106 citations · 883 across the 63 of their papers we have counts for
65 papers
Localized Generative Design of Ship Hulls with Diffusion Models and High-Fidelity Assessment
Hadi Keramati, Mohammed A. Hannan, Patrick Kirchen +4
Practical ship design requires localized refinement and performance evaluation. This paper presents a reward-directed diffusion framework for localized hull optimization. Parametri…
Cavitation Acoustic Perturbation Equations: A Computational Framework for Source-Resolved Multiphase Hydroacoustics
Zhi Cheng, Rajeev K. Jaiman
This work develops a cavitation-consistent acoustic perturbation framework for predicting sound generation and propagation in cavitating flows. Unlike conventional acoustic perturb…
A finite-difference summation-by-parts, conditionally stable partitioned algorithm for conjugate heat transfer problems
Sarah Nataj, David C. Del Rey Fernández, David Brown +1
In this work, we design and analyze a novel, provably conditionally stable, weakly coupled partitioned scheme to solve the conjugate heat transfer (CHT) problem. We consider a mode…
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…
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…
A finite element framework for fluid-structure interaction of turbulent cavitating flows with flexible structures
Nihar B. Darbhamulla, Rajeev K. Jaiman
We present a finite element framework for the numerical prediction of cavitating turbulent flows interacting with flexible structures. The vapor-fluid phases are captured through a…
A numerical study on the oscillatory dynamics of tip vortex cavitation
Saman Lak, Rajeev Jaiman
In this paper, we numerically study the oscillatory dynamics associated with the tip vortex cavitation over an elliptical hydrofoil section using our 3D variational multiphase flow…
Acoustics-based Active Control of Unsteady Flow Dynamics using Reinforcement Learning Driven Synthetic Jets
Siddharth Rout, Khai Phan, Chao-An Lin
Flow generated noise are caused shear flows and, hence, they can be used as feedback to control the flow. Existing flow control uses state variables like velocity, pressure, or vor…