Generation of micro-scale finite element models from synchrotron X-ray CT images for multidirectional carbon fibre reinforced composites

Sencu, R.M.; Yang, Zhenjun; Wang, Y.C.; Withers, Philip J.; Rau, Christoph; Parson, A.; Soutis, C.

doi:10.1016/j.compositesa.2016.09.010

Cited by 86 publications

(41 citation statements)

References 28 publications

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“…Additionally, tomography studies on low-density carbon fiber materials and composites present similar physical properties [17,18]. Investigation of carbon fiber fatigue and breakage via micro-CT modelling demonstrated contiguous initial qualities [19][20][21], which indicates that the carbonization progressed at a substantial level. The physical properties of micro-CT analysis are presented in Table 4 and the tomography images of the carbonized fibers are demonstrated in Figure 11.…”

Section: Morphology/micro-computed Tomography Characterization-physicmentioning

confidence: 99%

Thermal Treatment of Melt-Spun Fibers Based on High Density PolyEthylene and Lignin

Goulis

Konstantopoulos

Kartsonakis

et al. 2017

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Abstract:The purpose of this study was the synthesis of novel low-cost carbon fibers along with the investigation of the optimal parameters of temperature and time for the stabilization of hybrid high-density polyethylene (HDPE) and lignin melt-spun fibers. These fibers were manufactured by physical compounding of HDPE and chemically-modified softwood kraft lignin (SKL) in order to produce green fiber precursors for carbon fiber synthesis. Stabilization tests were performed with respect to thermal treatment (physical method) and sulfonation treatment (chemical method). The results revealed that only chemical methods induce the desired thermal process-ability to the composite fibers in order to manufacture carbon fibers by using a simple method. This investigation shed light on the stabilization techniques of polymeric fibers in the absence of any cyclic groups in terms of environmentally-friendly mass production of carbon fibers using low-cost and green raw materials. This study facilitates incorporation of softwood lignin in homegrown polymeric fibers by a low-cost production process via melt-spinning of composite fibers, which were successfully stabilized using a facile chemical method and carbonized. Additionally, a comprehensive investigation of the thermal behavior of the samples was accomplished, by examining several ways and aspects of fiber thermal treating. The properties of all studied fibers are presented, compared, and discussed.

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Section: Morphology/micro-computed Tomography Characterization-physicmentioning

confidence: 99%

Thermal Treatment of Melt-Spun Fibers Based on High Density PolyEthylene and Lignin

Goulis

Konstantopoulos

Kartsonakis

et al. 2017

View full text Add to dashboard Cite

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“…This is the case when complex geometries characterize the model, which cannot be discretized with a structured mesh. This undesired situation is becoming more frequent since it is increasingly common to create meshes directly from complex CAD solid geometries [14,15] or from an X-Ray tomography [16,17,18,19]. Moreover, newly developed technologies, e.g., additive manufacturing, allow to create elaborated solids, with consequent demanding numerical simulations [20,21,22].…”

Section: Introductionmentioning

confidence: 99%

A robust adaptive algebraic multigrid linear solver for structural mechanics

Franceschini

Magri

Mazzucco

et al. 2019

Computer Methods in Applied Mechanics and Engineering

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The numerical simulation of structural mechanics applications via finite elements usually requires the solution of large-size and ill-conditioned linear systems, especially when accurate results are sought for derived variables interpolated with lower order functions, like stress or deformation fields. Such task represents the most time-consuming kernel in commercial simulators; thus, it is of significant interest the development of robust and efficient linear solvers for such applications. In this context, direct solvers, which are based on LU factorization techniques, are often used due to their robustness and easy setup; however, they can reach only superlinear complexity, in the best case, thus, have limited applicability depending on the problem size. On the other hand, iterative solvers based on algebraic multigrid (AMG) preconditioners can reach up to linear complexity for sufficiently regular problems but do not always converge and require more knowledge from the user for an efficient setup. In this work, we present an adaptive AMG method specifically designed to improve its usability and efficiency in the solution of structural problems. We show numerical results for several practical applications with millions of unknowns and compare our method with two state-of-the-art linear solvers proving its efficiency and robustness.

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“…An image analysis algorithm is then commonly applied, aiming to distinguish the individual fibres, yarns or struts in order to provide geometrical information such as their lengths, cross-sectional areas and orientations (e.g. Le et al [22], Latil et al [23], Sencu et al [24]). Especially in order to distinguish the constituents in densely packed, small-scale structures, these algorithms need to be rather complex.…”

Section: Introductionmentioning

confidence: 99%

Estimating fibres’ material parameter distributions from limited data with the help of Bayesian inference

Rappel

Beex

2019

European Journal of Mechanics - A/Solids

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Numerous materials are essentially structures of discrete fibres, yarns or struts. Considering these materials at their discrete scale, one may distinguish two types of intrinsic randomness that affect the structural behaviours of these discrete structures: geometrical randomness and material randomness. Identifying the material randomness is an experimentally demanding task, because many small fibres, yarns or struts need to be tested, which are not easy to handle. To avoid the testing of hundreds of constituents, this contribution proposes an identification approach that only requires a few dozen of constituents to be tested (we use twenty to be exact). The identification approach is applied to artificially generated measurements, so that the identified values can be compared to the true values. Another question this contribution aims to answer is how precise the material randomness needs to be identified, if the geometrical randomness will also influence the macroscale behaviour of these discrete networks. We therefore also study the effect of the identified material randomness to that of the actual material randomness for three types of structures; each with an increasing level of geometrical randomness.

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Generation of micro-scale finite element models from synchrotron X-ray CT images for multidirectional carbon fibre reinforced composites

Cited by 86 publications

References 28 publications

Thermal Treatment of Melt-Spun Fibers Based on High Density PolyEthylene and Lignin

Thermal Treatment of Melt-Spun Fibers Based on High Density PolyEthylene and Lignin

A robust adaptive algebraic multigrid linear solver for structural mechanics

Estimating fibres’ material parameter distributions from limited data with the help of Bayesian inference

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