The Impact of Compaction and Sand Migration on Permeability and Non-Darcy Coefficient from Pore-Scale Simulations

Anbar, Sultan; Thompson, Karsten E.; Tyagi, Mayank

doi:10.1007/s11242-018-1190-3

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…Equation (3) directly considers relaxation towards equilibrium distribution, and transfer coefficients can be obtained easily using the value of τ. For example, the below formula is for kinematic viscosity (ν) (Anbar et al, 2019):…”

Section: Lattice Boltzmann Equation and Bgk Extensionmentioning

confidence: 99%

Design of Artificial Neural Network for predicting the reduction in permeability of porous media as a result of polymer gel injection

Kamel Targhi,

Emami Niri,

L.J. Zitha

2023

Geoenergy Science and Engineering

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Section: Lattice Boltzmann Equation and Bgk Extensionmentioning

confidence: 99%

Design of Artificial Neural Network for predicting the reduction in permeability of porous media as a result of polymer gel injection

Kamel Targhi,

Emami Niri,

L.J. Zitha

2023

Geoenergy Science and Engineering

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“…Proceeding with the same deformed granular assemblies (3D images extracted from the DEM RVE) as used for retention curve database generation in Section 3.2, LB single‐phase flow simulation is applied to generate the database of the pore‐fluid flow 89–97 . This database will then be used for the ANN‐based intrinsic permeability tensor estimation.…”

Section: Retention Behavior and Anisotropic Darcy Flow Representation In MLmentioning

confidence: 99%

An offline multi‐scale unsaturated poromechanics model enabled by self‐designed/self‐improved neural networks

Heider

Suh

Sun

2021

Num Anal Meth Geomechanics

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Supervised machine learning via artificial neural network (ANN) has gained significant popularity for many geomechanics applications that involves multi‐phase flow and poromechanics. For unsaturated poromechanics problems, the multi‐physics nature and the complexity of the hydraulic laws make it difficult to design the optimal setup, architecture, and hyper‐parameters of the deep neural networks. This paper presents a meta‐modeling approach that utilizes deep reinforcement learning (DRL) to automatically discover optimal neural network settings that maximize a pre‐defined performance metric for the machine learning constitutive laws. This meta‐modeling framework is cast as a Markov Decision Process (MDP) with well‐defined states (subsets of states representing the proposed neural network (NN) settings), actions, and rewards. Following the selection rules, the artificial intelligence (AI) agent, represented in DRL via NN, self‐learns from taking a sequence of actions and receiving feedback signals (rewards) within the selection environment. By utilizing the Monte Carlo Tree Search (MCTS) to update the policy/value networks, the AI agent replaces the human modeler to handle the otherwise time‐consuming trial‐and‐error process that leads to the optimized choices of setup from a high‐dimensional parametric space. This approach is applied to generate two key constitutive laws for the unsaturated poromechanics problems: (1) the path‐dependent retention curve with distinctive wetting and drying paths. (2) The flow in the micropores, governed by an anisotropic permeability tensor. Numerical experiments have shown that the resultant ML‐generated material models can be integrated into a finite element (FE) solver to solve initial‐boundary‐value problems as replacements of the hand‐craft constitutive laws.

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“…In the case of turbulent flow, several modifications to Darcy's Law have been proposed that introduce nonlinearity between gas velocity and pressure gradients. Nonlinear expressions between velocity and pressure gradients previously published in the literature are used for turbulent flows [6] [7]. Reported numerical results show that for Reynolds numbers greater than 1 the deviations from Darcy's Law begin to be significant and the linearity between the gas velocity and the pressure gradients is no longer valid.…”

Section: Introductionmentioning

confidence: 99%

Non-Darcy Flow in Molding Sands

Barron-Meza,

Reyes-Miranda,

Flores-Sanchez

2024

OJAppS

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Darcy's law is widely used to describe the flow in porous media in which there is a linear relationship between fluid velocity and pressure gradient. However, it has been found that for high numbers of Reynolds this law ceases to be valid. In this work, the Ergun equation is employed to consider the non-linearity of air velocity with the pressure gradient in casting sands. The contribution of non-linearity to the total flow in terms of a variable defined as a non-Darcy flow fraction is numerically quantified. In addition, the influence of the shape factor of the sand grains on the non-linear flow fraction is analyzed. It is found that for values of the Reynolds number less or equal than 1, the contribution of non-linearity for spherical particles is around 1.15%.

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The Impact of Compaction and Sand Migration on Permeability and Non-Darcy Coefficient from Pore-Scale Simulations

Cited by 9 publications

References 33 publications

Design of Artificial Neural Network for predicting the reduction in permeability of porous media as a result of polymer gel injection

Design of Artificial Neural Network for predicting the reduction in permeability of porous media as a result of polymer gel injection

An offline multi‐scale unsaturated poromechanics model enabled by self‐designed/self‐improved neural networks

Non-Darcy Flow in Molding Sands

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