Wind Turbine Blade Dynamics Simulation under the Effect of Atmospheric Turbulence

Amr Ismaiel


Wind energy is one of the fastest growing sources of renewable energy because of its cleanliness and sustainability. Due to the turbulent nature of wind, a wind turbine experiences severe dynamic loading and faces the danger of fatigue failure. In addition, severe blade deflections imply failure by tower strikes. For this reason, the study of blade deflections under different turbulence conditions is of high importance. In this work, a wind turbine’s blade is simulated under different turbulent conditions. Four different wind fields are generated with a mean wind velocity of 12 m/s and turbulence intensities of 1, 10, 25, and 50%. The blade deflections are calculated in the out-of-plane and in-plane directions as a time-marching series with different blade azimuth positions. The higher the turbulence intensity, the severer the fluctuations of the deflections around its mean value. For the 50% turbulence intensity, the standard deviation of the out-of-plane deflection is 600% larger than that of the 1% turbulence intensity case. The maximum deflections increase significantly as well. A maximum of 3.78 m of out-of-plane tip deflection leads to the danger of a tower strike. And a positive tip deflection of 0.07 m in the in-plane direction indicates that the blade goes against its natural behavior and against the inertial loads while rotating. Continuous monitoring of wind conditions is a must, to put the turbine on brake in cases of gusts and severe turbulence. In areas of high turbulence, downwind turbines can provide a better alternative to allow blade deflections without the danger of tower strikes.


Doi: 10.28991/ESJ-2023-07-01-012

Full Text: PDF


Renewable Energy; Structural Dynamics; Turbulence; Wind Turbines; Wind Energy.


Marashli, A., Gasaymeh, A. M., & Shalby, M. (2022). Comparing the Global Warming Impact from Wind, Solar Energy, and Other Electricity Generating Systems through Life Cycle Assessment Methods (A Survey). International Journal of Renewable Energy Research, 12(2), 899–920. doi:10.20508/ijrer.v12i2.13010.g8474.

Gao, D., Kwan, T. H., Dabwan, Y. N., Hu, M., Hao, Y., Zhang, T., & Pei, G. (2022). Seasonal-regulatable energy systems design and optimization for solar energy year-round utilization☆. Applied Energy, 322, 119500. doi:10.1016/j.apenergy.2022.119500.

Gkousis, S., Welkenhuysen, K., & Compernolle, T. (2022). Deep geothermal energy extraction, a review on environmental hotspots with focus on geo-technical site conditions. Renewable and Sustainable Energy Reviews, 162, 112430. doi:10.1016/j.rser.2022.112430.

Msigwa, G., Ighalo, J. O., & Yap, P. S. (2022). Considerations on environmental, economic, and energy impacts of wind energy generation: Projections towards sustainability initiatives. Science of the Total Environment, 849, 157755. doi:10.1016/j.scitotenv.2022.157755.

Shetty, C., & Priyam, A. (2022). A review on tidal energy technologies. Materials Today: Proceedings, 56(5), 2774–2779. doi:10.1016/j.matpr.2021.10.020.

Foteinis, S. (2022). Wave energy converters in low energy seas: Current state and opportunities. Renewable and Sustainable Energy Reviews, 162, 112448. doi:10.1016/j.rser.2022.112448.

Hakan Açıkel, H., & Bayır, E. (2022). Evaluation of capacity of hybrid energy systems to decrease the environmental pollution. Fuel, 328, 125356. doi:10.1016/j.fuel.2022.125356.

Farhat, O., Khaled, M., Faraj, J., Hachem, F., Taher, R., & Castelain, C. (2022). A short recent review on hybrid energy systems: Critical analysis and recommendations. Energy Reports, 8(9), 792–802. doi:10.1016/j.egyr.2022.07.091.

Bansal, A. K. (2022). Sizing and forecasting techniques in photovoltaic-wind based hybrid renewable energy system: A review. Journal of Cleaner Production, 369, 133376. doi:10.1016/j.jclepro.2022.133376.

Tahiri, F. E., Chikh, K., & Khafallah, M. (2021). Optimal management energy system and control strategies for isolated hybrid solar-wind-battery-diesel power system. Emerging Science Journal, 5(2), 111–124. doi:10.28991/esj-2021-01262.

Global Wind Energy Council (GWEC). (2021). GWEC global wind report. Global Wind Energy Council (GWEC), Brussels, Belgium.

