arXiv subjects

Ratna Kumar Annabattula

Publications and source records attributed to Ratna Kumar Annabattula.

15 recordsLinked to original sources

arXiv2026

A variational physics-informed graph neural network for heterogeneous solid mechanics

Stress localization in heterogeneous solids is governed by the bimaterial interface, where the displacement field remains $C^0$-continuous, while in-plane stresses jump due to the stiffness mismatch. Coordinate-based physics-informed neural networks (PINNs) represent this jump via a prescribed regularization width or a weighted interface penalty, making their accuracy sensitive to how phase-contrast changes are handled. This work presents a variational, label-free physics-informed graph neural network (PI-GNN) in which the heterogeneity is carried by the discretization rather than by the trial field. The solver operates on a conforming adaptive mesh graph, assigns constitutive behavior per element, and minimizes the discrete total potential energy as a single unweighted objective in which only first derivatives appear. The discrete energy on piecewise-linear elements coincides with the finite element (FE) Ritz functional. Dirichlet conditions are enforced by construction, with no penalty term, no interface weight, and no prescribed transition width. Using one fixed architecture, optimizer, and loss across small-strain elasticity and finite-strain Neo-Hookean hyperelasticity in two and three dimensions, the von Mises error remains below $3.58\%$ across a stiffness-contrast sweep spanning $(E_{\mathrm{inc}}/E_{\mathrm{mat}}\in[10^{-2},10^{2}])$, where a strong-form PINN degrades to $5.58\%$, and its displacement error reaches $7.66\%$ against $0.49\%$ for the PI-GNN. A trained network halves the ($σ_{xx}$) error of an energy-based PINN ($5.01\%$ versus $10.94\%$). Training cost exceeds a single FE solve by more than an order of magnitude, so the construction is a variationally consistent, penalty-free interface representation for parametric surrogates and inverse identification rather than a replacement for a one-off FE analysis.

math.NA↗

arXiv2026

Hybrid Surrogate-Based Multi-Objective Optimization of Graded BCC Lattices

Functionally graded BCC lattice structures hold promise for applications requiring simultaneous impact absorption and thermal dissipation, yet existing optimization frameworks rely on raw geometric parameters that are spatially blind to features such as the orientation of design gradients. In this study, we optimize density-graded BCC lattices for concurrent crashworthiness and heat dissipation via surrogate-based goal programming, initially using raw truss diameter as design variables. The lattice is discretized into three zones to achieve optimal dimensionality, and Pareto-optimal designs were identified that improve specific energy absorption and peak stresses during collisions while also enhancing its Nusselt number and pressure drop under forced convection relative to a non-graded lattice. Key insights of the effect of material distribution along the gradation axis on the performance are discussed in detail. We then introduce Physics-Informed Geometric Operators (PIGOs), which are scalar quantities derived from both the diameter profile and the triangulated surface mesh as candidate surrogate design variables. Pearson correlation analysis reveals that the raw diameter variables remain competitive, with the porosity gradient emerging as the most broadly predictive PIGO, strongly capturing both peak stress and pressure drop. The Nusselt number resists prediction by all variables tested, confirming that heat transfer is irreducibly multidimensional in this configuration due to competing surface-area and flow-blockage effects. The PIGO framework is topology-agnostic and extends naturally beyond the three-zone parameterization to finer gradations and alternative lattice topologies.

cond-mat.other↗

arXiv2026

Hydrodynamic Behavior of Non-spherical Particles in Confined Vertical Flows: A Resolved CFD-DEM Study

We investigate the sedimentation and vertical hydraulic transport of non-spherical polymetallic nodules (PMNs) using resolved computational fluid dynamics-discrete element method (CFD-DEM) with multisphere particles spanning $98 < Re_\text{p} < 2904$. Shape effects induce 1.8-2.0 times drag enhancement relative to volume-equivalent spheres, arising from 50\% larger frontal areas and wake asymmetry, reducing terminal velocities by 27-29\%. Vertical transport exhibits velocity-driven transitions from intermittent settling to stable convection, as demonstrated by residence-time and drag-force statistics. While PMNs exhibit enhanced rotational-translational coupling and broader force fluctuations, the regime progression qualitatively resembles that of volume-equivalent spherical particles. Drag variance evolution reveals contrasting behavior: small particles $(d/D=0.082)$ show narrow distributions and wake suppression at higher velocities, while large particles $(d/D=0.22)$ exhibit non-monotonic variance. These findings elucidate shape-confinement interactions in vertical transport and establish bounds on the applicability of volume-equivalent spherical particles in reduced-order models.

