A comparative review of multiscale models for effective properties of nano- and micro-composites

El‐Masry, Ahmed M.; Azoti, Wiyao; El‐Safty, Sherif A.; Elmarakbi, Ahmed

doi:10.1016/j.pmatsci.2022.101022

Cited by 28 publications

(9 citation statements)

References 298 publications

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“…Below these scales, discrete mechanical approaches are usually more appropriate for modelling purposes. The reader is referred to the works of Orifici and colleagues [210], Kanoute and colleagues [211], Soutis and Beaumont [212], and Elmasry and colleagues [213] for further details on FRP constitutive and multi-scale modelling. A computational model of these three scales in the case of a hybrid composite laminate is depicted in Figure 6, which illustrates the interactions between the different composite constituents at each length scale.…”

Section: Continuum Modelsmentioning

confidence: 99%

Mechanisms of Component Degradation and Multi-Scale Strategies for Predicting Composite Durability: Present and Future Perspectives

Ferreira Rocha,

Fonseca Gonçalves,

dos Reis

et al. 2024

J. Compos. Sci.

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Composite materials, valued for their adaptability, face challenges associated with degradation over time. Characterising their durability through traditional experimental methods has shown limitations, highlighting the need for accelerated testing and computational modelling to reduce time and costs. This study presents an overview of the current landscape and future prospects of multi-scale modelling for predicting the long-term durability of composite materials under different environmental conditions. These models offer detailed insights into complex degradation phenomena, including hydrolytic, thermo-oxidative, and mechano-chemical processes. Recent research trends indicate a focus on hygromechanical models across various materials, with future directions aiming to explore less-studied environmental factors, integrate multiple stressors, investigate emerging materials, and advance computational techniques for improved predictive capabilities. The importance of the synergistic relationship between experimental testing and modelling is emphasised as essential for a comprehensive understanding of composite material behaviour in diverse environments. Ultimately, multi-scale modelling is seen as a vital contributor to accurate predictions of environmental effects on composite materials, offering valuable insights for sustainable development across industries.

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Section: Continuum Modelsmentioning

confidence: 99%

Mechanisms of Component Degradation and Multi-Scale Strategies for Predicting Composite Durability: Present and Future Perspectives

Ferreira Rocha,

Fonseca Gonçalves,

dos Reis

et al. 2024

J. Compos. Sci.

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“…The accuracy of these methods was unsatisfactory in assessing the properties of AM-CFRPCs due to various limitations. [6][7][8][9] Fiber volume fraction has been investigated in detail via numerous experiments. [10][11][12][13] However, the current research of the combined effect of fiber content and porosity on structural properties is insufficient.…”

Section: Introductionmentioning

confidence: 99%

“…Within the realm of laminate composites, theoretical analysis methods such as the rule of mixtures, self‐consistent theory, and the Mori‐Tanaka method are founded upon the homogenization theory. The accuracy of these methods was unsatisfactory in assessing the properties of AM‐CFRPCs due to various limitations 6–9 . Fiber volume fraction has been investigated in detail via numerous experiments 10–13 .…”

Section: Introductionmentioning

confidence: 99%

A materials informatics framework based on reduced‐order models for extracting structure–property linkages of additively manufactured continuous fiber‐reinforced polymer composites

Zhang,

Shi,

et al. 2024

Polymer Composites

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The innovative combination of additive manufacturing (AM) and continuous fiber‐reinforced polymer composites (CFRPCs) confers products with the dual advantages of integrated manufacturing and designability of properties, but lack an efficient and reliable method for property prediction. This study presents a materials informatics framework using reduced‐order models and machine learning (ML) to extract the structure–property (SP) linkages between microstructures and macroscopic elastic properties of AM‐CFRPCs. The initial step involves generating microstructural 2D cross‐sections and representative volume elements (RVEs) with random fiber and pore distributions based on the minimum potential method. Then, finite element (FE) calculations are performed on RVEs to obtain nine macroscopic elastic properties. Following that, the quantification and dimensionality reduction of the 2D cross‐sectional dataset are conducted separately using two‐point spatial correlations and principal component analysis (PCA). Finally, a Bayesian optimized composite kernel support vector regression (CK‐SVR) algorithm is used to effectively establish complex mapping relationships between the reduced‐order representations of the microstructures and the mechanical properties. Despite the reduced‐order dataset containing only 3–6 variables, the framework generates an interpretable model exhibiting excellent accuracy with all predicted R2 values surpassing 0.91. Therefore, this framework presents a prospective solution for expediting the design and optimization of AM‐CFRPCs.Highlights A materials informatics scheme is proposed to predict the 9 elastic properties of AM‐CFRPCs. Microstructures are quantified and dimensionally reduced by two‐point statistics and PCA. SP linkages are established between 2D cross‐sections and 3D macromechanical properties. Modified CK‐SVR exhibits higher prediction accuracy compared to conventional models.

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“…Particle suspension is a complex fluid that contains a singlephase fluid and suspended particles. It has been extensively applied in natural science and industry fields, such as petroleum extraction [1,2], biomedical engineering [3,4], food processing [5,6], materials science [7,8], and heat transfer systems [9][10][11]. The complex interaction between particles and the base fluid in particle suspension may result in distinct flow and heat transfer characteristics [12].…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation Study on Heat Transfer Characteristics of Particle‐Loaded Flow in Microchannels

Song,

Guo,

Wang

et al. 2023

Chem Eng & Technol

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Particle suspension is considered powerful and perspective in enhancing heat transfer, but the unclear nature of particle‐loaded flow limits further understanding of particle suspension. Therefore, in this paper, orthogonal experiments and the arbitrary Lagrangian‐Eulerian method were used to investigate the heat transfer characteristics of particle‐loaded flow. Results indicate that when the particle is at the inlet of the microchannel, the factors arranged in important order are the Reynolds number, blockage ratio, particle initial position, specific heat capacity, and thermal conductivity. When the particle is far from the microchannel inlet, the factors arranged in important order are the Reynolds number, blockage ratio, particle initial position, thermal conductivity, and specific heat capacity.

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A comparative review of multiscale models for effective properties of nano- and micro-composites

Cited by 28 publications

References 298 publications

Mechanisms of Component Degradation and Multi-Scale Strategies for Predicting Composite Durability: Present and Future Perspectives

Mechanisms of Component Degradation and Multi-Scale Strategies for Predicting Composite Durability: Present and Future Perspectives

A materials informatics framework based on reduced‐order models for extracting structure–property linkages of additively manufactured continuous fiber‐reinforced polymer composites

Numerical Simulation Study on Heat Transfer Characteristics of Particle‐Loaded Flow in Microchannels

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