Bulletin of the American Physical Society
APS March Meeting 2024
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session N00: Poster Session II (11:30am-2:30pm CST)
11:30 AM,
Wednesday, March 6, 2024
Room: Hall BC
Sponsoring
Unit:
APS/SPS
Abstract: N00.00227 : How to (numerically) calculate tortuosity in porous media?*
Presenter:
Maciej Matyka
(Faculty of Physics and Astronomy, Institute of Theoretical Physics, University of Wrocław, pl. M. Borna 9, 50-204 Wrocław, Poland)
Authors:
Maciej Matyka
(Faculty of Physics and Astronomy, Institute of Theoretical Physics, University of Wrocław, pl. M. Borna 9, 50-204 Wrocław, Poland)
Damian Śnieżek
(Faculty of Physics and Astronomy, Institute of Theoretical Physics, University of Wrocław, pl. M. Borna 9, 50-204 Wrocław, Poland)
Sahrish Naqvi
(Faculty of Physics and Astronomy, Institute of Theoretical Physics, University of Wrocław, pl. M. Borna 9, 50-204 Wrocław, Poland)
Dawid Strzelczyk
(Faculty of Physics and Astronomy, Institute of Theoretical Physics, University of Wrocław, pl. M. Borna 9, 50-204 Wrocław, Poland)
Krzysztof Graczyk
(Faculty of Physics and Astronomy, Institute of Theoretical Physics, University of Wrocław, pl. M. Borna 9, 50-204 Wrocław, Poland)
In this poster we will present several ways of tortuosity computation. Initially, we spotlight the streamline-based approach. To facilitate this, our docker-integrated OpenFOAM (FVM) framework —engineered to efficiently construct porous media and execute pore-scale fluid flow simulations — will be supplemented with Python script to compute multiple streamlines. We will then compare results derived from this method with those from the velocity-based procedure. The poster will shed light on the challenges posed by these methodologies, especially in conditions like the inertial regime where non-linear dynamics become prominent. Additionally, the discourse will touch upon meshless interpolation techniques suitable to both for streamlines as well as for the Lattice Boltzmann solver in the context of tortuosity. Concluding, we will explore a novel procedure utilizing a deep learning Convolutional Neural Network (CNN) approach, designed to determine tortuosity in randomized porous media. This approach proficiently calculates hydrodynamic and diffusive tortuosity.
*Funded by National Science Centre, Poland under the OPUS call in the Weave programme 2021/43/I/ST3/00228.
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