Dief, T. N., Fechner, U., Schmehl, R., Yoshida, S., Ismaiel, A. M. M., & Halawa, A. M. (2018). System identification, fuzzy control and simulation of a kite power system with fixed tether length. Wind Energy Science, 3(1), 275–291. doi:10.5194/wes-3-275-2018.

Eijkelhof, D., & Schmehl, R. (2022). Six-degrees-of-freedom simulation model for future multi-megawatt airborne wind energy systems. Renewable Energy, 196, 137–150. doi:10.1016/j.renene.2022.06.094.

Francis, S., Umesh, V., & Shivakumar, S. (2021). Design and Analysis of Vortex Bladeless Wind Turbine. Materials Today: Proceedings, 47(16), 5584–5588. doi:10.1016/j.matpr.2021.03.469.

Aher, S., Chavan, P., Deshmukh, R., Pawar, V., & Thakre, M. (2021). Designing and software realization of an ANN-based MPPT-Fed bladeless wind power generation. Global Transitions Proceedings, 2(2), 584–588. doi:10.1016/j.gltp.2021.08.054.

Abuhashish, M. N., Daoud, A. A., & Elfar, M. H. (2022). A Novel Model Predictive Speed Controller for PMSG in Wind Energy Systems. International Journal of Renewable Energy Research, 12(1), 170–180. doi:10.20508/ijrer.v12i1.12750.g8385.

López-Queija, J., Robles, E., Jugo, J., & Alonso-Quesada, S. (2022). Review of control technologies for floating offshore wind turbines. Renewable and Sustainable Energy Reviews, 167, 112787. doi:10.1016/j.rser.2022.112787.

Song, W., Liu, Y., Wang, Z., Ding, S., Lin, X., Feng, Z., & Li, Z. (2022). A novel wind turbine control strategy to maximize load capacity in severe wind conditions. Energy Reports, 8, 7773–7779. doi:10.1016/j.egyr.2022.06.005.

Jiang, S. J., Chu, S. C., Zou, F. M., Shan, J., Zheng, S. G., & Pan, J. S. (2023). A parallel Archimedes optimization algorithm based on Taguchi method for application in the control of variable pitch wind turbine. Mathematics and Computers in Simulation, 203, 306–327. doi:10.1016/j.matcom.2022.06.027.

Aboelezz, A., Ghali, H., Elbayomi, G., & Madboli, M. (2022). A novel VAWT passive flow control numerical and experimental investigations: Guided Vane Airfoil Wind Turbine. Ocean Engineering, 257, 111704. doi:10.1016/j.oceaneng.2022.111704.

Xu, W., Li, C. cheng, Huang, S. xian, & Wang, Y. (2022). Aerodynamic performance improvement analysis of Savonius vertical axis wind turbine utilizing plasma excitation flow control. Energy, 239, 122133. doi:10.1016/

Mostafa, W., Abdelsamie, A., Sedrak, M., Thévenin, D., & Mohamed, M. H. (2022). Quantitative impact of a micro-cylinder as a passive flow control on a horizontal axis wind turbine performance. Energy, 244, 122654. doi:10.1016/

ShobanaDevi, A., Maragatham, G., Prabu, M. R., & Boopathi, K. (2021). Short-Term Wind Power Forecasting Using RLSTM. International Journal of Renewable Energy Research, 11(1), 392–406. doi:10.20508/ijrer.v11i1.11807.g8144.

Wang, C., Zhang, S., Liao, P., & Fu, T. (2022). Wind speed forecasting based on hybrid model with model selection and wind energy conversion. Renewable Energy, 196, 763–781. doi:10.1016/j.renene.2022.06.143.

Strickland, J. M. I., & Stevens, R. J. A. M. (2022). Investigating wind farm blockage in a neutral boundary layer using large-eddy simulations. European Journal of Mechanics, B/Fluids, 95, 303–314. doi:10.1016/j.euromechflu.2022.05.004.

Cao, L., Ge, M., Gao, X., Du, B., Li, B., Huang, Z., & Liu, Y. (2022). Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines. Applied Energy, 323, 119599. doi:10.1016/j.apenergy.2022.119599.

Rubert, T., Zorzi, G., Fusiek, G., Niewczas, P., McMillan, D., McAlorum, J., & Perry, M. (2019). Wind turbine lifetime extension decision-making based on structural health monitoring. Renewable Energy, 143, 611–621. doi:10.1016/j.renene.2019.05.034.

Heinonen, J., & Rissanen, S. (2017). Coupled-crushing analysis of a sea ice-wind turbine interaction–feasibility study of FAST simulation software. Ships and Offshore Structures, 12(8), 1056–1063. doi:10.1080/17445302.2017.1308782.

