Vinamra Agrawal

Vinamra Agrawal

Computational Materials Scientist in XCP-5

Publications

You can also find my articles on my Google Scholar profile.

The Alamo multiphysics solver for phase field simulations with strong-form mechanics and block structured adaptive mesh refinement

Published in The Journal of Open Source Software, 2025

We present the Alamo multiphysics solver and its latest capabilities, including solid mechanics and solid fluid interaction problems.

Recommended citation: Runnels B., Agrawal V. and Meier M., (2025). The Alamo multiphysics solver for phase field simulations with strong-form mechanics and block structured adaptive mesh refinement. Journal of Open Source Software, 10(116), 8581, https://doi.org/10.21105/joss.08581 https://doi.org/10.21105/joss.08581

Convolutional and Graph Neural Network framework for predicting critical impact velocity in heterogeneous PBX-9501

Published in Propellants, Explosives, Pyrotechnics, 2025

We use CNNs and GNNs to predict critical velocity for SDT response of PBX-9501 with macroscale pores.

Recommended citation: R. Perera, V. Agrawal, N. Cummock, B. McCracken, and G. Thich, “ Convolutional and Graph Neural Network Framework for Predicting Critical Impact Velocity in Heterogeneous PBX-9501.” Propellants, Explosives, Pyrotechnics (2025): e70058. https://doi.org/10.1002/prep.70058 https://doi.org/10.1002/prep.70058

Ductile fracture in functionally graded materials: Insight into crack behavior within the gradient interface

Published in arXiv preprint (journal submission under review), 2024

In this work, study the role of gradient architecture in crack propagation and failure of metallic functionally graded materials through the phase field formulation of ductile fracture.

Recommended citation: Piper K. and Agrawal V., Ductile fracture in functionally graded materials: Insight into crack behavior within the gradient interface, arXiv:2411.18642. https://doi.org/10.48550/arXiv.2411.18642

Multiscale graph neural networks with adaptive mesh refinement for accelerating mesh-based simulations

Published in Computer Methods in Applied Mechanics and Engineering, 2024

In this work, we develop a multiscale mesh-based GNN framework mimicking a conventional iterative multigrid solver, coupled with adaptive mesh refinement (AMR), to mitigate challenges with conventional mesh-based GNNs. We use the framework to accelerate phase field (PF) fracture problems involving coupled partial differential equations with a near-singular operator due to near-zero modulus inside the crack.

Recommended citation: Perera R. and Agrawal V., Multiscale graph neural networks with adaptive mesh refinement for accelerating mesh-based simulations, Computer Methods in Applied Mechanics and Engineering, 429 (2024), 117152. https://doi.org/10.1016/j.cma.2024.117152

Dynamic and adaptive mesh-based graph neural network framework for simulating displacement and crack fields in phase field models

Published in Mechanics of Materials, 2023

In this work, we present a dynamic mesh-based GNN framework for emulating phase field simulations of single-edge crack propagation with AMR for different crack configurations.

Recommended citation: Perera R. and Agrawal V., Dynamic and adaptive mesh-based graph neural network framework for simulating displacement and crack fields in phase field models, Mechanics of Materials 186 (2023), 104789. https://doi.org/10.1016/j.mechmat.2023.104789

Transmitting multiple high-frequency phonons across length scales using the concurrent atomistic–continuum method

Published in Computational Materials Science, 2023

In this work, we develop a technique to allow the full spectrum of phonons to be incorporated into the coarse-scaled regions of a periodic concurrent atomistic–continuum (CAC) framework.

Recommended citation: Davis A. and Agrawal V., Transmitting multiple high-frequency waves across length scales using the concurrent atomistic-continuum method, Computational Materials Science, 214 (2022), 111702. https://doi.org/10.1016/j.commatsci.2022.111702

A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks

Published in Mechanics of Materials, 2023

In this work, we use transfer learning to develop a generalized ML framework called ACCURATE to study multiple crack propagtion under mixed mode loading.

Recommended citation: Perera R. and Agrawal V., A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks, Mechanics of Materials 181 (2023), 104639 https://doi.org/10.1016/j.mechmat.2023.104639

Robust, strong form mechanics on an adaptive structured grid: efficiently solving variable-geometry near-singular problems with diffuse interfaces

Published in Computational Mechanics, 2023

The purpose of this work is to present a comprehensive strategy for efficiently solving such problems on an adaptive structured grid, while expositing some of the basic yet important nuances associated with solving near-singular problems in strong form.

