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Naqib Rahimi wins the Fluid Dynamics Committee
Student Paper Competition at EMI 2023


Naqib Rahimi

Naqib Rahimi, a Civil Engineering PhD student advised by Dr. Moutsanidis, was awarded first place in the Fluid Dynamics Committee Student Paper Competition of the 2023 Engineering Mechanics Institute Conference held in Atlanta, Georgia. He presented his research entitled “High Fidelity Modeling of Fracture Under Extreme Hydrodynamic Events: A Coupled SPH--Phase-Field FSI Approach”.

Naqib’s research focuses on developing robust and efficient computational tools for the simulation of structures under extreme events, with an emphasis on coastal structures and climate resilience. Coastal structures and regions are continuously exposed to coastal floods, tsunamis, and storms. The effects of these extreme hydrodynamic events on structures have been increasing due to the rapid climate change, and their impact on coastal communities can be devastating, ranging from costly and severe property damage to human losses. Computational modeling of such events through physics-based simulations, and the ability to predict their effect on coastal structures, is an important tool in increasing climate resilience of coastal communities. However, in the development of such models, special care should be taken to handle certain challenges such as free-surface flows, large structural deformations, and material separation. To address these challenges, Naqib developed a particle-based non-local approach for modeling structural fracture and fragmentation in fluid-structure interaction (FSI) scenarios of extreme hydrodynamic events. He employed the Smoothed Particle Hydrodynamics approach for the discretization of both fluid and structural domains, and used a hyperbolic phase field model of brittle fracture that allows for the realistic modeling of crack nucleation, propagation, and branching. Furthermore, he came up with a novel FSI coupling algorithm that increases the accuracy of the approach. He finally verified and validated the proposed framework against experiments and previous computational studies.