Leading edge erosion and particle-laden turbulent flow: effects of the turbulence intensity and presence of gravity
Keywords:
Wind turbine, Energy imbalance, leading edge, erosionAbstract
This study presents results from a laboratory 3D turbulent flow experiment simulating a stagnation point at the leading edge of a wind turbine to investigate erosion caused by airborne particles. In a separate experiment, the flow is seeded with tracer and inertial particles, and the Taylor microscale Reynolds number (Reλ) ranges from 100 to 150 and undergoes vertical strain deformation with a mean rate of 8 s-1. The Lagrangian particle tracking (LPT) technique is employed to measure particle motion in the presence of gravity—a factor that has not been thoroughly examined in numerical studies due to its complexity and computational cost. Each experiment, conducted at varying turbulence intensities, is repeated 20 times to ensure statistically robust measurements. The influence of turbulence intensity is analyzed, and results show that increasing turbulence intensity leads to a 19%–23% increase in the erosion ratio. The normalized Reynolds stress indicates that inertial particles experience greater fluctuations than tracers under the same turbulence intensity. Moreover, higher turbulence intensity has a more pronounced effect on tracer particles. Since inertial particles are heavier than tracers, they could be more strongly influenced by gravity.
Downloads
References
[1] I. E. A. (IEA). (2021) Global energy review 2021 [Online] Available:
https://www.iea.org/reports/global-energy-review-2021
[2] G. W. E. Council. (2021) Global wind report 2021 [Online] Available: https://gwec.net/wpcontent/uploads/2021/03/GWEC-Global-Wind-Report2021.pdf
[3] Hassanian, R., Shahinfar, A., Helgadóttir, Á., & Riedel, M. (2024). Optimizing Wind Energy
Production: Leveraging Deep Learning Models Informed with On-Site Data and Assessing
Scalability through HPC. Acta Polytechnica Hungarica, 21(9), 45-56.
[4] N. R. Deevela, T. C. Kandpal, and B. Singh, “A review of renewable energy based power supply
options for telecom towers,” Environment, Development and Sustainability, Vol. 2023.
[5] Hassanian, R., Myneni, H., Helgadóttir, Á. and Riedel, M "Vertical Axis Wind Turbine Powers
Telecom Towers: Green and Clean Configuration," 2023 6th International Conference on
Electrical Engineering and Green Energy (CEEGE), Grimstad, Norway, 2023, pp. 114-118.
[6] Hassanian, R., Helgadóttir, Á. and Riedel, M., "A wake loss model asymmetry induced by the
circulation of a vertical axis wind turbine," 2023 International Conference on Future Energy
Solutions (FES), Vaasa, Finland, 2023, pp. 1-5.
[7] Hassanian, R.; Yeganeh, N.; Riedel, M. Wind Velocity and Forced Heat Transfer Model for
Photovoltaic Module. Fluids 2024, 9, 17.
[8] Schramm, M.; Rahimi, H.; Stoevesandt, B.; Tangager, K. The Influence of Eroded Blades on
Wind Turbine Performance Using Numerical Simulations. Energies 2017, 10, 1420
[9] Hassanian, R.; Riedel, M. Leading-Edge Erosion and Floating Particles: Stagnation Point
Simulation in Particle-Laden Turbulent Flow via Lagrangian Particle Tracking. Machines
2023, 11, 566.
[10] Hassanian, R., Helgadóttir, Á., Bouhlali, L., Riedel, M.,; An experiment generates a specified
mean strained rate turbulent flow: Dynamics of particles. Physics of Fluids 1 January 2023; 35
(1): 015124.
[11] Hassanian, R., Helgadóttir, Á., Gharibi, F., Beck, A., Riedel, M.; Data-driven deep learning
models in particle-laden turbulent flow. Physics of Fluids 1 February 2025; 37 (2): 023348.
[12] Hassanian, R., Myneni, H., Helgadóttir, Á. and Riedel, M; Deciphering the dynamics of
distorted turbulent flows: Lagrangian particle tracking and chaos prediction through
transformer-based deep learning models. Physics of Fluids 1 July 2023; 35 (7): 075118.
[13] Brandt, L. and Coletti, F., “ Particle-laden turbulence: Progress and perspectives,” Annu. Rev.
Fluid Mech. 54, 159–189 (2022)
[14] C.-M. Lee, Á. Gylfason, P. Perlekar, and F. Toschi, “ Inertial particle acceleration in strained
turbulence,” J. Fluid Mech. 785, 31–53 (2015).
[15] M. R. J. Kompenhans, C. E. Willert, F. Scarano, C. J. Kähler, and S. T. Wereley, Particle Image
Velocimetry (Springer International Publishing, 2018).
[16] E. A. Cowen, S. G. Monismith, E. A. Cowen, and S. G. Monismith, “A hybrid digital particle
tracking velocimetry technique,” Exp. Fluids 22, 199–211 (1997).
[17] N. T. Ouellette, H. Xu, and E. Bodenschatz, “A quantitative study of three-dimensional
Lagrangian particle tracking algorithms,” Exp. Fluids 40, 301–313 (2006).
[18] Hassanian, R.; Helgadóttir, Á.; Riedel, M. Deep Learning Forecasts a Strained Turbulent Flow
Velocity Field in Temporal Lagrangian Framework: Comparison of LSTM and GRU. Fluids
2022, 7, 344.
[19] S. B. Pope, Turbulent Flows (Cambridge University Press, 2000).
[20] Hassanian, R.; Aach, M.; Lintermann, A.; Helgadóttir, Á.; Riedel, M. Turbulent Flow
Prediction-Simulation: Strained Flow with Initial Isotropic Condition Using a GRU Model
Trained by an Experimental Lagrangian Framework, with Emphasis on Hyperparameter
Optimization. Fluids 2024, 9, 84.
[21] H. T. John and L. Lumley, A First Course in Turbulence (MIT Press, 1972).
