Paper Titles
Preface
An Adaptive RVE Generation Algorithm for CFRP Composites Considering Pore Morphology and Spatial Statistics
p.3
A Preliminary Study on Designing a Composite Materials Hardness Testing Range Expander
p.13
Relevance of an Equivalent Circuit Model for the Discharge Phenomenon of Fibers Containing Fine Particles
p.23
Dynamic Contact Area Analysis of Composite Material Tractor Tires on Rigid Surfaces Using a Validated Finite Element Model
p.31
Investigation of Shear Band Effects on Energy Dissipation in Agricultural Non-Pneumatic Tires
p.39
Finite Element Analysis of Ply Orientation Effects in Carbon Fiber/Epoxy Composite External Fixators
p.47
Accessing the Residual Strain Development in Thick FRP Composite Laminates Using Embedded Temperature and FBG Strain Sensors
p.55
Sustainable Ecofriendly 3D Printed Composites for Thermal and Electrical Insulation Applications
p.63

An Adaptive RVE Generation Algorithm for CFRP Composites Considering Pore Morphology and Spatial Statistics

Article Preview

Abstract:

Manufacturing induced defects such as voids and non-uniform fiber distributions significantly affect the mechanical behavior of fiber reinforced polymer composites. However, generating high-fidelity representative volume elements (RVEs) that simultaneously capture fiber randomness and void characteristics remains a key challenge. In this work, we propose a novel adaptive fiber void generation (AFVG) algorithm to construct two-dimensional RVEs incorporating statistically controlled elliptical pores and randomly packed fibers. Based on an improved greedy placement scheme, the method integrates customized scoring functions and constraint checks to ensure realistic void morphology, orientation randomness, and fiber void spatial relationships, while maintaining target fiber volume fraction and porosity. The algorithm’s robustness is verified by spatial randomness metrics, and its effectiveness is demonstrated through finite element homogenization to predict the effective elastic moduli. Results confirm that the proposed method achieves high fiber packing efficiency and incorporates realistic void characteristics, providing a practical and extensible tool for multiscale modeling of composites with manufacturing defects.

You might also be interested in these eBooks
Composite Materials, Biomedical Materials, Additive Manufacturing, Processing and Forming Technologies View Preview

Info:

Periodical:

Key Engineering Materials (Volume 1058)

Pages:

3-12

DOI:

https://doi.org/10.4028/p-uacP17

Citation:

Cite this paper

Online since:

June 2026

Authors:

Ling Yan*, Meng Ze Li, Qing Wang

Keywords:

Fiber Reinforced Composites, Greedy Algorithm, Microscopic Models, Pore Defects, Representative Volume Element

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2026 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

References

[1] Me.M. Gorbatikh, L. Verpoest, Voids in Fiber-Reinforced Polymer Composites: A Review on Their Formation, Characteristics, and Effects on Mechanical Performance. J. Compos. Mater. (2019) 1579–1669.

DOI: 10.1177/0021998318772152

Google Scholar

[2] M.Z. Li, S.R. Li, Y.L Ke, Bottom-up Stochastic Multiscale Model for the Mechanical Behavior of Multidirectional Composite Laminates with Microvoids. Compos. Part A Appl. Sci. Manuf. 181 (2024).

DOI: 10.1016/j.compositesa.2024.108144

Google Scholar

[3] L. Wan, S.L. Millen, Multiscale Modelling of CFRP Composites Exposed to Thermo-Mechanical Loading from Fire. Compos. Part A Appl. Sci. Manuf. 187 (2024).

DOI: 10.1016/j.compositesa.2024.108481

Google Scholar

[4] M. L. Costa, S. F. Almeida, M. C. Rezende, The Influence of Porosity on the Interlaminar Shear Strength of Carbon/Epoxy and Carbon/Bismaleimide Fabric Laminates. Compos. Sci. Technol. (2001) 2101–2108.

DOI: 10.1016/s0266-3538(01)00157-9

Google Scholar

[5] R. Higuchi, T. Yokozeki, K. Nishida, High-Fidelity Computational Micromechanics of Composite Materials Using Image-Based Periodic Representative Volume Element. Compos. Sci. 328 (2024).

DOI: 10.1016/j.compstruct.2023.117726

Google Scholar

[6] D. He, Y. Chen, C. Breite, Multiscale Image-Based Modelling of Composite Materials. Int. Mater. Rev. (2025).

Google Scholar

[7] J. Fu, W. Tan, D. Xiao, X. Zhuang, Computational Intelligence in Stochastic Reconstruction of Porous Microstructures for Image-Based Poro/Micro-Mechanical Modeling. Arch Computat Methods Eng. (2025).

DOI: 10.1007/s11831-025-10313-9

Google Scholar

[8] A.F. Jahwari, H.E. Naguib, Finite Element Creep Prediction of Polymeric Voided Composites with 3D Statistical-Based Equivalent Microstructure Reconstruction. Compos. Part B Eng. (2016) 416–424.

DOI: 10.1016/j.compositesb.2016.06.042

Google Scholar

[9] Y. Zhang, Y. Li, X. Luan, Effects of Void Characteristics on the Mechanical Properties of Carbon Fiber Reinforced Composites: Micromechanical Modeling and Analysis. Polymers. 17 (2025).

DOI: 10.3390/polym17131721

Google Scholar

[10] T. David, F. Jacob, Generating a statistically equivalent representative volume element with discrete defects. Compos. Sci. (2016) 791-803.

DOI: 10.1016/j.compstruct.2016.06.077

Google Scholar

[11] Y.W. Kwon, D.H. Allen, R. Talreja, Multiscale Modeling and Simulation of Composite Materials and Structures. Multiscale Methods in Computational Mechanics: Progress and Accomplishments. Dordrecht: Springer Netherlands. (2010) 215-231.

DOI: 10.1007/978-90-481-9809-2_12

Google Scholar

[12] A. Stamopoulos, K. Tserpes, P. Prucha, Evaluation of Porosity Effects on the Mechanical Properties of Carbon Fiber-Reinforced Plastic Unidirectional Laminates by X-Ray Computed Tomography and Mechanical Testing. J. Compos. Mater. (2016) 2087-2098.

DOI: 10.1177/0021998315602049

Google Scholar

[13] B. Dewangan, N.D. Chakladar, Influence of Out‐of‐autoclave and Autoclave Manufacturing Processes on Mechanical Properties of Glass Fiber‐reinforced Epoxy Composite. Polym. Compos. (2024) 15998–16020.

DOI: 10.1002/pc.28885

Google Scholar

[14] D. Zhang, L. Zhan, B. Ma, Porosity and Residual Strain Analysis of CFRP Laminates Under a Novel Vibration‐Microwave Curing Process. Polym. Compos. (2025).

DOI: 10.1002/pc.70232

Google Scholar

[15] M.Z. Li, H.W. Zhang, Y.L. Ke, Greedy-Based Approach for Generating Anisotropic Random Fiber Distributions of Unidirectional Composites and Transverse Mechanical Properties Prediction. Comput. Mater. Sci. 218 (2023).

DOI: 10.1016/j.commatsci.2022.111966

Google Scholar