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Year 2025, Volume: 9 Issue: 2, 241 - 248, 28.06.2025

Emin Burak Özyılmaz , Mustafa Kaya

https://doi.org/10.30518/jav.1612888

Abstract

References

  • Andrés-Pérez, E., González-Juárez, D., Martin-Burgos, M. J., & Carro-Calvo, L. (2019). Constrained Single-Point Aerodynamic Shape Optimization of the DPW-W1 Wing Through Evolutionary Programming and Support Vector Machines (pp. 35–48).
  • Antony, J. (2014). Design of Experiments for Engineers and Scientists: 2nd Edition (2nd ed.). Elsevier.
  • Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). In Springer-Verlag Berlin Heidelberg. Springer-Verlag.
  • Box, G. E. P., & Behnken, D. W. (1960). Some New Three Level Designs for the Study of Quantitative Variables. Technometrics, 2(4), 455–475.
  • Caradonna, F. X., & Tung, C. (1981). Experimental and analytical studies of a model helicopter rotor in hover.
  • Cavazzuti, M. (2013). Optimization Methods: From Theory to Design. In Springer-Verlag Berlin Heidelberg (1st ed., Vol. 53, Issue 9). Springer Berlin Heidelberg.
  • Celi, R. (1999). Recent Applications of Design Optimization to Rotorcraft-A Survey. Journal of Aircraft, 36(1), 176–189.
  • Conlisk, A. T. (1997). Modern Helicopter Aerodynamics. Annual Review of Fluid Mechanics, 29(1), 515–567.
  • Costes, M., Renaud, T., & Rodriguez, B. (2012). Rotorcraft simulations: a challenge for CFD. International Journal of Computational Fluid Dynamics, 26(6–8), 383–405.
  • Ganguli, R. (2004). A Survey of Recent Developments in Rotorcraft Design Optimization. Journal of Aircraft, 41(3), 493–510.
  • Giunta, A., Narducci, R., Burgee, S., Grossman, B., Mason, W., Watson, L., & Haftka, R. (1995, June 19). Variable- complexity response surface aerodynamic design of an HSCT wing. 13th Applied Aerodynamics Conference.
  • Glaz, B., Friedmann, P. P., & Liu, L. (2008). Surrogate based optimization of helicopter rotor blades for vibration reduction in forward flight. Structural and Multidisciplinary Optimization, 35(4), 341–363.
  • Haider, B. A., Sohn, C. H., Won, Y. S., & Koo, Y. M. (2017). Aerodynamically efficient rotor design for hovering agricultural unmanned helicopter. Journal of Applied Fluid Mechanics, 10(5), 1461–1474.
  • Kaya, M. (2019). A CFD based application of support vector regression to determine the optimum smooth twist for wind turbine blades. Sustainability (Switzerland), 11(16).
  • Li, J., Du, X., & Martins, J. R. R. A. (2022). Machine learning in aerodynamic shape optimization. Progress in Aerospace Sciences, 134(February), 100849.
  • Li, J., & Zhang, M. (2021). Data-based approach for wing shape design optimization. Aerospace Science and Technology, 112, 106639.
  • McVeigh, M. A., & McHugh, F. J. (1984). Influence of Tip Shape, Chord, Blade Number, and Airfoil on Advanced Rotor Performance. Journal of the American Helicopter Society, 29(4), 55–62.
  • Newman, S. J. (2007). Principles of Helicopter Aerodynamics – Second edition J.G. Leishmann Cambridge University Press, The Edinburgh Building, Shaftesbury Road, Cambridge, CB2 2RU, UK. 2006. 826pp. Illustrated. £65. ISBN 0-521-85860-7. The Aeronautical Journal, 111(1126), 825–826.
  • Renzoni, P., D’Alascio, A., Kroll, N., Peshkin, D., Hounjet, M. H. L., Boniface, J. C., Vigevano, L., Allen, C. B., Badcock, K., Mottura, L., Schöll, E., & Kokkalis, A. (2000). EROS — a common European Euler code for the analysis of the helicopter rotor flowfield. Progress in Aerospace Sciences, 36(5–6), 437–485.
  • Sun, G., Sun, Y., & Wang, S. (2015). Artificial neural network based inverse design: Airfoils and wings. Aerospace Science and Technology, 42, 415–428.
  • Sun, H., & Lee, S. (2005). Response surface approach to aerodynamic optimization design of helicopter rotor blade. International Journal for Numerical Methods in Engineering, 64(1), 125–142.
  • Vu, N. A., & Lee, J. W. (2015). Aerodynamic design optimization of helicopter rotor blades including airfoil shape for forward flight. Aerospace Science and Technology, 42, 106–117.
  • Wang, Q., & Zhao, Q. (2020). Rotor blade aerodynamic shape optimization based on high-efficient optimization method. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 234(2), 375–387.
  • You, Y., & Jung, S. N. (2017). Optimum active twist input scenario for performance improvement and vibration reduction of a helicopter rotor. Aerospace Science and Technology, 63, 18–32.
  • Zhang, X., Xie, F., Ji, T., Zhu, Z., & Zheng, Y. (2021). Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization. Computer Methods in Applied Mechanics and Engineering, 373, 113485.

