Abstract
Over the last century, the growing demand for clean energy has emphasized wind energy as a promising solu-tion to address contemporary energy challenges. Within the realm of wind energy, the wind turbine plays a pivotal role in harnessing the kinetic energy of the wind and converting it into electrical power. Among the various components of the wind turbine system, turbine blades assume a critical role in capturing the wind's kinetic energy and converting it into rotational motion. Consequently, the design of wind turbine blades holds the utmost importance in determining the overall performance and efficiency of the entire wind turbine system. One essential aspect of blade design involves selecting an appropriate airfoil. Throughout history, numerous airfoil profiles have been developed for various applications. Notably, National Advisory Committee for Aeronautics (NACA) and National Renewable Energy Laboratory (NREL) airfoils have been tailored for aircraft and large-scale wind turbine blades, respectively. However, the quest for suitable airfoil types for small-scale wind turbine blades has been ongoing. This study delves into an examination of over 62 distinct NACA and NREL aerofoil types tailored for small horizontal-axis wind turbine blades. Employing specialized software, namely QBlade, specifically designed for modeling and simulating wind turbine blades, the study calculates key parameters such as power output, stress, deformation, and weight for each airfoil. Subsequently, based on the simulated data, the optimal airfoil is identified using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria selection approach. This selection process takes into account simulation results pertaining to power output, stress, deformation, and weight. The decision-making process involving multiple criteria is facilitated using Excel and Python. The findings of this study reveal that among the 62 airfoil types under consideration, the NACA 0024, NACA 2424, and NACA 4424 airfoils emerge as the most suitable choices for small horizontal-axis wind turbine blades.
References
AirfoilTools (2023, December 4). Airfoil tools. http://airfoiltools.com/
Balioti, V., Tzimopoulos, C., & Evangelides, C. (2018). Multi-criteria decision making using topsis method under fuzzy environment. Proceedings, 2(11), Article 637. https://doi.org/10.3390/proceedings2110637
Batu, T., & Lemu, H. G. (2020). Comparative study of the effect of chord length computation methods in design of wind turbine blade. In Y. Wang, K. Martinsen, T. Yu, & K. Wang, (Eds.), Advanced Manufacturing and Automation IX. IWAMA 2019. Lecture Notes in Electrical Engineering, 634 (pp. 106–115). Springer. https://doi.org/10.1007/978-981-15-2341-0_14
Batu, T., Lemu, H. G., & Sirhabizuh, B. (2020). Study of the performance of natural fiber reinforced compo-sites for wind turbine blade applications. Advances in Science and Technology Research Journal, 14(2), 67–75. https://doi.org/10.12913/22998624/118201
Beig, A. R., & Muyeen, S. M. (2016). Wind energy. In M. H. Rashid (Ed.), Electric renewable energy systems (pp. 60-70). Elsevier Inc. https://doi.org/10.1016/B978-0-12-804448-3.00004-9
Corke, T. C., Nelson, R. C., & Dame, N. (2015). Wind energy design (1st ed.). CRC Press. https://doi.org/10.1201/b22301
Eker, B., Akdogan, A., & Vardar, A. (2006). Using of composite material in wind turbine blades. Journal of Applied Sciences, 6(14), 2917–2921. https://doi.org/10.3923/jas.2006.2917.2921
Fu, C. (2008). Extended TOPSISs for belief group decision making. Journal of Service Science and Manage-ment, 1, 11–20. https://doi.org/10.4236/jssm.2008.11002
Gopinath, G. S. S., & Meher, M. V. K. (2018). Electricity a basic need for the human beings. AIP Conference Proceedings, 1992, Article 040024. https://doi.org/10.1063/1.5047989
