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Artificial Intelligence and High-Performance Computing in the Context of Particle-Laden Turbulent Flow and Wind Energy

ORAL

Abstract

Leading edge erosion on wind turbine blades is a significant issue that adversely affects the efficiency of wind turbines. Various airborne objects, such as ice, dust, insects, raindrops, and snowflakes, contribute to erosion, with the outer areas of the turbine blades being particularly susceptible due to higher speeds.

To investigate the behavior of particles causing erosion, this study investigates the fundamentals of turbulent particle-laden flow using experiments. Specifically, the stagnation area of the leading edge is measured, to observe particle behavior and erosion effects. A Lagrangian particle tracking technique is employed to gather data pertaining to the inertial particle dynamics and tracer particles, separately.

The study sheds light into the relationship between turbulence intensity, particle sizes, and deformation rates on leading edge erosion. An innovative approach involving deep learning-based models and high-performance computing to predict and model leading-edge erosion using the acquired dataset is proposed. The resulting predictive model can potentially be used to optimize blade surface materials and mitigate erosion effectively.

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Publication: - 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. https://doi.org/10.3390/machines11050566<br>- R. Hassanian, H. Myneni, Á. Helgadóttir, M. Riedel; 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. https://doi.org/10.1063/5.0157897<br>- R. Hassanian, Á. Helgadóttir, L. Bouhlali, M. Riedel; An experiment generates a specified mean strained rate turbulent flow: Dynamics of particles. Physics of Fluids 1 January 2023; 35 (1): 015124. https://doi.org/10.1063/5.0134306<br>- 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. https://doi.org/10.3390/fluids7110344<br>- R. Hassanian, M. Riedel and L. Bouhlali, "The Capability of Recurrent Neural Networks to Predict Turbulence Flow via Spatiotemporal Features," 2022 IEEE 10th Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems (ICCC), Reykjavík, Iceland, 2022, pp. 000335-000338, doi: 10.1109/ICCC202255925.2022.9922754.

Presenters

  • Morris Riedel

    The Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland; Juelich Supercomputing Centre, Germany, The Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Iceland; Juelich Supercomputing Centre, Germany

Authors

  • Morris Riedel

    The Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland; Juelich Supercomputing Centre, Germany, The Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Iceland; Juelich Supercomputing Centre, Germany

  • Ásdís Helgadóttir

    The Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, 102 Reykjavik, Iceland

  • Pedro Costa

    The Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Iceland; Delft University of Technology, The Netherlands

  • Andreas Lintermann

    Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany

  • Andrea Beck

    Institute of Aerodynamics and Gas Dynamics, University of Stuttgart, Stuttgart, Germany

  • Reza Hassanian

    The Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, The Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, 102 Reykjavik, Iceland