Purpose

Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

Design/methodology/approach

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

Findings

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

Originality/value

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.

中文翻译：

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利用卷积神经网络估计可变形多孔介质的渗透率

 目的

准确评估流体流动行为并确定变形多孔介质的渗透率非常耗时且仍然具有挑战性。本文旨在提出一种新颖的机器学习方法，用于在水力耦合分析约束下的不同变形阶段快速估计多孔介质的渗透率。

设计/方法论/途径

本文提出了一种卷积神经网络（CNN），该网络以水力耦合过程中机械变形平衡方程和流体动力学玻尔兹曼方程的有限元耦合分析结果为指导[记为有限元格玻尔兹曼本文中的模型（FELBM）]。 FELBM 确保使用非结构化网格对耦合流体流动进行格子玻尔兹曼分析，该网格随机械变形产生的相应节点位移而变化。它为使用 CNN 的不同阶段的渗透率估计提供可靠的标签数据。

 发现

所提出的 CNN 可以快速准确地估计可变形多孔介质的渗透率，显着减少处理时间。应用研究表明，测试集和验证集在预测可变形多孔介质的渗透率方面具有很高的准确性。验证集的相应相关系数( R ² )为0.93，测试集A和测试集B的R ²分别为0.93和0.94。

 原创性/价值

本研究提出了一种利用 CNN 的创新方法，以 FELBM 耦合分析为指导，快速估计动态变形下多孔介质的渗透率。 CNN 快速而准确的性能凸显了其未来应用的巨大潜力。