The intrinsic permeability of microcracks in porous solids: Analytical models and Lattice Boltzmann simulations

Timothy, Jithender J.; Meschke, Günther

doi:10.1002/nag.2673

Cited by 9 publications

(5 citation statements)

References 62 publications

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“…The hydromechanical coupling takes place at the midpoint of a common edge of two adjacent Voronoi polygons (point C in Figure 2A), at which both mechanical and transport responses are available. The discrete formulation of a 1D lattice element is in the sense of 1D classical FEM, which is different from other lattice formulations, for instance, the lattice spring model 49 and lattice discrete particle model 50 …”

Section: Methodsmentioning

confidence: 99%

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Hydromechanical couplings of reinforced tensioned members of steel fiber reinforced concrete by dual lattice model

Pham

Sorelli

Fafard

et al. 2020

Num Anal Meth Geomechanics

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The durability of concrete structures strongly depends on the water and chloride penetration in cracked concrete during its service life. This work aims at modeling the damage effect of tension stiffening behavior on the permeability for concrete tie specimen under tensile load by a dual lattice model, which considers hydromechanical couplings. Three concrete materials, including normal strength concrete (NSC), steel fiber reinforced concretes (SFRC), and ultra high‐performance fiber reinforced concrete (UHPFRC), are considered. The hydromechanical lattice model is based on a dual element network modeling: the water transport and the mechanical response. The fiber bridging effect is considered by means of the cohesive law of Mazars. The water flow in the damaged conduit elements is proportional to the cube of the crack width, which results from the damage variable. Experimental results available in open literature for both NSC and SFRC tie specimens are used to analyze and validate the proposed model. Considering a UHPFRC tie specimen, the model well predicted the load drops due to macrocrack occurrence, load hardening, and permeability evolution. Based on the present results, the current lattice hydromechanical model is a useful tool for predicting the service life of steel bar reinforcing concrete structure with and without steel fiber reinforcement.

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Section: Methodsmentioning

confidence: 99%

“…The discrete formulation of a 1D lattice element is in the sense of 1D classical FEM, which is different from other lattice formulations, for instance, the lattice spring model 49 and lattice discrete particle model. 50…”

Section: Hydromechanical Lattice Modelmentioning

confidence: 99%

Hydromechanical couplings of reinforced tensioned members of steel fiber reinforced concrete by dual lattice model

Pham

Sorelli

Fafard

et al. 2020

Num Anal Meth Geomechanics

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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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“…Numerous studies have been conducted to link theories and soil experiments to gain further knowledge on the Forchheimer formula for non-Darcy flows [8,22,[34][35][36]42]. However, the nature of geomaterials has prevented ideal conditioning and observation and has hindered the systematic construction of relationships between material properties and permeability.…”

Section: Introductionmentioning

confidence: 99%

Unified permeability modeling for transitional Darcy/non-Darcy flow based on 3D pore-level numerical flow tests

Nomura,

Itto,

Takase

et al. 2024

Preprint

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This study presents a unified representation of the seepage characteristics of virtual soil, including the transition zone from Darcy to non-Darcy flow, by conducting 3D pore-level fluid simulations.　According to the concept of mathematical homogenization, the process of defining a virtual test region as a representative volume element (RVE) and then assessing the apparent permeability from virtual 3D pore flow tests in this region is established as “numerical seepage flow testing” (NSFT).　Rigid particles of a single size are placed in the virtual test area, with two types of particle configurations: regularly arranged and randomly arranged. Both Darcy and non-Darcy flows are achieved by varying the macroscopic hydraulic gradients and other material or geometrical properties for NSFTs, such as grain diameter or porosity, and the macroscopic seepage flow characteristics are discussed in terms of the relationship between the apparent permeability and Reynolds number. After confirming that the individual relationships depend on the material or geometrical properties, we propose a unified expression of apparent permeability by introducing the “permeability reduction ratio”, and various empirically derived relationships between Darcy and non-Darcy flow speed and hydraulic gradient are used as references for this expression. To validate the proposed unified expression of apparent permeability over a wide range of Reynolds numbers, the NSFT results are illustrated as a relationship between the Reynolds number and permeability reduction ratio when the porosity is fixed. The illustrated relationships confirm that the permeability reduction ratio turns out to be a function of the Reynolds number and porosity only, thus validating the proposed unified expression. Additionally, the influence of the regularity of the particle arrangement and the particle size distribution characteristics of the NSFT specimens is raised as a factor determining the functional form of the permeability reduction ratio.

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The intrinsic permeability of microcracks in porous solids: Analytical models and Lattice Boltzmann simulations

Cited by 9 publications

References 62 publications

Hydromechanical couplings of reinforced tensioned members of steel fiber reinforced concrete by dual lattice model

Hydromechanical couplings of reinforced tensioned members of steel fiber reinforced concrete by dual lattice model

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

Unified permeability modeling for transitional Darcy/non-Darcy flow based on 3D pore-level numerical flow tests

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