A Comparative Metamodel Based Shape Optimization Study for Maximizing Thrust of a Helicopter Rotor Blade Under a Torque Constraint

Year 2025, Volume: 9 Issue: 2, 241 - 248, 28.06.2025

Emin Burak Özyılmaz , Mustafa Kaya

https://doi.org/10.30518/jav.1612888

Abstract

The solution of Reynolds-Averaged Navier-Stokes (RANS) equations is crucial for accurately predicting the aerodynamic loads on helicopter rotor blades. In particular, the computational process required for blade shape optimization, involving numerous RANS solutions, is highly time-consuming. To reduce this computational cost, a recently adopted approach is the use of metamodels, such as machine learning methods. A well-established metamodel is expected to successfully replicate CFD solutions. In this study, different machine learning techniques were employed as metamodels and evaluated based on a series of CFD solutions. The machine learning models aimed to capture the functional relationship between the generated thrust and torque and the twist distribution along the rotor blade. The smooth twist variation was modelled using a 3-knot cubic spline, with five parameters serving as inputs for the spline definition. The optimal twist distribution was determined concerning a reference helicopter rotor blade, the Caradonna-Tung rotor blade. The optimization scenarios were defined to maximize thrust force while maintaining the baseline torque value. The optimal cases were identified using the Quadratic Response Surface Method, Support Vector Regression, and Artificial Neural Network Regression. As a result of this study, a significant increase in the thrust force generated by the helicopter rotor blade was observed.