Recommended citation: Agrawal, V., Runnels, B. Robust, strong form mechanics on an adaptive structured grid: efficiently solving variable-geometry near-singular problems with diffuse interfaces. Computational Mechanics 72, 1009–1027 (2023). https://doi.org/10.1007/s00466-023-02325-8

Investigating shock wave propagation, evolution, and anisotropy using a moving window concurrent atomistic–continuum framework

Published in Computational Mechanics, 2023

In this work, we develop novel techniques within the concurrent atomistic–continuum (CAC) multiscale framework to simulate shock wave propagation through a two-dimensional, single-crystal lattice.

Recommended citation: Davis, A.S., Agrawal, V. Investigating shock wave propagation, evolution, and anisotropy using a moving window concurrent atomistic–continuum framework. Computational Mechanics 71, 721–743 (2023). https://doi.org/10.1007/s00466-022-02258-8

Graph neural networks for simulating crack coalescence and propagation in brittle materials

Published in Computer Methods in Applied Mechanics and Engineering, 2022

This work develops a Graph Neural Network (GNN) based framework to simulate fracture and stress evolution in brittle materials due to multiple microcracks’ interaction.

Recommended citation: Perera R., Guzzeti D. and Agrawal V., Graph neural networks for emulating crack coalescence and propagation in brittle materials, Computer Methods in Applied Mechanics and Engineering, 395 (2022), 115021. https://doi.org/10.1016/j.cma.2022.115021

Moving window techniques to model shock wave propagation using the concurrent atomistic–continuum method

Published in Computer Methods in Applied Mechanics and Engineering, 2022

In this work, we develop two distinct moving window approaches within a Concurrent Atomistic–Continuum (CAC) framework to model shock wave propagation through a one-dimensional monatomic chain.

Recommended citation: Davis A., Lloyd J.T. and Agrawal V., Moving window techniques to model shock wave propagation using the concurrent atomistic-continuum method, Computer Methods in Applied Mechanics and Engineering, 389 (2022), 114360. https://doi.org/10.1016/j.cma.2021.114360

Block structured adaptive mesh refinement and strong form elasticity approach to phase field fracture with applications to delamination, crack branching and crack deflection

Published in Computer Methods in Applied Mechanics and Engineering, 2021

In this work a novel numerical framework is proposed for implementing hybrid phase field fracture in heterogeneous materials.

Recommended citation: Agrawal V. and Runnels B., Block structured adaptive mesh refinement and strong form elasticity approach to phase field fracture with applications to delamination, crack branching and crack deflection, Computer Methods in Applied Mechanics and Engineering, 385 (2021), 114011. https://doi.org/10.1016/j.cma.2021.114011

Numerical modeling and 3D-gravity inversion of the Vargeão impact structure formed in a mixed basalt/sandstone target, Paraná Basin, Brazil

Published in Journal of South American Earth Sciences, 2021

In this work, we studied the formation of the Vargeão impact structure through impact modeling.

Recommended citation: Silva L. M., Vasconcelos M. A. R., Agrawal V. and Crosta A. P., Numerical modeling and 3D-gravity inversion of the Vargeão impact structure formed in a mixed basalt/sandstone target of the Paraná Basin, Brazil, Journal of South American Earth Sciences, 110 (2021), 103396. https://doi.org/10.1016/j.jsames.2021.103396

Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain boundaries in microstructural images

Published in Computational Materials Science, 2021

In this work, an optimized machine learning (ML) framework is proposed to autonomously and efficiently characterize pores, particles, grains and grain boundaries (GBs) from a given microstructure image.

Recommended citation: Perera R., Guzzetti D. and Agrawal V., Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain boundaries in microstructural images, Computational Materials Science, 196 (2021), 110524. https://doi.org/10.1016/j.commatsci.2021.110524

Massively parallel finite difference elasticity using block-structured adaptive mesh refinement with a geometric multigrid solver

Published in Journal of Computational Physics, 2021

We present a novel reflux-free multiscale multigrid framework to solve equations of elasticity in strong form using finite difference method on a block-structured adaptively refining grid.

