106 citations · 883 across the 63 of their papers we have counts for
9 papers · 1 filter
An Efficient and Accurate Surrogate Modeling of Flapping Dynamics in Inverted Elastic Foils using Hypergraph Neural Networks
Aarshana R. Parekh, Rui Gao, Rajeev K. Jaiman
Cantilevered elastic foils can undergo self-induced, large-amplitude flapping when subject to fluid flow, a widely observed phenomenon of fluid-structure interaction, from flutteri…
HeatGen: A Guided Diffusion Framework for Multiphysics Heat Sink Design Optimization
Hadi Keramati, Morteza Sadeghi, Rajeev K. Jaiman
This study presents a generative optimization framework based on a guided denoising diffusion probabilistic model (DDPM) that leverages surrogate gradients to generate heat sink de…
A Physics-Guided Probabilistic Surrogate Modeling Framework for Digital Twins of Underwater Radiated Noise
Indu Kant Deo, Akash Venkateshwaran, Rajeev K. Jaiman
Ship traffic is an increasing source of underwater radiated noise in coastal waters, motivating real-time digital twins of ocean acoustics for operational noise mitigation. We pres…
A Phase Field Formulation of Frictional Sliding Contact for 3D Fully Eulerian Fluid Structure Interactions
Biswajeet Rath, Rajeev K. Jaiman
Frictional sliding contact in hydrodynamic environments can be found in a range of engineering applications. Accurate modeling requires an integrated numerical framework capable of…
Propulsive transitions and scaling relations of a heaving flexible foil in a cylinder wake
Guojun Li, Lanlan Wang, Weitao Jiang +2
We numerically investigate the propulsive dynamics of a heaving flexible foil immersed in the wake of a stationary circular cylinder, focusing on the coupled effects of unsteady wa…
MUTE-DSS: A Digital-Twin-Based Decision Support System for Minimizing Underwater Radiated Noise in Ship Voyage Planning
Akash Venkateshwaran, Indu Kant Deo, Rajeev K. Jaiman
We present a novel MUTE-DSS, a digital-twin-based decision support system for minimizing underwater radiated noise (URN) during ship voyage planning. It is a ROS2-centric framework…
Predicting Flow-Induced Vibration in Isolated and Tandem Cylinders Using Hypergraph Neural Networks
Shayan Heydari, Rui Gao, Rajeev K Jaiman
We present a finite element-inspired hypergraph neural network framework for predicting flow-induced vibrations in freely oscillating cylinders. The surrogate architecture transfor…
Predicting Wave Dynamics using Deep Learning with Multistep Integration Inspired Attention and Physics-Based Loss Decomposition
Indu Kant Deo, Rajeev K. Jaiman
In this paper, we present a physics-based deep learning framework for data-driven prediction of wave propagation in fluid media. The proposed approach, termed Multistep Integration…
A Mesh-Adaptive Hypergraph Neural Network for Unsteady Flow Around Oscillating and Rotating Structures
Rui Gao, Zhi Cheng, Rajeev K. Jaiman
Graph neural networks, recently introduced into the field of fluid flow surrogate modeling, have been successfully applied to model the temporal evolution of various fluid flow sys…