Keywords

Machine Learning Optimization Aerodynamics CFD

References

  • Andrés-Pérez, E., González-Juárez, D., Martin-Burgos, M. J., & Carro-Calvo, L. (2019). Constrained Single-Point Aerodynamic Shape Optimization of the DPW-W1 Wing Through Evolutionary Programming and Support Vector Machines (pp. 35–48).
  • Antony, J. (2014). Design of Experiments for Engineers and Scientists: 2nd Edition (2nd ed.). Elsevier.
  • Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). In Springer-Verlag Berlin Heidelberg. Springer-Verlag.
  • Box, G. E. P., & Behnken, D. W. (1960). Some New Three Level Designs for the Study of Quantitative Variables. Technometrics, 2(4), 455–475.
  • Caradonna, F. X., & Tung, C. (1981). Experimental and analytical studies of a model helicopter rotor in hover.
  • Cavazzuti, M. (2013). Optimization Methods: From Theory to Design. In Springer-Verlag Berlin Heidelberg (1st ed., Vol. 53, Issue 9). Springer Berlin Heidelberg.
  • Celi, R. (1999). Recent Applications of Design Optimization to Rotorcraft-A Survey. Journal of Aircraft, 36(1), 176–189.
  • Conlisk, A. T. (1997). Modern Helicopter Aerodynamics. Annual Review of Fluid Mechanics, 29(1), 515–567.
  • Costes, M., Renaud, T., & Rodriguez, B. (2012). Rotorcraft simulations: a challenge for CFD. International Journal of Computational Fluid Dynamics, 26(6–8), 383–405.
  • Ganguli, R. (2004). A Survey of Recent Developments in Rotorcraft Design Optimization. Journal of Aircraft, 41(3), 493–510.
  • Giunta, A., Narducci, R., Burgee, S., Grossman, B., Mason, W., Watson, L., & Haftka, R. (1995, June 19). Variable- complexity response surface aerodynamic design of an HSCT wing. 13th Applied Aerodynamics Conference.
  • Glaz, B., Friedmann, P. P., & Liu, L. (2008). Surrogate based optimization of helicopter rotor blades for vibration reduction in forward flight. Structural and Multidisciplinary Optimization, 35(4), 341–363.
  • Haider, B. A., Sohn, C. H., Won, Y. S., & Koo, Y. M. (2017). Aerodynamically efficient rotor design for hovering agricultural unmanned helicopter. Journal of Applied Fluid Mechanics, 10(5), 1461–1474.
  • Kaya, M. (2019). A CFD based application of support vector regression to determine the optimum smooth twist for wind turbine blades. Sustainability (Switzerland), 11(16).
  • Li, J., Du, X., & Martins, J. R. R. A. (2022). Machine learning in aerodynamic shape optimization. Progress in Aerospace Sciences, 134(February), 100849.
  • Li, J., & Zhang, M. (2021). Data-based approach for wing shape design optimization. Aerospace Science and Technology, 112, 106639.
  • McVeigh, M. A., & McHugh, F. J. (1984). Influence of Tip Shape, Chord, Blade Number, and Airfoil on Advanced Rotor Performance. Journal of the American Helicopter Society, 29(4), 55–62.
  • Newman, S. J. (2007). Principles of Helicopter Aerodynamics – Second edition J.G. Leishmann Cambridge University Press, The Edinburgh Building, Shaftesbury Road, Cambridge, CB2 2RU, UK. 2006. 826pp. Illustrated. £65. ISBN 0-521-85860-7. The Aeronautical Journal, 111(1126), 825–826.
  • Renzoni, P., D’Alascio, A., Kroll, N., Peshkin, D., Hounjet, M. H. L., Boniface, J. C., Vigevano, L., Allen, C. B., Badcock, K., Mottura, L., Schöll, E., & Kokkalis, A. (2000). EROS — a common European Euler code for the analysis of the helicopter rotor flowfield. Progress in Aerospace Sciences, 36(5–6), 437–485.
  • Sun, G., Sun, Y., & Wang, S. (2015). Artificial neural network based inverse design: Airfoils and wings. Aerospace Science and Technology, 42, 415–428.
  • Sun, H., & Lee, S. (2005). Response surface approach to aerodynamic optimization design of helicopter rotor blade. International Journal for Numerical Methods in Engineering, 64(1), 125–142.
  • Vu, N. A., & Lee, J. W. (2015). Aerodynamic design optimization of helicopter rotor blades including airfoil shape for forward flight. Aerospace Science and Technology, 42, 106–117.
  • Wang, Q., & Zhao, Q. (2020). Rotor blade aerodynamic shape optimization based on high-efficient optimization method. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 234(2), 375–387.
  • You, Y., & Jung, S. N. (2017). Optimum active twist input scenario for performance improvement and vibration reduction of a helicopter rotor. Aerospace Science and Technology, 63, 18–32.
  • Zhang, X., Xie, F., Ji, T., Zhu, Z., & Zheng, Y. (2021). Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization. Computer Methods in Applied Mechanics and Engineering, 373, 113485.
There are 25 citations in total.

Details

Primary Language English
Subjects Aerospace Engineering (Other)
Journal Section Research Articles
Authors

Emin Burak Özyılmaz 0000-0003-2134-7778

Mustafa Kaya 0000-0002-2542-0795

Publication Date June 28, 2025
Submission Date January 3, 2025
Acceptance Date May 14, 2025
Published in Issue Year 2025 Volume: 9 Issue: 2

Cite

APA Özyılmaz, E. B., & Kaya, M. (2025). A Comparative Metamodel Based Shape Optimization Study for Maximizing Thrust of a Helicopter Rotor Blade Under a Torque Constraint. Journal of Aviation, 9(2), 241-248. https://doi.org/10.30518/jav.1612888
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