Seminar – Dr. Rajeev Jaiman: Computational Physics of Fluid-Structure Interactions: CFD + FEA with Scientific Machine Learning

Computational Physics of Fluid-Structure Interactions: CFD + FEA with Scientific Machine Learning

Speaker: Dr. Rajeev Jaiman, Associate Professor UBC Mechanical Engineering, NSERC/Seaspan Industrial Research Chair in Intelligent and Green Marine Vessels (IGMVs)

When: Nov 30, 2023 | 12-1 PM
Where: CEME Building (6250 Applied Science Lane), room 2202

Zoom Details:
(Meeting ID: 674 0899 5337  |  Passcode: 072654)

The Department of Mechanical Engineering’s Fall Seminar Series shares cutting-edge research by MECH faculty members.


Advances in high-performance computing have empowered us to perform large-scale finite element analysis (FEA) routinely for millions of unknown variables in multiphysics and coupled fluid-structure systems. These high-fidelity simulations via nonlinear partial differential equations can provide invaluable physical insight for the development of new designs and devices in marine/offshore and aerospace engineering. Despite efficient numerical algorithms and powerful supercomputers, the state-of-the-art computational fluid dynamics (CFD) and coupled fluid-structure simulations are somewhat inefficient and hence less attractive concerning downstream tasks such as parameter space exploration, design optimization and the development of real-time control and monitoring strategies for marine/offshore and aerospace structures. On a similar note, two other pillars namely theoretical analysis and experimental testing can suffer serious limitations with regard to the scaling to realistic geometry and physical situations. The emergence of data-driven methods and machine learning has been recently recognized as a powerful alternative and can offer a fourth pillar as a unifying force to combine the three pillars of science and engineering.

In this talk, I will highlight some of our recent Lab efforts to integrate and complement the HPC-based high-fidelity computations with data-driven modeling of multiphysics interfaces, with a particular emphasis on unsteady fluid flow and fluid-structure interaction in marine/offshore and aerospace engineering. The primary focus of this talk is: (i) to demonstrate the capability of in-house multiphase FSI framework using Eulerian-Lagrangian and fully Eulerian formalisms, and (ii) to develop efficient reduced-order and deep learning models for the physical modeling of fluid-structure systems. I will present validation of our FSI methods and tools for increasing complexity of problems along with the demonstration of a full-scale flying bat, a flexible propeller-blade with cavitation and an ice-going ship in open water. A series of canonical academic test cases will be covered to elucidate the integration of CFD/FEA datasets with model reduction and deep learning techniques for unsteady flow and fluid-structure interaction. The in-house hybrid CFD/FEA and data-driven framework is precisely aligned with the aerospace and marine industry need for structural life prediction, control and monitoring via physics-based digital twin.


Rajeev K. Jaiman is currently an Associate Professor and NSERC/Seaspan Industrial Research Chair in the Department of Mechanical Engineering at the University of British Columbia (UBC), Vancouver. Prior to his current appointment at UBC, he was an assistant professor in the Department of Mechanical Engineering at the National University of Singapore (NUS). Before joining NUS, he was the Director of CFD Development at Altair Engineering, California. Dr. Jaiman earned his first degree in Aerospace Engineering from the Indian Institute of Technology, Mumbai. He received his master’s and doctorate degrees from the University of Illinois at Urbana-Champaign with Computational Science and Engineering option. His research interests broadly include multiphysics simulations, fluid-structure interaction, computational fluid dynamics, data-driven modeling and machine learning.