Bulletin of the American Physical Society
76th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 19–21, 2023; Washington, DC
Session X43: Turbulence: Modeling III
8:00 AM–10:10 AM,
Tuesday, November 21, 2023
Room: 207B
Chair: Scott Salesky, University of Oklahoma
Abstract: X43.00007 : Turbulent flow prediction: Lagrangian Particle Tracking-Deep Learning (LPT-DL) based models*
9:18 AM–9:31 AM
Presenter:
Reza Hassanian
(The Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland)
Authors:
Reza Hassanian
(The Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland)
Ásdís Helgadóttir
(The Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland)
Clara M Velte
(Department of Civil and Mechanical Engineering, Technical University of Denmark)
Morris Riedel
(The Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland; Juelich Supercomputing Centre, Germany)
In recent times, the integration of deep learning with High-performance computing has emerged as a promising approach to simulate and predict fluid flow behavior. In this study, we propose a deep learning methodology that combines long short-term variants and Transformer models to forecast the velocity of a strained incompressible turbulent flow. The prediction is based on experimental datasets based on Lagrangian particle tracking technique, specifically focusing on Taylor microscale Reynolds numbers, Reλ ranging from 100 to 500, and vertical mean strain rates 2S of 4 and 8 s-1.
The results obtained from this approach demonstrate remarkable achievements. Nonetheless, further investigations are essential to determine the capability of these models in predicting the duration of flow periods and the range of Reynolds numbers they can accurately handle. By addressing these aspects, we can enhance the reliability and utility of deep learning techniques in turbulent flow prediction.
*This work was performed in the Center of Excellence (CoE) Research on AI and Simulation-Based Engineering at Exascale (RAISE) and the EuroCC 2 projects receiving funding from EU's Horizon 2020 Research and Innovation Framework Programme and European Digital Innovation Hub Iceland (EDIH-IS) under Grant Agreement Nos. 951733, 101101903, and 101083762, respectively.
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