Constitutive Modeling of Polymer Matrix Composites

Dear Colleagues,

Polymer matrix composites (PMCs) exhibit intricate nonlinear, time-dependent, and multi-physics behaviors under diverse loading and environmental conditions. Developing predictive constitutive models is pivotal for advancing their applications in aerospace, automotive, energy, bioengineering, and defense systems. However, challenges persist in bridging experimental characterization with multi-scale theoretical frameworks, addressing environmental aging effects, and harnessing data-driven approaches to overcome computational and generalizability limitations.

The aim of this Special Issue is to compile cutting-edge research and reviews on constitutive modeling of PMCs, emphasizing the integration of experimental, theoretical, and computational advancements. Topics of interest include, but are not limited to, the following:

(1) Experimental characterization and model calibration/validation

• Viscoelasticity, viscoplasticity, and damage evolution.
• Hygrothermal, oxidative, and environmental aging effects.
• High-strain-rate and dynamic failure mechanisms.

(2) Multi-scale and multi-physics modeling

• Micromechanics of fiber–matrix interfaces and interphases.
• Rate-dependent behavior and fatigue life prediction.
• Uncertainty quantification and stochastic modeling.

(3) Data-driven and machine learning approaches

• ML-augmented constitutive laws and surrogate models.
• Neural networks for real-time property prediction.
• Digital twin frameworks integrating in situ monitoring data.
• Explainable AI and transfer learning for cross-material scaling.

(4) Emerging applications

• Extreme environment performance (e.g., cryogenic, hypervelocity).
• Recyclable and sustainable composite design.
• Model-guided high-strength/toughened material development.

Prof. Dr. Xiaoqiang Zhou
Guest Editor

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