineering applications. AIAA J. 32(8), 1598–1605 (1994)

    Article  Google Scholar 

  28. Montoya, M.C.; Nieto, F.; Alvarez, A.J.; Hernandez, S.; Jurado, J.A.: Numerical simulations of the aerodynamic response of circular segments with different corner angles by means of 2D URANS. Impact of turbulence modeling approaches. Eng. Appl. Comput. Fluid Mech. 12(1), 750–779 (2018)

    Google Scholar 

  29. Igali, D.; Mukhmetov, O.; Zhao, Y.; Fok, S.C.; Teh, S.L.: Comparative analysis of turbulence models for automotive aerodynamic simulation and design. Int. J. Automot. Technol. 20(6), 1145–1152 (2019). https://doi.org/10.1007/s12239-019-0107-7

    Article  Google Scholar 

  30. Kumar, P.; Singh, S.K.: Flow past a bluff body subjected to lower subcritical Reynolds number. J. Ocean Eng. Sci. 5, 173–179 (2020)

    Article  Google Scholar 

  31. Eleni, D.C.; Athanasios, T.I.; Dionissios, M.P.: Evaluation of the Turbulence Models for the Simulation of the Flow over an Aerofoil. J Mech Eng Res 4(3), 100–111 (2012)

    Google Scholar 

  32. Sharma, A.; Kumar, P.; Singh, S.K.: Numerical analysis of flow structures behind the bluff body at different aspect ratio. IOP Conf. Ser. Mater. Sci. Eng. (2018). https://doi.org/10.1088/1757-899X/402/1/012056.012056

    Article  Google Scholar 

  33. Anderson Jr, J.D.: Fundamental of aerodynamics, 2nd edn. McGraw-Hill Inc, NY (1991)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 600036, India

    Krishanu Kumar, Pankaj Kumar & Santosh Kumar Singh

Authors
  1. Krishanu Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pankaj Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Santosh Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Santosh Kumar Singh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, K., Kumar, P. & Singh, S.K. Aerodynamic Performance Optimization of Multiple Slat Airfoil based on Multi-Objective Genetic Algorithm. Arab J Sci Eng 46, 7411–7422 (2021). https://doi.org/10.1007/s13369-021-05448-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-05448-3

Keywords

Search

Navigation