Low-Order Modeling and Machine Learning in Fluid Dynamics: Methods VI

ORAL · X12 · ID: 2665352

Presentations

  • A meshless method to compute the POD and its variants from scattered data

    ORAL

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    Publication: https://arxiv.org/abs/2407.03173

    Presenters

    • Iacopo Tirelli

      Department of Aerospace Engineering, Universidad Carlos III de Madrid, Avda. Universidad 30, Legan´es, 28911, Madrid, Spain.

    Authors

    • Iacopo Tirelli

      Department of Aerospace Engineering, Universidad Carlos III de Madrid, Avda. Universidad 30, Legan´es, 28911, Madrid, Spain.

    • Miguel A Mendez

      Environmental and Applied Fluid Dynamics, von Karman Institute for Fluid Dynamics, Waterloosesteenweg 72, Sint-Genesius-Rode, 1640, Bruxelles, Belgium.

    • Andrea Ianiro

      Universidad Carlos III de Madrid, Department of Aerospace Engineering, Universidad Carlos III de Madrid, Avda. Universidad 30, Legan´es, 28911, Madrid, Spain.

    • Stefano Discetti

      Department of Aerospace Engineering, Universidad Carlos III de Madrid, Avda. Universidad 30, Legan´es, 28911, Madrid, Spain., Universidad Carlos III de Madrid

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  • Data-driven insights into fluid-structure association and energy quantification

    ORAL

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    Publication: Published:
    [1] C.Y. Li, Z. Chen, X. Lin, A.U. Weerasuriya, X. Zhang, Y. Fu, T.K.T. Tse, The linear-time-invariance notion to the Koopman analysis: The architecture, pedagogical rendering, and fluid–structure association, Physics of Fluids 34(12), 125136 (2022).
    [2] C.Y. Li, Z. Chen, T.K.T. Tse, A.U. Weerasuriya, X. Zhang, Y. Fu, X. Lin, The linear-time-invariance notion of the Koopman analysis. Part 2. Dynamic Koopman modes, physics interpretations and phenomenological analysis of the prism wake, Journal of Fluid Mechanics 959, A15 (2023).
    [3] Y. Fu, X. Lin, L. Li, Q. Chu, H. Liu, X. Zheng, C.-H. Liu, Z. Chen, C. Lin, T.K.T. Tse, C.Y. Li, A POD-DMD augmented procedure to isolating dominant flow field features in a street canyon, Physics of Fluids 35(2), 025112 (2023).

    Submitted:
    [1] C.Y. Li, L. Zhang, S. Li, X. Zhang, Z. Chen, Y. Fu, X. Lin, D.Z. Peng, Y. Wang, B. Zhang, L. Zhou, Y. Wang, H. Liu, A.U. Weerasuriya, T.K.T. Tse, Q. Yang, Linear-time invariance notion to Koopman analysis. Part 3. Data-driven quantification of fluid-structure energy transfers, Physics of Fluids (2024)
    [2] X. Lin, D.Z. Peng, T.K.T. Tse, C.Y. Li, Y. Wang, POD-DMD-DFT Augmented Analysis: identify and visualize energy-wise and evolution-wise significant nonlinear flow features, Nonlinear Dynamics (2024)

    Presenters

    • Cruz Y. Li

      School of Civil Engineering, Chongqing University

    Authors

    • Cruz Y. Li

      School of Civil Engineering, Chongqing University

    • Yunlong Wang

      School of Civil Engineering, Liaoning Technical University

    • Shuang Wu

      School of Civil Engineering, Liaoning Technical University

    • Xisheng Lin

      Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology

    • Tim K.T. Tse

      Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology

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  • Flow dynamics from flow field measurements and a Galerkin Model.

    ORAL

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    Presenters

    • Qihong Lorena L Li Hu

      Universidad Carlos III de Madrid

    Authors

    • Qihong Lorena L Li Hu

      Universidad Carlos III de Madrid

    • Patricia García-Caspueñas

      Universidad Carlos III de Madrid

    • Andrea Ianiro

      Universidad Carlos III de Madrid, Department of Aerospace Engineering, Universidad Carlos III de Madrid, Avda. Universidad 30, Legan´es, 28911, Madrid, Spain.

    • Stefano Discetti

      Universidad Carlos III de Madrid

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  • A novel LES-augmented machine learning algorithm for turbulent flow and bed morphodynamics prediction in large-scale environments

    ORAL

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    Publication: Zhang, Z, Sotiropoulos, F., Khosronejad A., (2024), Toward ultra-efficient high-fidelity prediction of bed morphodynamics of large-scale meandering rivers using a novel LES-trained machine learning approach, In Review.

    Presenters

    • Fotis Sotiropoulos

      Virginia Commonwealth University

    Authors

    • Zexia Zhang

      Stony Brook University

    • Fotis Sotiropoulos

      Virginia Commonwealth University

    • Ali Khosronejad

      Stony Brook University (SUNY)

    View abstract →