Two-dimensional simulations of resin flow in dual-scale fibrous porous medium under constant pressure

The paper deals with the initial motion of a two-dimensional bubble starting from rest in the form of a cylinder with its axis horizontal. The theory is based on the assumptions of irrotational motion in the liquid round the bubble, constant pressure within the bubble, and small displacements from the cylindrical form. This theory predicts that the bubble should rise with the acceleration of gravity, over a distance of at least the initial bubble radius, and that a tongue of liquid should be projected up from the base of the bubble into its interior. These predictions are confirmed by experiments which also show how the vorticity necessary for steady motion in the spherical-cap form is generated by the detachment of two small bubbles from the back of the main bubble.

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Permeability Modeling Considering Microstructure of Preform

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.334-335.437 ◽

2007 ◽

Vol 334-335 ◽

pp. 437-440 ◽

Cited By ~ 1

Author(s):

Do Hoon Lee ◽

Joon Ho Lee ◽

Woo I. Lee

Keyword(s):

Porous Medium ◽

Unsaturated Flow ◽

Manufacturing Industries ◽

Experimental Measurements ◽

Resin Flow ◽

Flow Conditions ◽

Composite Manufacturing ◽

Liquid Molding ◽

Flow Through ◽

Inhomogeneous Microstructure

Liquid molding processes are becoming more popular among the composite manufacturing industries due to their versatility and economy among other merits. In analyzing the flow during the process, permeability is the most important parameter. Permeability has been regarded as a property of the porous medium. However, in many practical cases, the value may vary depending on the flow conditions such as the flow rate. It is speculated that this deviation is caused by inhomogeneous microstructure of the medium. In this study, numerical simulations as well as experimental measurements have been done to investigate the cause of deviation. Microstructure of porous medium was modeled as an array of porous cylinders. Resin flow through the array was simulated numerically. Simulations were performed for two different flow conditions, namely saturated flow and unsaturated flow. Based upon the results, permeabilities were estimated and compared for the two flow conditions. In addition, a model was proposed to predict the permeability for different flow conditions. Results showed that experimental data were in agreement with the prediction by the model.

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Multi-scale modeling and online monitoring of resin flow through dual-scale textiles in liquid composite molding processes

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-018-1703-9 ◽

2018 ◽

Vol 96 (5-8) ◽

pp. 2215-2230 ◽

Cited By ~ 16

Author(s):

Pierpaolo Carlone ◽

Felice Rubino ◽

Valentino Paradiso ◽

Fausto Tucci

Keyword(s):

Online Monitoring ◽

Resin Flow ◽

Liquid Composite Molding ◽

Multi Scale ◽

Scale Modeling ◽

Composite Molding ◽

Flow Through ◽

Multi Scale Modeling ◽

Dual Scale

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Determination of the Permeability Tensor of Fibrous Reinforcements for VARTM

10.1115/imece1999-0136 ◽

1999 ◽

Author(s):

Pavel B. Nedanov ◽

Suresh G. Advani ◽

Shawn W. Walsh ◽

William O. Ballata

Keyword(s):

Constant Pressure ◽

Permeability Tensor ◽

Three Dimensions ◽

Resin Flow ◽

Flow Front ◽

Resin Impregnation ◽

Permeable Media ◽

Front Position ◽

Transverse Permeability

Abstract VARTM and SCRIMP composite manufacturing processes use a highly permeable media to distribute the resin through the thickness of the composite. Hence, manufacturing simulations of resin flow in such processes requires reliable data for in-plane as well as transverse permeability. The goal of this study is to propose a method for simultaneous determination of the principal values of 3D-permeability tensor of fibrous reinforcements. The permeability components are calculated from experimental data, consisting of flow front position with time during resin impregnation in three dimensions from a radial source under constant pressure using the SMARTweave [Walsh (1993), Fink et al.(1995)] sensor system. Experimental results are compared with numerical simulation.

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COMPUTATIONAL MODELING OF RTM AND LRTM PROCESSES APPLIED TO COMPLEX GEOMETRIES

Revista de Engenharia Térmica ◽

10.5380/reterm.v11i1-2.62007 ◽

2012 ◽

Vol 11 (1-2) ◽

pp. 93 ◽

Cited By ~ 2

Author(s):

J. Da S. Porto ◽

M. Letzow ◽

E. D. Dos Santos ◽

S. C. Amico ◽

J. A. Souza ◽

...

Keyword(s):

Porous Medium ◽

Flow Behavior ◽

Resin Transfer Molding ◽

Three Dimensional ◽

Complex Geometries ◽

Resin Flow ◽

Transfer Molding ◽

Rtm Process ◽

General Terms ◽

Filling Time

Light Resin Transfer Molding (LRTM) is a variation of the conventional manufacturing process known as Resin Transfer Molding (RTM). In general terms, these manufacturing processes consist of a closed mould with a preplaced fibrous preform through which a polymeric resin is injected, filling the mold completely, producing parts with complex geometries (in general) and good finish. Those processes differ, among other aspects, in the way that injection occurs. In the RTM process the resin is injected through discrete points whereas in LRTM it is injected into an empty channel (with no porous medium) which surrounds the entire mold perimeter. There are several numerical studies involving the RTM process but LRTM has not been explored enough by the scientific community. Based on that, this work proposes a numerical model developed in the FLUENT package to study the resin flow behavior in the LRTM process. Darcy’s law and Volume of Fluid method (VOF) are used to treat the interaction between air and resin during the flow in the porous medium, i.e. the mold filling problem. Moreover, two three-dimensional geometries were numerically simulated considering the RTM and LRTM processes. It was possible to note the huge differences about resin flow behavior and filling time between these processes to manufacture the same parts.

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Predicting porosity, permeability, and tortuosity of porous media from images by deep learning

Scientific Reports ◽

10.1038/s41598-020-78415-x ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Krzysztof M. Graczyk ◽

Maciej Matyka

Keyword(s):

Neural Networks ◽

Porous Medium ◽

Porous Media ◽

Deep Learning ◽

Lattice Boltzmann ◽

Good Accuracy ◽

Two Dimensional ◽

Empirical Estimate ◽

Flow Through ◽

Boltzmann Method

AbstractConvolutional neural networks (CNN) are utilized to encode the relation between initial configurations of obstacles and three fundamental quantities in porous media: porosity ($$\varphi$$ φ ), permeability (k), and tortuosity (T). The two-dimensional systems with obstacles are considered. The fluid flow through a porous medium is simulated with the lattice Boltzmann method. The analysis has been performed for the systems with $$\varphi \in (0.37,0.99)$$ φ ∈ ( 0.37 , 0.99 ) which covers five orders of magnitude a span for permeability $$k \in (0.78, 2.1\times 10^5)$$ k ∈ ( 0.78 , 2.1 × 10 5 ) and tortuosity $$T \in (1.03,2.74)$$ T ∈ ( 1.03 , 2.74 ) . It is shown that the CNNs can be used to predict the porosity, permeability, and tortuosity with good accuracy. With the usage of the CNN models, the relation between T and $$\varphi$$ φ has been obtained and compared with the empirical estimate.

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