Experimental investigation of turbulent characteristics in pore-scale regions of porous media

Nguyen, Thien; King, Stephen; Hassan, Yassin A.

doi:10.1007/s00348-021-03171-1

Cited by 8 publications

(3 citation statements)

References 91 publications

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“…From an experimental perspective, Guo et al and Nguyen et al noted a lack of experimental research on turbulent cross-flow through intricate porous structures [7,8]. Additionally, Hilliard et al highlighted the challenges of replicating porous flow in natural and anthropogenic systems under laboratory conditions [9].…”

Section: Challenges For Studying Composite Porous Fluid Systemsmentioning

confidence: 99%

Physics-informed neural network for turbulent flow reconstruction in composite porous-fluid systems

Jang,

Jadidi,

Rezaeiravesh

et al. 2024

Mach. Learn.: Sci. Technol.

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This study explores the implementation of Physics-Informed Neural Networks (PINN) to analyze turbulent flow in composite porous-fluid systems. These systems are composed of a fluid-saturated porous medium and an adjacent fluid, where the flow properties are exchanged across the porous-fluid interface. The PINN model employs a novel approach combining supervised learning and enforces fidelity to flow physics through penalization by the Reynolds-Averaged Navier-Stokes (RANS) equations. Two cases were simulated for this purpose: solid block, i.e., porous media with zero porosity, and porous block with a defined porosity. The effect of providing internal training data on the accuracy of the PINN predictions for prominent flow features including leakage, channeling effect and wake recirculation were investigated. Additionally, L2 norm error, which evaluates the prediction accuracy for flow variables was studied. Furthermore, PINN training time in both cases with internal training data were considered in this study. The results showed that the PINN predictions achieved high accuracy for the prominent flow features compared to the reference RANS data. In addition, second-order internal training data in the wall-normal direction reduced the L2 norm error by 100% for the solid block case, while for the porous block case, providing training data at the porous-fluid interface, increased the prediction accuracy by nearly 40% for second-order statistics. The study elucidates the impact of the internal training data distribution on the PINN training in complex turbulent flow dynamics, underscoring the necessity of turbulent second-order statistics variables in PINN training and an additional velocity gradient treatment to enhance PINN prediction.

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Section: Challenges For Studying Composite Porous Fluid Systemsmentioning

confidence: 99%

Physics-informed neural network for turbulent flow reconstruction in composite porous-fluid systems

Jang,

Jadidi,

Rezaeiravesh

et al. 2024

Mach. Learn.: Sci. Technol.

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“…Pore-scale insights into the flow characteristics can be obtained by both experimental and modelling studies. The experimental approaches usually utilise optical techniques such as microparticle image velocimetry (PIV) for the visualisation of flow in pore space 13,21 . X-ray CT imaging and magnetic resonance imaging (MRI) are the other advanced techniques for the visualisation of fluid flow in porous media 22,23 .…”

Section: Introductionmentioning

confidence: 99%

“…X-ray CT imaging and magnetic resonance imaging (MRI) are the other advanced techniques for the visualisation of fluid flow in porous media 22,23 . The experimental studies are crucial for not only shedding light on the dynamics of flow in porous structures but also providing an experimental database to support the verification and improvement of numerical approaches 21 . However, there are some limiting factors hindering their extensive application in different disciplines.…”

Section: Introductionmentioning

confidence: 99%

On the effect of flow regime and pore structure on the flow signatures in porous media

Farahani

Nezhad

2022

Physics of Fluids

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In this study, lattice Boltzmann method (LBM) is utilised for three-dimensional simulation of fluid flow through two porous structures, consisting of grains with the same diameter: (i) a homogeneous porous domain, in which the grains are placed with a simple cubic packing configuration, and (ii) a randomly-packed porous domain. An ultra-fine mesh size is considered to perform the simulations in three orders of magnitude of Reynolds number (), covering laminar to turbulent flow regimes, and capture different flow signatures. Pore velocity fields are derived, and their sample probability density functions (PDF) are analyzed versus time to investigate the dynamics of the flow. The analysis of the PDFs clearly shows that stagnant zones play a significant role in the formation of the pore flow fields, manifested by multimodal PDFs, and the distribution of the velocities in porous media at various cannot be characterized by a single PDF model regardless of the pore structure. While the velocities at the stagnant regions and in the vicinity of the solid boundaries are primarily affected by the viscous forces and exhibit a power-law PDF at different , the velocities in the main (preferential) flow pathways away from the boundaries are shown to be influenced by the inertial forces, hence having an exponential PDF when is low. At high, however, depending on the tortuosity of the porous structure, the velocities may exhibit an exponential or even Laplace PDF.

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Magnetic resonance velocity imaging of turbulent gas flow in a packed bed of catalyst support pellets

Elgersma

Sederman

Mantle

et al. 2023

Chemical Engineering Journal

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Experimental investigation of turbulent characteristics in pore-scale regions of porous media

Cited by 8 publications

References 91 publications

Physics-informed neural network for turbulent flow reconstruction in composite porous-fluid systems

Physics-informed neural network for turbulent flow reconstruction in composite porous-fluid systems

On the effect of flow regime and pore structure on the flow signatures in porous media

Magnetic resonance velocity imaging of turbulent gas flow in a packed bed of catalyst support pellets

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