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arXiv 2501.06466physics.flu-dyncs.CVcs.LG

基于CNN的可变形多孔介质从微观到宏观的流动建模

CNN-powered micro- to macro-scale flow modeling in deformable porous media

Institute of Mechanics and Computational Mechanics (IBNM), Leibniz University Hannover(莱布尼茨汉诺威大学力学与计算力学研究所(IBNM));Institute of Applied Mechanics, University of Stuttgart(斯图加特大学应用力学研究所);Institute of General Mechanics, RWTH Aachen University(亚琛工业大学普通力学研究所)

Yousef Heider, Fadi Aldakheel, Wolfgang Ehlers

AI总结:

本研究针对可变形多孔介质渗透率传统估算效率低的问题,提出以二值化显微CT图像为输入、基于CNN预测对称二阶渗透率张量的方法,经Bentheim砂岩数据集和LBM模拟数据训练验证有效,可服务于多学科领域。

摘要:

本研究提出了一种新的应用,即利用少量真实微观几何结构的显微CT图像,预测可变形多孔介质的宏观固有渗透率张量。研究的核心目标是开发一种高效的基于机器学习(ML)的方法,以克服传统渗透率估算技术的局限性——传统方法往往依赖耗时的实验或计算成本高昂的流体动力学模拟。本研究的创新点在于利用卷积神经网络(CNN,Convolutional Neural Networks)预测变形和各向异性流动条件下的孔隙流体流动行为。具体而言,该方法以多孔微观结构的二值化CT图像为输入,预测对称二阶渗透率张量,这是连续介质多孔介质流动建模中的关键参数。\n该方法包含四个关键步骤:(1)构建不同体积应变水平下Bentheim砂岩的CT图像数据集;(2)采用格子玻尔兹曼方法(LBM,lattice Boltzmann method)开展单相流的孔隙尺度模拟,以生成渗透率数据;(3)以处理后的CT图像为输入、渗透率张量为输出,训练CNN模型;(4)探索提升模型泛化能力的技术,包括数据增强和替代CNN架构。研究通过实例验证了CNN准确预测渗透率张量的能力,该参数是岩土工程、水文学和材料科学等多个学科中的关键参数。研究还为感兴趣的读者提供了示例源代码。

英文摘要:

This work introduces a novel application for predicting the macroscopic intrinsic permeability tensor in deformable porous media, using a limited set of micro-CT images of real microgeometries. The primary goal is to develop an efficient, machine-learning (ML)-based method that overcomes the limitations of traditional permeability estimation techniques, which often rely on time-consuming experiments or computationally expensive fluid dynamics simulations. The novelty of this work lies in leveraging Convolutional Neural Networks (CNN) to predict pore-fluid flow behavior under deformation and anisotropic flow conditions. Particularly, the described approach employs binarized CT images of porous micro-structure as inputs to predict the symmetric second-order permeability tensor, a critical parameter in continuum porous media flow modeling. The methodology comprises four key steps: (1) constructing a dataset of CT images from Bentheim sandstone at different volumetric strain levels; (2) performing pore-scale simulations of single-phase flow using the lattice Boltzmann method (LBM) to generate permeability data; (3) training the CNN model with the processed CT images as inputs and permeability tensors as outputs; and (4) exploring techniques to improve model generalization, including data augmentation and alternative CNN architectures. Examples are provided to demonstrate the CNN's capability to accurately predict the permeability tensor, a crucial parameter in various disciplines such as geotechnical engineering, hydrology, and material science. An exemplary source code is made available for interested readers.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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