Haselibozchaloee, D., Correia, J., Mendes, P., de Jesus, A., & Berto, F. (2022). A review of fatigue damage assessment in offshore wind turbine support structure. International Journal of Fatigue, 164, 107145. doi:10.1016/j.ijfatigue.2022.107145.

Chanprasert, W., Sharma, R. N., Cater, J. E., & Norris, S. E. (2022). Large Eddy Simulation of wind turbine fatigue loading and yaw dynamics induced by wake turbulence. Renewable Energy, 190, 208–222. doi:10.1016/j.renene.2022.03.097.

Gao, J., Sweetman, B., & Tang, S. (2022). Multiaxial fatigue assessment of floating offshore wind turbine blades operating on compliant floating platforms. Ocean Engineering, 261, 111921. doi:10.1016/j.oceaneng.2022.111921.

Martín del Campo, J. O., & Pozos-Estrada, A. (2022). A simplified method for structural and fatigue analyses of wind turbine support structures. Journal of Wind Engineering and Industrial Aerodynamics, 224, 104983. doi:10.1016/j.jweia.2022.104983.

Katsikogiannis, G., Hegseth, J. M., & Bachynski-Polić, E. E. (2022). Application of a lumping method for fatigue design of monopile-based wind turbines using fully coupled and simplified models. Applied Ocean Research, 120, 102998. doi:10.1016/j.apor.2021.102998.

Rincón-Casado, A., Juliá-Lerma, J. M., García-Vallejo, D., & Domínguez, J. (2022). Experimental estimation of the residual fatigue life of in-service wind turbine bolts. Engineering Failure Analysis, 141, 106658. doi:10.1016/j.engfailanal.2022.106658.

Ismaiel, A. M. M., & Yoshida, S. (2018). Study of turbulence intensity effect on the fatigue lifetime of wind turbines. Evergreen, 5(1), 25–32. doi:10.5109/1929727.

Hansen, K. S., Barthelmie, R. J., Jensen, L. E., & Sommer, A. (2012). The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm. Wind Energy, 15(1), 183–196. doi:10.1002/we.512.

Chamorro, L. P., & Porté-Agel, F. (2009). A Wind-Tunnel Investigation of Wind-Turbine Wakes: Boundary-Layer Turbulence Effects. Boundary-Layer Meteorology, 132(1), 129–149. doi:10.1007/s10546-009-9380-8.

Bardal, L. M., & Sætran, L. R. (2017). Influence of turbulence intensity on wind turbine power curves. Energy Procedia, 137, 553–558. doi:10.1016/j.egypro.2017.10.384.

Siddiqui, M. S., Rasheed, A., Kvamsdal, T., & Tabib, M. (2015). Effect of turbulence intensity on the performance of an offshore vertical axis wind turbine. Energy Procedia, 80, 312–320. doi:10.1016/j.egypro.2015.11.435.

Griffin, D. A. (2001). Windpact turbine design scaling studies technical area 1-composite blades for 80-to 120-meter rotor (No. NREL/SR-500-29492). National Renewable Energy Lab (NREL), Golden City, United States. doi:10.2172/783406.

Khazem, E. A. Z., Abdullah, O. I., & Sabri, L. A. (2019). Steady-state and vibration analysis of a WindPact 1.5-MW turbine blade. FME Transactions, 47(1), 195–201. doi:10.5937/fmet1901195K.

Burton, T., Jenkins, N., Sharpe, D., & Bossanyi, E. (2011). Wind Energy Handbook. John Wiley & Sons, Hoboken, United States. doi:10.1002/9781119992714.

Jonkman, B. J., & Kilcher, L. TurbSim User’s Guide: Version 1.06.00. Technical Report, National Renewable Energy Laboratory (NREL), A National Laboratory of the U. S. Department of energy, Office of Energy Efficiency & Renewable Energy, Golden City, United States.

Jonkman, J. M., & Buhl Jr, M. L. (2005). Fast user’s guide. National Renewable Energy Laboratory, Golden. Technical Report No. NREL/EL-500-38230, , National Renewable Energy Laboratory (NREL), A National Laboratory of the U. S. Department of energy, Office of Energy Efficiency & Renewable Energy, Golden City, United States.

Full Text: PDF

DOI: 10.28991/ESJ-2023-07-01-012


  • There are currently no refbacks.

Copyright (c) 2022 Amr Ismaiel