Technology Category
Machine Learning: Matrix & Tensor MethodsComputer Vision: Diffusion Models for VisionReasoning under Uncertainty: Relational Probabilistic Models
CNN-powered micro- to macro-scale flow modeling in deformable porous media
🤖 AI Summary
Predicting the macroscopic permeability tensor of deforming porous media is computationally expensive due to reliance on high-fidelity pore-scale simulations. To address this, we propose an end-to-end convolutional neural network (CNN) framework that directly predicts the symmetric second-order anisotropic permeability tensor from binary micro-CT images—marking the first such approach. Trained on a limited dataset of micro-CT volumes paired with lattice Boltzmann method (LBM) simulation labels, the model incorporates data augmentation and architectural optimizations to achieve high prediction accuracy across varying volumetric strain conditions in Bentheim sandstone. This method substantially reduces dependence on costly pore-scale simulations and physical experiments, thereby enhancing efficiency in multiscale modeling. The implementation is publicly available, establishing a scalable, data-driven paradigm for flow modeling in deformable porous media.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Porous Deformable Materials
Rapid Accurate Prediction
Fluid Flow
Innovation
Methods, ideas, or system contributions that make the work stand out.
CNN
Fluid Dynamics
Deformable Materials
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