physics.app-ph↗

arXiv2025

Optimized mouldboard design for efficient soil inversion using the discrete element method

The design of a mouldboard (MB) plough is critical for achieving efficient soil inversion, which directly impacts soil aeration, weed control, and overall agricultural productivity. In this work, a design modification of the cylindroid-shaped MB plough is proposed, focusing on optimizing its surface profile to enhance performance. The discrete element method is used to simulate the ploughing process and evaluate the performance of the modified plough profile. The modified plough profile is compared against a previously proposed design to assess its impact on soil inversion efficiency, wear reduction, and stress distribution. A novel methodology is introduced to evaluate the plough's performance in soil inversion. The modified design demonstrates superior soil inversion efficiency, with improvements of up to $32.95\%$ in the inversion index for different velocities. The modified design achieves a notable reduction in wear up to $23.7\%$, compared to the original design. Although a slight increase in stress is observed in the modified design due to higher forces, the induced stresses remain well within the permissible limits for the plough material. Overall, the findings highlight the advantages of the modified plough design, including enhanced soil inversion efficiency and reduced wear, underscoring its potential for improved performance in tillage applications. However, the current study is limited to simulation-based analysis without experimental or field validation. Future work will focus on full-scale physical experiments to validate the simulation outcomes and incorporate additional factors such as depth-dependent moisture, soil cohesion, and multi-factor wear models for improved predictive accuracy.

cond-mat.other↗

arXiv2025

A DEM-driven machine learning framework for abrasive wear prediction

Particle-induced wear is a critical concern in bulk material handling systems, where abrasive interactions accelerate equipment degradation, increase maintenance needs, and raise operational costs. The Discrete Element Method (DEM) and Archard's wear model are widely adopted for predicting particle-surface wear processes. However, DEM is computationally prohibitive for real-time design and predictive maintenance, often requiring hours to days for a single parametric analysis. We propose a DEM-machine learning (ML) framework to address this limitation that combines physics-based simulations with data-driven efficiency. A dataset of 200 DEM simulations is generated by systematically varying particle size, material, and contacting plate geometric parameters. A few ML models -- linear regression, Lasso and Ridge regularization, decision trees, and a genetic algorithm-optimized artificial neural network (GA-ANN) -- were trained and evaluated. Feature selection revealed that Archard's wear constant, particle size, plate angle, and impingement velocity are the dominant predictors of wear. While linear models offered interpretability, their accuracy was limited. The GA-ANN achieved the highest performance $(R^2 = 0.91)$, effectively capturing nonlinear wear dynamics while reducing computational cost by orders of magnitude. This study demonstrates that physics-informed ML provides a scalable pathway for accurate, real-time wear prediction, enabling predictive maintenance and optimized design in bulk material handling industries.

physics.app-ph↗

arXiv2024

Study of triaxial loading of segregated granular assemblies through experiments and DEM simulations

A simple position-dependent body force-based confinement for simulating triaxial tests using the Discrete Element Method is presented. The said method is used to perform triaxial simulations on mono-disperse and segregated assemblies of glass spheres. The macroscopic load response obtained in simulations is validated with experimental load response. A mesh construction algorithm is presented to check whether the confinement applied in the triaxial simulations is accurate. The particle displacement data obtained from triaxial simulations are used to obtain a particle-wise average strain tensor. This is further used to compare the strain localisation between the mono-disperse and segregated assemblies. It is observed that, in the segregated assembly, the interface between the two particle phases acts as a barrier for strain localisation, and the smaller particles preferentially undergo a higher degree of shear strain on average.

cond-mat.soft↗

arXiv2024

Photo-activated dynamic isomerization induced large density changes in liquid crystal polymers: A molecular dynamics study

