Lattice Boltzmann Model for Upscaling of Flow in Heterogeneous Porous Media Based on Darcy's Law

Liu, G. Z.; Liu, F. L.; Li, M.; Xu, Jinkai; Liu, Weifeng; Liu, Q.; Wang, Moran

doi:10.1615/jpormedia.2019023331

Cited by 4 publications

(2 citation statements)

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“…These networks make models more adaptable to various porous material topologies and heat transport scenarios. One of the most significant advancements in developing a more comprehensive and adaptable simulation tool is its capacity to perform many tasks without requiring expensive setup modifications [7].…”

Section: Introductionmentioning

confidence: 99%

Enhancing unsteady heat transfer simulation in porous media through the application of convolutional neural networks

Saraireh

2024

Eng. Res. Express

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This research describes a novel technique for anticipating unstable heat transfer in porous media. Convolutional neural networks (CNNs) are used with finite volume method (FVM) and long short-term memory (LSTM) networks to accomplish this. Heat transport networks are difficult to characterise using traditional numerical methodologies owing to their nonlinearity and complexity. The proposed solution combines FVM's precise physical modelling with CNN's and LSTM's superior pattern identification and temporal analysis. This collaboration supports the suggested strategy. Heat transport dynamics simulations in porous materials are more accurate, efficient, and adaptable when employing this hybrid framework. The experimental setup focused on porous material properties and gathered and processed a large amount of data. The building's three-dimensional shape, heat transfer, and time were investigated. Temporal fluctuations were also used. Multiple indicators are used to evaluate the overall performance of the model. These criteria include convergence speed, F1 score, accuracy, precision, recall, and computational cost. In the most notable numerical results, the proposed strategy surpasses both the Finite Element and the Lattice Boltzmann methods. The presented method enabled fast convergence and reduced processing costs. These results were: accuracy (0.92), precision (0.93), recall (0.91), and F1 score (0.92). The proposed method is generalizable and adaptable, and it can address a variety of heat transport simulation problems in porous media. Unlike CNNs, which can identify significant spatial patterns, LSTM cells can only see temporal dynamics. These two components are required to show heat transfer, which is a continually changing phenomenon. Modern technology enables more complex simulations. Processing expenses are lowered, and estimations are more accurate. These two discoveries were obtained through the inquiry and methodologies. Finally, the CNN-FVM-LSTM technique simulates heat transport using complicated computer models. Predicting unusually high temperatures in porous materials may improve the model's accuracy, computational efficiency, and flexibility.

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Section: Introductionmentioning

confidence: 99%

Enhancing unsteady heat transfer simulation in porous media through the application of convolutional neural networks

Saraireh

2024

Eng. Res. Express

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“…Therefore, it is suitable for simulating flow patterns in porous media with micro-nano pore throat structure (e.g., tight sandstone [2,3], shale [4,5], fibrous porous media [6], and porous electrode [7]). This simulation method mainly has lattice Boltzmann method (LBM) [8], direct simulation Monte-Carlo (DSMC) [9], dissipative particle dynamics (DPD) [10], etc. Whereas due to the simple and advantageous boundary conditions processing of the LBM method, it is more suitable to simulate flows in complex porous media.…”

Section: Introductionmentioning

confidence: 99%

The Application of REV-LBM Double Mesh Local Refinement Algorithm in Porous Media Flow Simulation

et al. 2022

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REV-scale LBM (representative elementary volume scale lattice Boltzmann method) provides a possible way for the unified microscopic simulation of transscale seepage combined with pore-scale LBM. However, shown by numerical results, when simulated fluid flows in tight porous media with discrete microfractures or dispersed dissolution pore, the nonphysical oscillation phenomenon will occur in the region of relatively greater velocity gradient, due to the insufficient computational node caused by computational capability constraints. And for that reason, the increase in simulation scale under certain computational capability will be restricted. In order to solve this problem, this paper extends the application of the original LBM local refinement algorithm into REV-LBM and corrects the original REV-LBM velocity processing method, such that REV-scale LBM has a more reasonable local refinement algorithm. Through conventional computation example, this paper proves that the local refinement algorithm can maintain well computational accuracy while at least double the time efficiency and could save more than 30% of CPU time in the practical application. This algorithm has a great potential of application in simulating flows in larger size porous media with complex micro-nano structures and can provide a new idea to transscale simulation in porous media.

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Compaction and sintering effects on scaling law of permeability-porosity relation of powder materials

Tian

Zhang²,

Zhang³

et al. 2023

International Journal of Mechanical Sciences

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Lattice Boltzmann Model for Upscaling of Flow in Heterogeneous Porous Media Based on Darcy's Law

Cited by 4 publications

References 0 publications

Enhancing unsteady heat transfer simulation in porous media through the application of convolutional neural networks

Enhancing unsteady heat transfer simulation in porous media through the application of convolutional neural networks

The Application of REV-LBM Double Mesh Local Refinement Algorithm in Porous Media Flow Simulation

Compaction and sintering effects on scaling law of permeability-porosity relation of powder materials

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