Advanced computational modelling of composite materials
[Display omitted] •An in-depth review of computational methods for the failure modelling of composite materials.•The significance of multi-scale and multi-physics modelling approaches in composite materials.•The emerging methods and future directions of composite failure modelling. This review paper...
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| Published in: | Engineering fracture mechanics Vol. 305; p. 110120 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
08.07.2024
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| Subjects: | |
| ISSN: | 0013-7944, 1873-7315 |
| Online Access: | Get full text |
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| Summary: | [Display omitted]
•An in-depth review of computational methods for the failure modelling of composite materials.•The significance of multi-scale and multi-physics modelling approaches in composite materials.•The emerging methods and future directions of composite failure modelling.
This review paper presents an overview of computational methods for modelling the failure of composite materials, with a focus on fracture modelling. The paper begins by discussing the principles and concepts of continuum damage mechanics (CDM), phase field method (PFM), cohesive zone model (CZM) and discrete element method (DEM), highlighting their ability to predict crack initiation, propagation, and coalescence. The paper also includes case studies and examples that demonstrate the effectiveness and limitations of each method in simulating fracture behaviour in different composite materials. We then review existing methods for modelling the deformation and fracture behaviour of composite material under dynamic loading. Additionally, the significance of multiscale modelling, multi-physics modelling and data-driven methods in composite failure analysis is discussed. Multiscale models provide a comprehensive understanding of deformation and fracture across various length scales, while multi-physics modelling can provide valuable insights into failure mechanisms when multiple physical phenomena are coupled, such as hygrothermal degradation of composite materials. On the other hand, data-driven methods enhance fracture and multiscale modelling through machine learning and statistical techniques. Current challenges and recommendations for future work have also been articulated. |
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| ISSN: | 0013-7944 1873-7315 |
| DOI: | 10.1016/j.engfracmech.2024.110120 |