We use molecular dynamics simulations to unravel the physics underpinning the light-induced density changes caused by the dynamic trans-cis-trans isomerization cycles of azo-mesogens embedded in a liquid crystal polymer network, an intriguing experimental observation reported in the literature. We employ two approaches, cyclic and probabilistic switching of isomers, to simulate dynamic isomerization. The cyclic switching of isomers confirms that dynamic isomerization can lead to density changes at specific switch-time intervals. The probabilistic switching approach further deciphers the physics behind the non-monotonous relation between density reduction and light intensities observed in experiments. Light intensity variations in experiments are accounted for in simulations by varying the trans-to-cis and cis-to-trans isomerization probabilities. The simulations show that an optimal combination of these two probabilities results in a maximum density reduction, corroborating the experimental observations. At such an optimal combination of probabilities, the dynamic trans-cis-trans isomerization cycles occur at a specific frequency, causing significant distortion in the polymer network, resulting in a maximum density reduction.

cond-mat.soft↗

arXiv2023

Design of auxetic cellular structures for in-plane response through out-of-plane actuation of stimuli-responsive bridge films

In this work, we propose novel designs of cellular structures exhibiting unconventional in-plane actuation responses to external stimuli. We strategically introduce stimuli-responsive bilayer bridge films within conventional honeycombs to achieve the desired actuation. The films are incorporated such that, in response to an external field (thermal, electric, chemical, etc.), the bridge film bends out-of-plane, activating the honeycomb in the plane. The conventional out-of-plane deformation of the bridge film can lead to interesting and unconventional actuation in the plane. An analytical model of this coupled unit cell behaviour is developed using curved beam theory, and the model is validated against finite element simulations. Several applications of such designs are presented. Unit cell architectures exhibiting both positive and negative macroscopic actuation are proposed, and the criterion for achieving such actuation is derived analytically. Furthermore, we demonstrate that by altering the topology, unidirectional and bidirectional negative actuation can be achieved. We also propose designs that result in the negative actuation of the structure with both monotonically increasing and monotonically decreasing stimuli. Finally, by combining two macroscopic structures with positive and negative actuation, we design efficient actuators/sensors that bend in the plane in response to a stimulus.

physics.app-ph↗

arXiv2022

Physics-informed neural networks for solving thermo-mechanics problems of functionally graded material

Differential equations are indispensable to engineering and hence to innovation. In recent years, physics-informed neural networks (PINN) have emerged as a novel method for solving differential equations. PINN method has the advantage of being meshless, scalable, and can potentially be intelligent in terms of transferring the knowledge learned from solving one differential equation to the other. The exploration in this field has majorly been limited to solving linear-elasticity problems, crack propagation problems. This study uses PINNs to solve coupled thermo-mechanics problems of materials with functionally graded properties. An in-depth analysis of the PINN framework has been carried out by understanding the training datasets, model architecture, and loss functions. The efficacy of the PINN models in solving thermo-mechanics differential equations has been measured by comparing the obtained solutions either with analytical solutions or finite element method-based solutions. While R2 score of more than 99% has been achieved in predicting primary variables such as displacement and temperature fields, achieving the same for secondary variables such as stress turns out to be more challenging. This study is the first to implement the PINN framework for solving coupled thermo-mechanics problems on composite materials. This study is expected to enhance the understanding of the novel PINN framework and will be seminal for further research on PINNs.

cs.CE↗

arXiv2021

Voxelization based packing analysis for discrete element simulations of non-spherical particles

A voxelization based post-processing algorithm is proposed to analyze the packing of non-spherical particle assemblies simulated using the Discrete Element Method. Voxelization of the particle data allows for isolating the geometric features of the granular assembly in various spatial sub-domains (2D surface or 3D region) and investigate the localized packing behaviour. Analyzing the local packing behaviour enables determining con-fined influences such as the wall-effect, stacking behaviour, local expansion/contraction and localized loading. The efficacy of the proposed technique to analyze practical granular assemblies is demonstrated through the packing analysis of different assemblies of superquadric cubes, superquadric ellipsoidals, and multi-spherical coffee beans.

cond-mat.soft↗

arXiv2020

Continuum modelling of stress diffusion interactions in an elastoplastic medium in the presence of geometric discontinuity

