Work

Multiphysics-Guided Neural Networks Integrating Damage Mechanics and Thermodynamic Dissipation for PMMA Plate Impact Analysis Under Pressure Loading

Year: 2026

Type: article

Abstract: The impact resistance of thin PMMA plates in deep-sea transparent structures is important for crashworthiness safety and is governed by high-strain-rate loading and hydrostatic pressure. Conventional empirical constitutive models exhibit limited adaptability to strain ratio effects and complex energy dissipation, leading to significant deviations in anti-impact assessment. This study proposes a constitutive-aware physics-informed neural network framework that integrates a data-driven constitutive neural network with embedded physical constraints. Such a proposed constitutive network replaces the theoretical model to capture rate-dependent stress response, while a physics-informed network enforces momentum conservation, energy balance, thermodynamic dissipation inequality, and pressure-induced stress limits. A dynamically weighted multimodal loss function is proposed and is used to ensure stable constraint enforcement during data training. Experimental validation based on a high-speed impact test confirms the constitutive and physics constraint network accuracy. Numerical results demonstrate that increasing hydrostatic pressure alters energy dissipation, increasing residual projectile velocities, and degrading impact resistance. The framework provides an efficient, physically consistent method for evaluating anti-impact performance of thin-walled PMMA structures in deep-sea engineering.

Source: Applied Sciences

DOI: 10.3390/app162010187

Authors: Xiangyu Qiu, Qinghai Du, Tianyu Xia, Ye Li

Institution: Shanghai Ocean University

Language: English

Topic: High-Velocity Impact and Material Behavior

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: National Natural Science Foundation of China, Shanghai Ocean University