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S3-11: Machine Learning and Multiphase flows
Time:
Monday, 12/May/2025:
4:40pm - 6:20pm
Session Chair: Daniele Marchisio
Location: Mercure Salle 1
Presentations
4:40pm - 5:00pm Toward Validating Theoretical and Developing Data-Driven Deagglomeration Models: Particle-Resolved DNS for Forced HIT
Ali Khalifa , Michael Breuer
Helmut-Schmidt Universität Hamburg, Germany
5:00pm - 5:20pm Heuristic Stochastic Inverse Multiphase Flow Model for Steady-State Three Phase-Flow
André Mendes Quintino 1 , Ivar Eskerud Smith2 , Milan Stanko1
1 Norwegian University of Science and Technology (NTNU), Norway; 2 SINTEF Industry, Norway
5:20pm - 5:40pm Development of AI-driven Multiphase Data Generation Models Using Generative AI Techniques
Shuichiro Miwa 1 , Wen Zhou1 , Yang Liu2 , Koji Okamoto1
1 The University of Tokyo, Japan; 2 Virginia Tech, USA
5:40pm - 6:00pm Physics-Informed Neural Networks for Two-Phase Flow Simulations: An Integrated Approach with Advanced Interface Tracking Methods
WEN ZHOU , Shuichiro Miwa, Koji Okamoto
University of Tokyo, Japan
6:00pm - 6:20pm Modeling Of Flow and Transport in Porous Media: Integrating CFD and Deep Learning for Applications in Digitally Generated Geometries
Gianluca Boccardo1 , Agnese Marcato1,3 , Enrico Agostini2 , Javier E. Santos3 , Yacine Haroun2 , Maxime Moreaud2 , Frederic Augier2 , Daniele Marchisio 1
1 Politecnico di Torino, Italy; 2 IFP-Energies Nouvelles, Lyon, France; 3 Earth and Environmental Sciences, Los Alamos National Laboratory, United States
