arXiv subjects

Miro Duhovic

Publications and source records attributed to Miro Duhovic.

4 recordsLinked to original sources

arXiv2026

Finite basis physics-informed neural networks with hard constraints for viscous fluid flow in highly perforated domains

In this work, viscous fluid flow governed by the Stokes equations in highly perforated domains is studied using physics-informed neural networks (PINNs). Perforated microstructures induce complex boundary conditions and fine-scale flow features that are difficult for standard neural networks to resolve. Conventional PINNs, even when combined with advanced training techniques, can suffer from a loss of accuracy and efficiency as the number of perforations increases. One important source of this difficulty is the soft enforcement of boundary conditions through penalty terms, which can lead to stiffness, gradient conflicts, and poor resolution of near-boundary flow structures. Hard constraints provide an alternative by encoding boundary conditions exactly into the network ansatz, but may introduce undesirable non-local effects due to the global nature of the approximation. To address these challenges, finite basis PINNs (FBPINNs), which are based on domain decomposition and localisation principles, are used together with hard boundary constraints that efficiently encode perforation-related boundary conditions. This approach helps mitigate spectral bias, improves overall accuracy, and exhibits convergence that is only weakly affected by the number of perforations, thereby providing an efficient and highly parallelisable neural network framework. The proposed approach is further supported with theoretical arguments, specifically focusing on the localisation and approximation properties of FBPINNs.

physics.flu-dyn↗

arXiv2026

3D Finite Element-Based Multiphysics Simulation of a Shape Memory Alloy Hybrid Composite Module

Shape adaptive shape memory alloy hybrid composites (SMAHCs) are composites that incorporate shape memory alloys (SMAs) to realize shape transformation. Despite the availability of numerous analytical and finite element models for predicting the transient response of SMAHCs, many approaches exhibit limitations with respect to the thermomechanical coupling and comprehensive experimental validation. Therefore, this paper presents a coupled, multiphysics, 3D finite element approach for the simulation of a SMAHC actuator, integrating mechanical, thermal and electromagnetic solvers in the Finite Element Code ANSYS LS-DYNA. The proposed approach employs a micromechanical constitutive model implemented in ANSYS LS-DYNA, to accurately capture the complex thermomechanical phase transformation of SMAs. A key feature of the model is the ability to prescribe a defined martensitic pre-strain through a preceding simulation step, in which an initially scaled SMA wire is mechanically loaded and stretched to its nominal length. This procedure enables partial detwinning of the martensitic microstructure and provides a physically motivated initialization of the material state. Joule heating of the SMA wires, as well as varying mechanical loads and ambient temperature conditions, are explicitly considered. The simulation results are validated against experimental data and a fully coupled transient staggered scheme model to assess the predictive capability of the 3D approach. The results show good qualitative agreement, reproducing the characteristic hysteresis of actuator deflection as a function of temperature. Quantitatively, the predicted deflections are of the correct order of magnitude, although marginally outside the 95 % experimental confidence interval. Overall, a consistent trend between simulation and experiment is observed, giving rise to possibility of simulating more complex SMAHC systems.

physics.app-ph↗

arXiv2025

Hybrid machine learning based scale bridging framework for permeability prediction of fibrous structures

This study introduces a hybrid machine learning-based scale-bridging framework for predicting the permeability of fibrous textile structures. By addressing the computational challenges inherent to multiscale modeling, the proposed approach evaluates the efficiency and accuracy of different scale-bridging methodologies combining traditional surrogate models and even integrating physics-informed neural networks (PINNs) with numerical solvers, enabling accurate permeability predictions across micro- and mesoscales. Four methodologies were evaluated: Single Scale Method (SSM), Simple Upscaling Method (SUM), Scale-Bridging Method (SBM), and Fully Resolved Model (FRM). SSM, the simplest method, neglects microscale permeability and exhibited permeability values deviating by up to 150\% of the FRM model, which was taken as ground truth at an equivalent lower fiber volume content. SUM improved predictions by considering uniform microscale permeability, yielding closer values under similar conditions, but still lacked structural variability. The SBM method, incorporating segment-based microscale permeability assignments, showed significant enhancements, achieving almost equivalent values while maintaining computational efficiency and modeling runtimes of ~45 minutes per simulation. In contrast, FRM, which provides the highest fidelity by fully resolving microscale and mesoscale geometries, required up to 270 times more computational time than SSM, with model files exceeding 300 GB. Additionally, a hybrid dual-scale solver incorporating PINNs has been developed and shows the potential to overcome generalization errors and the problem of data scarcity of the data-driven surrogate approaches. The hybrid framework advances permeability modelling by balancing computational cost and prediction reliability, laying the foundation for further applications in fibrous composite manufacturing.

cs.LG↗

arXiv2024

Quantitative Fiber Orientation Analysis of Carbon Fiber Sheet Molding Compounds using Polarization Imaging and X-Ray Computed Tomography

For the quality inspection of carbon fiber sheet molding compounds, polarization imaging is a promising alternative to more established methods like computed tomography, since it is cheaper, faster, and provides a larger field-of-view. For uni- and bidirectional carbon fiber reinforced composite materials, machine vision cameras with on-chip polarized image sensors have been successfully validated for visualizing fiber orientation. Although this imaging technique is already being applied to multidirectional materials, to our knowledge, it has not yet been validated for such materials. In this paper, fiber orientations obtained by angle of linear polarization images (AOLP) of commercially available pressed carbon fiber sheet molding compound materials are compared with orientations estimated from computed tomography scans. The fiber orientations in the computed tomography images are estimated using the maximal response of anisotropic Gaussian filters and the deviation between orientations estimated from polarization and computed tomography images is calculated. Both imaging methods showed encouraging visual similarity, but also notable numerical differences, which are discussed in depth. Moreover, it is shown that the surface layer fiber orientation is representative of the fiber orientation through the entire specimen.

physics.ins-det↗