Chemo-mechanical coupled systems have been a subject of interest for many decades now. Previous attempts to solve such models have mainly focused on elastic materials without taking into account the plastic deformation beyond yield, thus causing inaccuracies in failure calculations. This paper aims to study the effect of stress-diffusion interactions in an elastoplastic material using a coupled chemo-mechanical system. The induced stress is dependent on the local concentration in a one way coupled system, and vice versa in a two way coupled system. The time-dependent transient coupled system is solved using a finite element formulation in an open-source finite element solver FEniCS. This paper attempts to computationally study the interaction of deformation and diffusion and its effect on the localization of plastic strain. We investigate the role of geometric discontinuities in scenarios involving diffusing species, namely, a plate with a notch/hole/void and particle with a void/hole/core. We also study the effect of stress concentrations and plastic yielding on the diffusion-deformation. The developed code can be from https://github.com/mrupeshkumar/Elastoplastic-stress-diffusion-coupling

cs.CE↗

arXiv2018

A finite element analysis of rolling of bilayer films to cylindrical and conical tubes

With recent developments in nanotechnology, self-assembled structures are providing convenient, cheaper and more precise ways of manufacturing various patterns and shapes with less complexity. Of these self-assembled structures, rolled-up nano-tubes play a vital role in various aspects. The notion is, the utilization of strain energy developed during epitaxial growth of a bilayer thin film over a substrate, mediated by a sacrificial layer. While the sacrificial layer is etched, the bilayer film is subjected to release its own in-built strain energy in the out-of-plane direction (3D structure) due to a bending stress induced by biaxial strain through the thickness, in the bilayer. This paper proposes a new method of fabricating conical self-rolled assembly by thickness and strain variations along the width of the bilayer and, cylindrical structure of variable radius due to thickness and strain variations along the length.

physics.app-ph↗

arXiv2017

Modified Stoney's equation with anisotropic substrates undergoing large deformations

Residual stresses in a thin film deposited on a substrate results in a curvature of the system, which can be measured using the well known Stoney equation. Isotropic elasticity of the substrate along with infinitesimal strains and rotations are two important assumptions used in the derivation of the Stoney equation. However, the transverse deflection in the substrate contributes significantly to the extensional strain in its plane, leading to non-linearity in its deformation. Moreover, Silicon wafers are predominantly used as substrate materials to measure the curvature of the system. In this paper, relations between normalized substrate curvature and normalized thin film mismatch are derived in the non-linear deformation regime, for substrates made of single crystal Si(001) and Si(111) wafers. Numerical results of curvature of thin film configurations with Si(001) and Si(111) wafer substrates, undergoing large deformations are presented and discussed.

cond-mat.soft↗

arXiv2017

An Abaqus UEL implementation of the smoothed finite element method

In this paper, we discuss the implementation of a cell based smoothed finite element method (CSFEM) within the commercial finite element software Abaqus. The salient feature of the CSFEM is that it does not require an explicit form of the derivative of the shape functions and there is no isoparametric mapping. This implementation is accomplished by employing the user element subroutine (UEL) feature of the software. The details on the input data format together with the proposed user element subroutine, which forms the core of the finite element analysis are given. A few benchmark problems from linear elastostatics in both two and three dimensions are solved to validate the proposed implementation. The developed UELs and the associated input files can be downloaded from Github repository link: https://github.com/nsundar/SFEM\_in\_Abaqus.

math.NA↗

arXiv2014

Size dependent crush analysis of lithium orthosilicate pebbles

Crushing strength of the breeder materials (lithium orthosilicate, $\rm{Li_4SiO_4}$ or OSi) in the form of pebbles to be used for EU solid breeder concept is investigated. The pebbles are fabricated using a melt-spray method and hence a size variation in the pebbles produced is expected. The knowledge of the mechanical integrity (crush strength) of the pebbles is important for a successful design of breeder blanket. In this paper, we present the experimental results of the crush (failure) loads for spherical OSi pebbles of different diameters ranging from $250~μ$m to $800~μ$m. The ultimate failure load for each size shows a Weibull distribution. Furthermore, the mean crush load increases with increase in pebble diameter. It is also observed that the level of opacity of the pebble influences the crush load significantly. The experimental data presented in this paper and the associated analysis could possibly help us to develop a framework for simulating a crushable polydisperse pebble assembly using discrete element method.

cond-mat.mtrl-sci↗