Technical Papers
Jan 11, 2022

Optimal Design for the Blunt Trailing-Edge Profile of Wind Turbine Airfoils under Glaze Ice Conditions

Publication: Journal of Engineering Mechanics
Volume 148, Issue 3

Abstract

Glaze ice is more likely to occur on the rotating blade, and greatly decreases the energy utilization efficiency of wind turbines. Moreover, due to its complex and irregular shape, a high-quality grid and more grid cells are needed in aerodynamic calculation. To improve this situation, this study develops a novel multiobjective optimization method for the blunt trailing edge of airfoils under glaze ice conditions. The parametric representation of the asymmetric trailing-edge profile is given by the B-spline function. The aerodynamic coefficients of the airfoils without and with glaze ice are calculated using the computational fluid dynamics (CFD) method and back propagation (BP) neural network, respectively. The update mode of the potential well center of nonoptimal particles is modified by the social learning and the optimal particle position is identified using the Lévy flight and greedy algorithm for quantum particle swarm optimization (QPSO) algorithm. The optimizer based on the improved QPSO algorithm integrated with CFD method and BP network seeks the trailing-edge control parameters maximizing the lift coefficient and lift-drag ratio. The lift and drag coefficients, lift-drag ratios, and pressure contours of the original and optimized airfoils are investigated before and after icing. Significant improvements of the aerodynamic performance are achieved in this process, confirming that the presented method constitutes a valuable tool for the airfoil design of wind turbines operating in icing conditions.

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Data Availability Statement

All data, models, and code generated or used during the study appear in the published article.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 51805369) and the Science and Technology Planning Project of Tianjin (Grant No. 20YDTPJC00820).