Hazmoune, M., Lazaroiu, G., Ciupageanu, D. A., & Debbache, M. (2021, March 25-27). Comparative study of airfoil profile effect on the aerodynamic performance of small scale wind turbines. Proceedings of the 12th International Symposium on Advanced Topics in Electrical Engineering ATEE 2021, Bucharest, Romania. https://doi.org/10.1109/ATEE52255.2021.9425211
Hsu, Y., Wu, W., & Chang, Y. (2014). Reliability analysis of wind turbine towers. Procedia Engineering, 79, 218–224. https://doi.org/10.1016/j.proeng.2014.06.334
Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: methods and applications a state-of-the-art survey. Springer-Verlag. https://doi.org/10.1007/978-3-642-48318-9
Islam, M.R., Bashar, L. B., Saha, D. K., & Rafi, N. S. (2019). Comparison and Selection of Airfoils for Small Wind Turbine between NACA and NREL's S series Airfoil Families. International Journal of Research in Electrical, Electronics and Communication Engineering, 4(2), 1-11. https://doi.org/10.5281/zenodo.3520469
Liu, W. (2016). Design and kinetic analysis of wind turbine blade-hub-tower coupled system. Renewable Energy, 94, 547–557. https://doi.org/10.1016/j.renene.2016.03.068
Lotfi, F. H., & Fallahnejad, R. (2010). Imprecise shannon’s entropy and multi attribute decision making. Entro-py, 12(1), 53–62. https://doi.org/10.3390/e12010053
Marten, D., & Wendler, J. (2013). QBLADE: An open source tool for design and simulation of horizontal and vertical axis wind turbines. International Journal of Emerging Technology and Advanced Engineering, 3(3), 264–269.
Noronha, N. P., & Krishna, M. (2021). Aerodynamic performance comparison of airfoils suggested for small horizontal axis wind turbines. Materials Today: Proceedings, 46, 2450-2455. https://doi.org/10.1016/j.matpr.2021.01.359
Okokpujie, I. P., Okonkwo, U. C., Bolu, C. A., Ohunakin, O. S., Agboola, M. G., & Atayero, A. A. (2020). Im-plementation of multi-criteria decision method for selection of suitable material for development of hori-zontal wind turbine blade for sustainable energy generation. Heliyon, 6, Article e03142. https://doi.org/10.1016/j.heliyon.2019.e03142
Osei, E. Y., Opoku, R., Sunnu, A. K., & Adaramola, M. S. (2020). Development of high performance airfoils for application in small wind turbine power generation. Journal of Energy, 2020, Article 9710189. https://doi.org/10.1155/2020/9710189
Papathanasiou, J., & Ploskas, N. (2018). TOPSIS. In J. Papathanasiou, & N. Ploskas, (Eds.), Multiple criteria decision aid. Methods, examples and Python implementations (pp. 1-30). Springer. https://doi.org/10.1007/978-3-319-91648-4_1
Plaisier, M. A., & Smeets, J. B. J. (2016). Object size can influence perceived weight independent of visual esti-mates of the volume of material. Scientific Reports, 5, Article 17719. https://doi.org/10.1038/srep17719
Rehman, S., Khan, S. A., & Alhems, L. M. (2020). Application of topsis approach to multi-criteria selection of wind turbines for on-shore sites. Applied Sciences, 10(21), Article 7595. https://doi.org/10.3390/app10217595
Salgado, V., Troya, C., Moreno, G., & Molina, J. (2016). Airfoil selection methodology for small wind turbines. International Journal of Renewable Energy Research, 6(4), 1410–1415. https://doi.org/10.20508/ijrer.v6i4.4642.g6930
Shahbaz, M., Loganathan, N., Sbia, R., & Afza, T. (2015). The effect of urbanization, affluence and trade openness on energy consumption: A time series analysis in Malaysia. Renewable and Sustainable Energy Reviews, 47, 683–693. https://doi.org/10.1016/j.rser.2015.03.044
Sudarsono, S., Purwanto, P., Soedarsono, J. W., & Munir, B. (2013). Utilization of Albizia wood (Albizia Falcata ) and ramie fibers as wind turbine propeller modification of NACA 4415 standard airfoil. Applied Mechanics and Materials, 391,41-45. https://doi.org/10.4028/www.scientific.net/AMM.391.41
Wang, Q., & Li, D. (2021). A new airfoil design method for wind turbine to improve maximum lift of airfoil. Wind Engineering, 45(6), 1447-1458. https://doi.org/10.1177/0309524X20984428