106 citations · 883 across the 63 of their papers we have counts for
9 papers · 1 filter
A Graph Neural Network Surrogate Model for Multi-Objective Fluid-Acoustic Shape Optimization
Farnoosh Hadizadeh, Wrik Mallik, Rajeev K. Jaiman
This article presents a graph neural network (GNN) based surrogate modeling approach for fluid-acoustic shape optimization. The GNN model transforms mesh-based simulations into a c…
Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks
Indu Kant Deo, Rajeev Jaiman
Accurate prediction over long time horizons is crucial for modeling complex physical processes such as wave propagation. Although deep neural networks show promise for real-time fo…
H-SIREN: Improving implicit neural representations with hyperbolic periodic functions
Rui Gao, Rajeev K. Jaiman
Implicit neural representations (INR) have been recently adopted in various applications ranging from computer vision tasks to physics simulations by solving partial differential e…
Flow-Induced Vibration of Flexible Hydrofoil Within Cavitating Turbulent Flow
Zhi Cheng, Rajeev Jaiman
The flow-induced vibration and cavitation dynamics of three-dimensional flow past a cantilever flexible hydrofoil are investigated using a large eddy simulation (LES) model, a homo…
Modeling of Hydroacoustic Noise from Marine Propellers with Tip Vortex Cavitation
Zhi Cheng, Suraj Kashyap, Brendan Smoker +2
The present work aims to study the cavitating turbulent flow of a full-scale marine propeller and explore the physical mechanism underpinning the underwater radiated noise. We empl…
An Efficient Phase-field Framework for Contact Dynamics between Deformable Solids in Fluid Flow
Biswajeet Rath, Xiaoyu Mao, Rajeev K. Jaiman
Elastic contact in hydrodynamic environments is a complex multiphysics phenomenon and can be found in applications ranging from engineering to biological systems. Understanding the…
Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net
Indu Kant Deo, Akash Venkateshwaran, Rajeev K. Jaiman
There is a significant need for precise and reliable forecasting of the far-field noise emanating from shipping vessels. Conventional full-order models based on the Navier-Stokes e…
A 3D phase-field based Eulerian variational framework for multiphase fluid-structure interaction with contact dynamics
Xiaoyu Mao, Rajeev Jaiman
Using a fixed Eulerian mesh, the phase-field method has been successfully utilized for a broad range of moving boundary problems involving multiphase fluids and single-phase fluid-…
A multi-objective optimization framework for reducing the impact of ship noise on marine mammals
Akash Venkateshwaran, Indu Kant Deo, Jasmin Jelovica +1
The underwater radiated noise (URN) emanating from ships presents a significant threat to marine mammals, given their heavy reliance on hearing for essential life activities. The i…