References

Allawi, H. M., W. A. Manaseer, and M. A. Shraideh. 2018. “A greedy particle swarm optimization (GPSO) algorithm for testing real-world smart card applications.” Int. J. Software Tools Technol. Trans. 22 (2): 183–194.
Baker, J. P., E. A. Mayda, and C. P. van Dam. 2006. “Experimental analysis of thick blunt trailing-edge wind turbine airfoils.” J. Sol. Energy Eng. 128 (4): 422–431. https://doi.org/10.1115/1.2346701.
Chen, J., X. F. Guo, Y. Xie, and Y. Z. Sun. 2015. “Optimization design method for the large thick wind turbine airfoils with a blunt trailing edge.” J. Harbin Eng. Univ. 36 (7): 970–974.
Chen, X. M., and R. Agarwal. 2010. “Optimization of flatback airfoils for wind turbine blades using a genetic algorithm with an artificial neural network.” In Proc., 48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, 1–13. Reston, VA: American Institute of Aeronautics and Astronautics.
Chen, X. M., and R. Agarwal. 2013. “Optimization of flatback airfoils for wind turbine blades.” Aircr. Eng. Aerosp. Technol. 85 (5): 355–365. https://doi.org/10.1108/AEAT-05-2012-0059.
Fortin, G., and J. Perron. 2009. “Wind turbine icing and de-icing.” In Proc., 47th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, 1–23. Reston, VA: American Institute of Aeronautics and Astronautics.
Fu, Z. G., and L. Shi. 2016. “Aerodynamic performance of wind turbine airfoil under icing conditions.” Acta Energiae Solaris Sinica 37 (3): 609–616.
Ge, M. W., H. Zhang, Y. Wu, and Y. H. Li. 2019. “Effects of leading edge defects on aerodynamic performance of the S809 airfoil.” Energy Convers. Manage. 195 (Sep): 466–479. https://doi.org/10.1016/j.enconman.2019.05.026.
Huang, Z. H. 2014. “Adaptive mutation behavior for quantum particle swarm optimization.” Commun. Comput. Inf. Sci. 472: 187–191.
Huang, Z. X., Y. H. Yu, and D. C. Huang. 2012. “Quantum-behaved particle swarm algorithm with self-adapting adjustment of inertia weight.” J. Shanghai Jiaotong Univ. 46 (2): 228–232.
Li, C., M. N. Shi, Y. Q. Zhou, and E. F. Wang. 2021. “Quantum particle swarm optimization extraction algorithm based on quantum chaos encryption.” Complexity 16–17: 1–21.
Li, P. C., H. Y. Wang, K. P. Song, and E. L. Yang. 2012. “Research on the improvement of quantum potential well-based particle swarm optimization algorithm.” Acta Phys. Sin. 61 (6): 19–28.
Lin, X., B. Feng, and J. Sun. 2008. “Chaos quantum-behaved particle swarm optimization algorithm.” Comput. Eng. Des. 29 (10): 186–188.
Liu, T., L. Jiao, W. Ma, and R. H. Shang. 2017. “Quantum-behaved particle swarm optimization with collaborative attractors for nonlinear numerical problems.” Commun. Nonlinear Sci. Numer. Simul. 44 (Mar): 167–183. https://doi.org/10.1016/j.cnsns.2016.08.001.
Miller, M., K. Lee Slew, and E. Matida. 2016. “The effect of aerodynamic evaluators on the multi-objective optimization of flatback airfoils.” In Vol. 753 of Proc., Journal of Physics: Conf. Series, 1–10. London: IOP Publishing.
Murcia, J. P., and P. Álvaro. 2011. “CFD analysis of blunt trailing edge airfoils obtained with several modification methods.” Revista de Ingeniería 33 (Jan): 14–24. https://doi.org/10.16924/revinge.33.2.
Sayadi, H., and A. R. Shateri. 2013. “Numerical simulation of turbulent flow around an airfoil with blunt trailing edge aerodynamic characteristics modification of this airfoil with base and aerodynamic cavity.” Int. J. Adv. Manuf. Technol. 6 (1): 61–67.
Standish, K. J., and C. P. van Dam. 2003. “Aerodynamic analysis of blunt trailing edge airfoils.” J. Sol. Energy Eng. 125 (4): 479–487. https://doi.org/10.1115/1.1629103.
Sun, J., W. B. Xu, and B. Feng. 2006. “Adaptive parameter control for quantum-behaved particle swarm optimization on individual level.” In Vol. 4 of Proc., IEEE Int. Conf. on Systems, Man and Cybernetics, 3049–3054. New York: IEEE.
Winnemoeller, T., and C. P. van Dam. 2007. “Design and numerical optimization of thick airfoils including blunt trailing edges.” J. Aircr. 44 (1): 232–240.
Xu, H. R., H. Yang, and C. Liu. 2015. “Optimization of enlarging the thickness of airfoil’s trailing edge for wind turbines.” Acta Energiae Solaris Sin. 36 (3): 743–748.
Yang, R., R. N. Li, S. A. Zhang, and D. S. Li. 2010. “Computational analyses on aerodynamic characteristics of flatback wind turbine airfoils.” J. Mech. Eng. 46 (2): 106–110. https://doi.org/10.3901/JME.2010.02.106.
Yoo, H. S., and J. C. Lee. 2015. “Numerical analysis of NACA64-418 airfoil with blunt trailing edge.” Int. J. Aeronaut. Space 16 (4): 493–499.
Yuan, X. P., P. Jin, and G. P. Zhou. 2018. “An improved QPSO algorithm base on social learning and Lévy flights.” Syst. Sci. Control Eng. 6 (3): 364–373. https://doi.org/10.1080/21642583.2019.1566857.
Zhang, G. Y., Y. G. Wu, and W. Gu. 2013. “Quantum-behaved particle swarm optimization algorithm based on elitist learning.” Control Decis. 28 (9): 1341–1348.
Zhang, X., G. G. Wang, H. L. Liu, and W. Li. 2018. “Study of optimization design method for asymmetric blunt trailing-edge airfoil of wind turbine.” J. Eng. Thermophsics 39 (2): 326–334.
Zhang, X., M. J. Zhang, G. G. Wang, W. Li, and J. T. Ruan. 2019. “Optimization design of blunt trailing-edge airfoil under conditions of rough blades.” China Mech. Eng. 30 (6): 728–734.
Zhang, X., X. Y. Zhang, G. G. Wang, and W. Li. 2020. “Effects of blunt trailing-edge optimization on aerodynamic characteristics of NREL phase VI wind turbine blade under rime ice conditions.” J. Vibroeng 22 (5): 1196–1209. https://doi.org/10.21595/jve.2020.21067.

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Go to Journal of Engineering Mechanics
Journal of Engineering Mechanics
Volume 148Issue 3March 2022

History

Received: Aug 3, 2021
Accepted: Nov 20, 2021
Published online: Jan 11, 2022
Published in print: Mar 1, 2022
Discussion open until: Jun 11, 2022

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Authors

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Professor, Tianjin Key Laboratory of Advanced Mechatronics Equipment Technology, Tiangong Univ., Tianjin 300387, China. Email: [email protected]
Master’s Candidate, School of Mechanical Engineering, Tiangong Univ., Tianjin 300387, China. Email: [email protected]
Associate Professor, School of Energy and Safety Engineering, Tianjin Chengjian Univ., Tianjin 300384, China (corresponding author). Email: [email protected]
Xiaoyao Zhang [email protected]
Master’s Candidate, School of Mechanical Engineering, Tiangong Univ., Tianjin 300387, China. Email: [email protected]
Lecturer, School of Engineering, Univ. of Leicester, Leicester LE1 7RH, UK. ORCID: https://orcid.org/0000-0002-3539-5474. Email: [email protected]

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