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Turbulence: Modeling III

ORAL · X43 · ID: 1761894

Presentations

  • A hidden mechanism of dynamic LES models

    ORAL

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    Presenters

    • Xiaohan Hu

      University of Pennsylvania

    Authors

    • Xiaohan Hu

      University of Pennsylvania

    • Keshav Vedula

      Aerothermal Engineering Group, SpaceX

    • George I Park

      University of Pennsylvania

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  • Modeling the subgrid scale scalar variance: a priori tests and application to supersaturation in cloud turbulence

    ORAL

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    Presenters

    • Scott T Salesky

      University of Oklahoma

    Authors

    • Scott T Salesky

      University of Oklahoma

    • Kendra Gillis

      University of Oklahoma

    • Jesse C Anderson

      Michigan Technological University

    • Ian Hellman

      Michigan Technological University

    • Will Cantrell

      Michigan Technological University

    • Raymond A Shaw

      Michigan Technological University

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  • Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning

    ORAL

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    Presenters

    • Yifeng Tian

      Los Alamos National Laboratory

    Authors

    • Yifeng Tian

      Los Alamos National Laboratory

    • Michael Woodward

      Los Alamos National Labs

    • Mikhail Stepanov

      The University of Arizona

    • Chris L Fryer

      Los Alamos National Laboratory

    • Criston M Hyett

      The University of Arizona

    • Daniel Livescu

      LANL

    • Michael Chertkov

      University of Arizona

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  • Turbulent flow prediction: Lagrangian Particle Tracking-Deep Learning (LPT-DL) based models

    ORAL

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    Publication: - 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, 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>- 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

    Presenters

    • 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

    Authors

    • 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

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

    View abstract →

  • Numerical Multi-Fractal Cascade of Atmospheric Turbulence

    ORAL

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    Presenters

    • Vicente Corral

      University of Texas at El Paso

    Authors

    • Arturo Rodriguez

      University of Texas at El Paso

    • Vicente Corral

      University of Texas at El Paso

    • Piyush Kumar

      University of Texas at El Paso

    • Vinod Kumar

      University of Texas at El Paso

    View abstract →