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In this work, we present a proof of concept for both 3D-printed media and a machine learning analysis methodology to investigate void formation and transport during liquid filling. Through a series of experiments, we characterized void formation and transport at constant flow rates using two calibrated fabric-like porous geometries created by Stereolithography and Multi Jet Fusion 3D printing techniques. Our findings highlight the significance of porous medium geometry and void size, in addition to the capillary number, in characterizing void formation and mobility during resin flow into a mold containing a fibrous preform. Notably, the paper's strength lies in the presentation of advanced bubble analysis methods, including frame-by-frame high-resolution video analysis, enabling the identification of individual bubbles and the extraction of their statistics, such as count, size, and velocity throughout the experiment. These insights contribute to the design of more efficient processes, resulting in composite parts with reduced void content.
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Reduced Model for Properties of Multiscale Porous Media with Changing Geometry
In this paper, we consider an important problem for modeling complex coupled phenomena in porous media at multiple scales. In particular, we consider flow and transport in the void space between the pores when the pore space is altered by new solid obstructions formed by microbial growth or reactive transport, and we are mostly interested in pore-coating and pore-filling type obstructions, observed in applications to biofilm in porous media and hydrate crystal formation, respectively. We consider the impact of these obstructions on the macroscopic properties of the porous medium, such as porosity, permeability and tortuosity, for which we build an experimental probability distribution with reduced models, which involves three steps: (1) generation of independent realizations of obstructions, followed by, (2) flow and transport simulations at pore-scale, and (3) upscaling. For the first step, we consider three approaches: (1A) direct numerical simulations (DNS) of the PDE model of the actual physical process called BN which forms the obstructions, and two non-DNS methods, which we call (1B) CLPS and (1C) LP. LP is a lattice Ising-type model, and CLPS is a constrained version of an Allen–Cahn model for phase separation with a localization term. Both LP and CLPS are model approximations of BN, and they seek local minima of some nonconvex energy functional, which provide plausible realizations of the obstructed geometry and are tuned heuristically to deliver either pore-coating or pore-filling obstructions. Our methods work with rock-void geometries obtained by imaging, but bypass the need for imaging in real-time, are fairly inexpensive, and can be tailored to other applications. The reduced models LP and CLPS are less computationally expensive than DNS, and can be tuned to the desired fidelity of the probability distributions of upscaled quantities.
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- PAR ID:
- 10296583
- Date Published:
- Journal Name:
- Computation
- Volume:
- 9
- Issue:
- 3
- ISSN:
- 2079-3197
- Page Range / eLocation ID:
- 28
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/computation9030028
