arXiv subjects

Aamir Dean

Publications and source records attributed to Aamir Dean.

5 recordsLinked to original sources

arXiv2026

PI-GINOT: Data-free geometry-informed neural operator learning for finite-strain hyperelasticity on parametric DogBone specimens

Parametric nonlinear solid-mechanics simulations are widely used in virtual testing, optimisation, and uncertainty analysis, but repeated finite-element simulations become costly when geometry changes. This paper presents PI-GINOT, a physics-informed neural operator that predicts finite-strain hyperelastic responses across a four-parameter family of DogBone specimens without using finite-element training data. Each specimen is described by a boundary point cloud, which is encoded into geometry features. A cross-attention decoder then predicts displacement at arbitrary points. Displacement boundary conditions are enforced exactly, while stresses are computed using automatic differentiation and a compressible Neo-Hookean plane-stress model. Training is guided by equilibrium, traction-free and symmetry conditions, deformation stability, and internal force consistency. Abaqus simulations are used only after training for validation. Across eight test geometries, PI-GINOT achieves displacement errors of 2.1%-7.1%, peak von Mises stress errors of 0.9%-13.3%, and section-force errors below 10.3%. Larger errors occur in individual stress components, especially for narrow specimens, mainly because of steep stress gradients near the gauge-to-fillet transition. These results show that PI-GINOT can provide useful geometry-dependent predictions for nonlinear solid mechanics without labelled simulation data, while also revealing where better local stress resolution is still needed.

physics.comp-ph↗

arXiv2026

A Puck-informed mode-resolved phase-field fatigue framework for unidirectional composites

Fatigue fracture in unidirectional fibre-reinforced composites is strongly mode dependent: transverse and off-axis cycling is governed by matrix and inter-fibre mechanisms, whereas fibre-aligned cycling activates a longitudinal channel with a higher fracture-energy scale and a different crack topology. Single-damage-variable models can fit global stiffness loss but cannot identify the active mechanism. This work proposes a Puck-informed, mode-resolved phase-field fatigue framework with separate channels for fibre-dominated and matrix/inter-fibre fatigue. Each channel has its own fatigue history, threshold, and resistance-degradation law. Fatigue does not directly degrade elastic stiffness; it lowers the fracture resistance of the active channel, while the corresponding phase field controls stiffness loss and crack-path evolution. The formulation is implemented in Abaqus/Standard using a compact UMAT-UEL architecture with one orthotropic mechanical routine and two scalar phase-field layers. Using one fixed IM7/8552 material and fatigue card, the model is verified through one-element tests, parameter sweeps, and centred-notch and open-hole tension cases at 0, 45, and 90 degrees under monotonic and cyclic loading. Without orientation- or geometry-specific tuning, the framework reproduces transverse matrix/inter-fibre cracking at 90 degrees, off-axis cracking at 45 degrees, and longitudinal matrix splitting with delayed fibre activation at 0 degrees. The fatigue lives follow the expected ordering: 45- and 90-degree cases fail within about 1,000 cycles, while 0-degree cases run out to 200,000 cycles without fibre cracking. Additional load, hole-size, mesh, length-scale, and cycle-block studies confirm consistent crack modes and converged trends. The study is a numerical verification and cross-geometry consistency assessment, not a calibrated experimental life-prediction claim.

physics.comp-ph↗

arXiv2026

A multiphysics deep energy method for fourth-order phase-field fracture with piezoresistive self-sensing

Piezoresistive materials can act as self-sensing media because deformation and cracking modify their electrical resistance. This paper presents a fracture-informed multiphysics framework for phase-field fracture using the Deep Energy Method. Mechanical deformation and fracture are solved first, after which the converged strain and damage fields determine a passive electrical-conduction readout. The electrical field does not contribute to the fracture-driving energy, and a control calculation confirmed that removing the electrical contribution produced no observable change in the crack morphology or global mechanical response. The mechanics-fracture formulation combines small-strain elasticity, a spectral tension-compression split, history-field irreversibility, and a fourth-order AT2-type regularization. The subsequent steady-conduction problem employs a conductivity law that accounts for linearized piezoresistivity and crack-induced degradation. The implementation uses admissible neural trial functions, quadrature-based energy evaluation, warm-started load stepping, and a bounded phase-field variable. Component-level verification includes analytical conduction tests, a reference-aligned single-edge-notched tension benchmark, and a perforated tensile-plate example. The results show that substantial local damage does not necessarily cause an immediate global resistance increase; a pronounced signal emerges only when major current-carrying ligaments are disrupted. The framework thus provides a transparent forward model for interpreting resistance-based self-sensing in fractured piezoresistive materials.

physics.comp-ph↗

arXiv2026

A multi-phase-field model for fiber-reinforced composite laminates based on puck failure theory

This article proposes a multi-phase-field model using the Puck failure theory to predict the failure in fiber-reinforced composites (FRCs) laminates. Specifically, this work proposes a two-dimensional multi-field model in conjunction with a mesh overlay method to compute in-plane damage in the FRCs laminates with various ply orientations. The formulation considers the two independent phase-field variables to trigger fiber and inter-fiber-dominated failure separately, thereby accessing the interrelation between the damage. Furthermore, the model considers two characteristic length scales and two structural tensors to describe the damage modes accurately. Each ply in the laminate is represented using a separate mesh and is combined using the mesh overlay method. Four benchmark examples are utilized to demonstrate the predictive capability of the proposed model. Specifically, coupon tests in tensile and compressive loading, open-hole tension, compact tension, and double-edged notched tension examples are presented along with the comparison with the experimental results from the literature. Furthermore, results regarding cross-ply laminates and isotropic laminates show the model's ability to mimic the experimental results both qualitatively and quantitatively.

physics.comp-ph↗

arXiv2025

A hybrid electromechanical phase-field and deep learning framework for predicting fracture in dielectric nanocomposites

The accurate and efficient prediction of crack propagation in dielectric materials is a critical challenge in structural health monitoring and the design of smart systems. This work presents a hybrid modeling framework that combines an electromechanical phase-field fracture model with deep learning-based surrogate modeling to predict fracture evolution in dielectric nanocomposite plates. The underlying finite element simulations capture the coupling between mechanical deformation and electrical field perturbations caused by cracks, using a variational phase-field formulation. High-fidelity simulation outputs - namely, phase-field damage variables and electric potential fields -- are used to train convolutional neural networks (CNNs) with ResNet-U-Net architectures for pixel-wise segmentation of crack paths. The study systematically compares the performance of CNNs trained on phase-field versus electric potential data across multiple ResNet backbones. The results reveal that electric potential fields, although they encode damage indirectly, offer superior segmentation accuracy, faster convergence, and enhanced generalization, owing to their smoother gradient distribution and global spatial coverage. The proposed framework significantly reduces computational costs while preserving high accuracy, offers potential when appropriately adapted for sensor-based input data.

physics.comp-ph↗