Recommended citation: Runnels B., Agrawal V., Zhang W. and Almgren A., Massively parallel finite difference scheme elasticity problem using a block-structured adaptive mesh refinement with a geometric multigrid solver, Journal of Computational Physics, 427 (2021), 110065. https://doi.org/10.1016/j.jcp.2020.110065

Mechanical characterization and modeling stress relaxation behavior of acrylic–polyurethane-based graft-interpenetrating polymer networks

Published in Polymer Engineering and Sciences, 2021

The stress relaxation behavior of acrylic–polyurethane (PU)-based graft-interpenetrating polymer networks (IPNs) was characterized via dynamic mechanical analysis (DMA) and modeled using finite element method (FEM) analysis.

Recommended citation: Alizadeh N., Celestine A.N, Auad M.L. and Agrawal V., Mechanical characterization and modeling stress relaxation behavior of acrylic-polyurethane based graft-Interpenetrating Polymer Networks (IPNs), Polymer Engineering and Science 61 (2021), 1299-1309. https://doi.org/10.1002/pen.25640

Experimental and numerical investigation into mechanical degradation of polymers

Published in Composites Part B: Engineering, 2020

This work presents a novel reduced order numerical model for the mechanical behavior and degradation of polymers, along with experimental results, illustrating the influence of water absorption on 3D printed and injection molded nylon 6.

Recommended citation: Celestine A.N, Agrawal V. and Runnels B., Experimental and numerical investigations into mechanical degradation of polymers, Composites Part B, 201 (2020), 108369. https://doi.org/10.1016/j.compositesb.2020.108369

One-dimensional moving window atomistic framework to model long-time shock wave propagation

Published in Computer Methods in Applied Mechanics and Engineering, 2020

We develop a long-time moving window framework using Molecular Dynamics (MD) to model shock wave propagation through a one-dimensional chain of atoms.

Recommended citation: Davis A. and Agrawal V., One-dimensional moving window atomistic framework to model long-time shock propagation, Computer Methods in Applied Mechanics and Engineering, 371 (2020), 113290. https://doi.org/10.1016/j.cma.2020.113290

High-fracture-toughness acrylic–polyurethane-based graft-interpenetrating polymer networks for transparent applications

Published in Polymer International, 2020

Polyurethane (PU) and acrylic-based copolymers out of styrene were utilized to synthesize transparent PU–acrylic graft-interpenetrating polymer networks (graft-IPNs) for the first time.

Recommended citation: Alizadeh N., Bade M., Minkler M., Celestine A.N., Agrawal V., Beckingham B. and Auad M., High fracture-toughness acrylic-polyurethane based graft interpenetrating polymer networks for transparent applications, Polymer International 70 (2021), 636-647 https://doi.org/10.1002/pi.6149

Phase field disconnections: A continuum method for disconnection-mediated grain boundary motion

Published in Scripta Materialia, 2020

This work presents a continuum phase field model in which disconnections arise naturally as the result of GB energy nonconvexity, the minimum dissipation potential model for GB migration, and the prescribed coupling factor.

Recommended citation: Runnels B. and Agrawal V., Phase field disconnections: a continuum method for disconnection-mediated grain boundary motion, Scripta Materialia, 186 (2020), 6-10. https://doi.org/10.1016/j.scriptamat.2020.04.042

A new approach for phase field modeling of grain boundaries with strongly nonconvex energy

Published in Modelling and Simulation in Materials Science and Engineering, 2019

In this work, we model grain boundary (GB) evolution through phase field approach using additively decoupled regularization scheme utilizing a K23 second order curvature regularization to penalize regions of sharp curvature induced by nonconvex GB energy.

Recommended citation: Ribot J.G., Agrawal V. and Runnels B., A new approach to phase field modeling of grain boundaries with strongly nonconvex energy, Modeling and Simulations in Materials Science and Engineering, 27 (2019), 084007. https://doi.org/10.1088/1361-651X/ab47a0

Computer modelling of the cochlea and the cochlear implant: A review

Published in Cochlear Implants International, 2013

This study reviews the major developments in cochlear models, summarizes, and categorizes features of models used in different studies and makes recommendations for future development.

Recommended citation: Agrawal V. and Newbold C., Computer modeling of the cochlea and the cochlear implant: a review, Cochlear Implants International, 13 (2012), 113-123. https://doi.org/10.1179/1754762811Y.0000000015