Turbulence: Modeling II

Presentations

• Multilevel Lagrangian model for passive scalar gradients in turbulence at high Reynolds and Schmidt numbers

  ORAL

  Nov. 19, 2023, 4:35 PM – Nov. 19, 2023, 4:48 PM

  Presenters

  • Soumak Bhattacharjee

    • Duke University

  Authors

  • Soumak Bhattacharjee

    • Duke University

  • Andrew D Bragg

    • Duke University

  View abstract →

• Predicting scalar gradient dynamics in turbulent mixing using deep neural networks

  ORAL

  Nov. 19, 2023, 4:48 PM – Nov. 19, 2023, 5:01 PM

  Presenters

  • Dhawal Buaria

    • New York University
    • New York University (NYU)

  Authors

  • Dhawal Buaria

    • New York University
    • New York University (NYU)

  • Katepalli R Sreenivasan

    • New York University (NYU)
    • New York University

  View abstract →

• Assessment of RANS models for Rayleigh-Taylor mixing using the Macroscopic Forcing Method

  ORAL

  Nov. 19, 2023, 5:01 PM – Nov. 19, 2023, 5:14 PM

  Presenters

  • Dana Lynn Lavacot

    • Stanford University

  Authors

  • Dana Lynn Lavacot

    • Stanford University

  • Jessie Liu

    • Stanford University

  • Brandon E Morgan

    • Lawrence Livermore National Lab

  • Ali Mani

    • Stanford University
    • Standard University
    • Department of Mechanical Engineering, Stanford University

  View abstract →

• A Robust Turbulent Combustion Closure model via Deep Operator Network

  ORAL

  Nov. 19, 2023, 5:14 PM – Nov. 19, 2023, 5:27 PM

  Presenters

  • Arsalan Taassob

    • North Carolina State University

  Authors

  • Arsalan Taassob

    • North Carolina State University

  • Anuj Kumar

    • North Carolina State University

  • Tarek Echekki

    • North Carolina State University

  • Rishikesh Ranade

    • Ansys

  View abstract →

• Compressed representations and reduced order modeling of the turbulent flow response to roughness

  ORAL

  Nov. 19, 2023, 5:27 PM – Nov. 19, 2023, 5:40 PM

  Presenters

  • Miles J Chan

    • Caltech
    • California Institute of Technology

  Authors

  • Miles J Chan

    • Caltech
    • California Institute of Technology

  • Ugo Piomelli

    • Queen's University

  • Beverley J McKeon

    • Stanford University

  View abstract →

• Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement for Turbulent Flow Reconstruction.

  ORAL

  Nov. 19, 2023, 5:40 PM – Nov. 19, 2023, 5:53 PM

  Publication: This work has been accepted by the Journal "Transactions on Intelligent Systems and Technology (TIST)".

  Presenters

  • Shengyu Chen

    • University of Pittsburgh

  Authors

  • Shengyu Chen

    • University of Pittsburgh

  • Peyman Givi

    • University of Pittsburgh

  • Can Zheng

    • University of Pittsburgh

  • Xiaowei Jia

    • University of Pittsburgh

  View abstract →

• Towards a physical interpretation of machine-learned turbulence models

  ORAL

  Nov. 19, 2023, 5:53 PM – Nov. 19, 2023, 6:06 PM

  Presenters

  • Jiaqi Li

    • Penn State

  Authors

  • Jiaqi Li

    • Penn State

  • Yuanwei Bin

    • Pennsylvania State University & Peking University
    • Pennsylvania State University

  • George P Huang

    • Wright State University

  • Xiang Yang

    • Pennsylvania State University
    • The Penn State Department of Mechanical Engineering
    • Penn State Department of Mechanical Engineering

  View abstract →

• Wavelet-based predictions of bursting events in 2D Kolmogorov flow

  ORAL

  Nov. 19, 2023, 6:06 PM – Nov. 19, 2023, 6:19 PM

  Presenters

  • Anagha Madhusudanan

    • Indian Institute of Science

  Authors

  • Anagha Madhusudanan

    • Indian Institute of Science

  • Rich R Kerswell

    • Univ of Cambridge
    • DAMTP, University of Cambridge

  View abstract →

• Symmetries Based Invariant Modeling for Second Moment Turbulence Modeling

  ORAL

  Nov. 19, 2023, 6:19 PM – Nov. 19, 2023, 6:32 PM

  Presenters

  • Felician C Putz

    • TU Darmstadt

  Authors

  • Felician C Putz

    • TU Darmstadt

  • Nils Benedikt

    • TU Berlin

  • Dario S Klingenberg

    • Technische Universitat Darmstadt

  • Martin Oberlack

    • TU Darmstadt

  View abstract →