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Polymers
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Constitutive Modeling of Polymer Matrix Composites
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Constitutive Modeling of Polymer Matrix Composites

A special issue of Polymers (ISSN 2073-4360). This special issue belongs to the section "Polymer Physics and Theory".

Deadline for manuscript submissions: 25 December 2025 | Viewed by 410

Special Issue Editor

Prof. Dr. Xiaoqiang Zhou

E-Mail Website
Guest Editor
Department of Mechanics, School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan, China
Interests: polymer-matrix composites; solid mechanics; wave motion; constitutive modeling; data-driven computational mechanics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Polymers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • polymer matrix composites
  • constitutive modeling
  • multi-scale modeling
  • data-driven
  • machine learning
  • multi-physical coupling

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Published Papers (2 papers)

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Research

26 pages, 7957 KiB  
Article
Elastoplastic Modeling of Kevlar® Composite Laminates: A Cyclic Loading Approach for In-Plane Characterization
by Rene Alejandro Canceco de la Cruz, Luis Adrián Zúñiga Avilés, Gabriel Plascencia Barrera, Alberto Díaz Díaz and José Martin Herrera Ramírez
Polymers 2025, 17(16), 2235; https://doi.org/10.3390/polym17162235 (registering DOI) - 17 Aug 2025
Abstract
This study investigates the elastoplastic behavior of phenol formaldehyde/polyvinyl butyral matrix (70% PF/30% PVB) reinforced with Kevlar® fibers through comprehensive in-plane tensile testing. Cyclic loading–unloading tests were conducted at a 100%/min strain rate using a universal testing system at room temperature on [...] Read more.
This study investigates the elastoplastic behavior of phenol formaldehyde/polyvinyl butyral matrix (70% PF/30% PVB) reinforced with Kevlar® fibers through comprehensive in-plane tensile testing. Cyclic loading–unloading tests were conducted at a 100%/min strain rate using a universal testing system at room temperature on 04, 904, and ±45s laminates. The experimental results revealed significant nonlinear hardening behavior beyond yield stress, accompanied by yarn stiffening effects during loading cycles. A novel elastoplastic constitutive model was developed, incorporating Hill’s yield criterion adapted for orthotropic materials and an isotropic hardening function that accounts for equivalent plastic strains and progressive yarn stiffening. Laminates with other stacking sequences were also tested and the accuracy of the predictions of the nonlinear behavior was assessed. In these laminates, delaminations took place and the model provided an overestimation of the stress–strain response. Since the model could not predict delamination onset and propagation, an adaptation of the model considering fully delaminated interfaces brought a lower bound of this response. Despite the limitations of the model, it can be used to provide reasonable limits to the stress–strain response of laminates accounting for plastic strains within plies. This study provides essential mechanical properties and constitutive relationships for designing Kevlar® composite structures with tailored stiffness characteristics for impact-resistant applications. Full article
(This article belongs to the Special Issue Constitutive Modeling of Polymer Matrix Composites)
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16 pages, 4484 KiB  
Article
Microscale Flow Simulation of Resin in RTM Process for Optical Fiber-Embedded Composites
by Tianyou Lu, Bo Ruan, Zhanjun Wu and Lei Yang
Polymers 2025, 17(15), 2076; https://doi.org/10.3390/polym17152076 - 29 Jul 2025
Viewed by 276
Abstract
By embedding optical fiber sensors into fiber preforms and utilizing liquid molding processes such as resin transfer molding (RTM), intelligent composite materials with self-sensing capabilities can be fabricated. In the liquid molding process of these intelligent composites, the quality of the final product [...] Read more.
By embedding optical fiber sensors into fiber preforms and utilizing liquid molding processes such as resin transfer molding (RTM), intelligent composite materials with self-sensing capabilities can be fabricated. In the liquid molding process of these intelligent composites, the quality of the final product is highly dependent on the resin flow and impregnation effects. The embedding of optical fibers can affect the microscopic flow and impregnation behavior of the resin; therefore, it is necessary to investigate the specific impact of optical fiber embedding on the resin flow and impregnation of fiber bundles. Due to the difficulty of directly observing this process at the microscopic scale through experiments, numerical simulation has become a key method for studying this issue. This paper focuses on the resin micro-flow in RTM processes for intelligent composites with embedded optical fibers. Firstly, a steady-state analysis of the resin flow and impregnation process was conducted using COMSOL 6.0 obtaining the velocity and pressure field distribution characteristics under different optical fiber embedding conditions. Secondly, the dynamic process of resin flow and impregnation of fiber bundles at the microscopic scale was simulated using Fluent 2022R2. This study comprehensively analyzes the impact of different optical fiber embedding configurations on resin flow and impregnation characteristics, determining the impregnation time and porosity after impregnation under different optical fiber embedding scenarios. Additionally, this study reveals the mechanisms of pore formation and their distribution patterns. The research findings provide important theoretical guidance for optimizing the RTM molding process parameters for intelligent composite materials. Full article
(This article belongs to the Special Issue Constitutive Modeling of Polymer Matrix Composites)
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