Taichi-LBM3D: A Single-Phase and Multiphase Lattice Boltzmann Solver on Cross-Platform Multicore CPU/GPUs

Yang, Junling; Xu, Yi; Yang, Liang

doi:10.3390/fluids7080270

Cited by 8 publications

(5 citation statements)

References 43 publications

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“…By evolving these distribution functions in discrete time steps, LBM simulates the collective behavior of particles on a lattice grid, capturing macroscopic fluid flow dynamics. The 3D LBM solver used in this study (Yang et al, 2022) implements a common model of 19 velocities at each node, often called D3Q19 and multiple-relaxationtime (MRT) scheme to improve its numerical stability and shorten the computation time.…”

Section: Lattice-boltzmann Methodsmentioning

confidence: 99%

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Insights Into Estimation of Sand Permeability: From Empirical Relations to Microstructure-based Methods

Bodak,

Sobótka

2024

Studia Geotechnica Et Mechanica

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This study evaluates various methods for estimating soil permeability using microtomography-derived data and compares them to the conventional laboratory approaches. Different methods, including measurement in custom-designed permeameter at micro-CT-compatible scale, empirical equations, simulated sifting, semi-theoretical equations, pore-network modeling, and lattice-Boltzmann simulations, were applied to samples of sandy soils with distinct microstructural properties. The empirical equations showed varied results, highly dependent on the method chosen. The simulated sifting method was able to adequately estimate the granulometric properties of the soil, allowing the use of empirical permeability formulations for substantially small samples. Semi-theoretical equations based on the microstructural properties presented reasonable agreement for some samples. The pore-network modeling approach demonstrated computational efficiency but lacked accuracy. The lattice-Boltzmann method required significant computational resources but did not provide substantially closer alignment with the measured hydraulic properties of some samples. None of the simulations was able to properly determine the permeability of silty and organically contaminated sand. The study highlights the complexity of permeability estimation, emphasizing the need for choosing volumes of interest, resolution of micro-CT scans, and methods that match specific soil characteristics and available computational resources.

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Section: Lattice-boltzmann Methodsmentioning

confidence: 99%

“…In this study, lattice-Boltzmann simulations were conducted using Taichi-lang (Hu et al, 2019) based implementation (Yang et al, 2022). To facilitate high computational demands, a different machine had to be used as LBM is demanding in terms of both CPU/GPU and RAM.…”

Section: Lattice-boltzmann Methodsmentioning

confidence: 99%

Insights Into Estimation of Sand Permeability: From Empirical Relations to Microstructure-based Methods

Bodak,

Sobótka

2024

Studia Geotechnica Et Mechanica

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“…These permeability values serve the purpose of comparing and verifying the authenticity of the permeability model predictions subsequent to the application of DCT treatment and ANN modeling. This calculation method is implemented using the Taichi toolkit [ 41 ], which is a tool package available in the Python programming software, Version 3.7.…”

Section: Methodsmentioning

confidence: 99%

Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks

You

Liao

et al. 2023

Materials

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The permeability of porous materials determines the fluid flow rate and aids in the prediction of their mechanical properties. This study developed a novel approach that combines the discrete cosine transform (DCT) and artificial neural networks (ANN) for permeability analysis and prediction in digital rock images, focusing on nanoscale porous materials in shale formations. The DCT effectively captured the morphology and spatial distribution of material structure at the nanoscale and enhanced the computational efficiency, which was crucial for handling the complexity and high dimensionality of the digital rock images. The ANN model, trained using the Levenberg–Marquardt algorithm, preserved essential features and demonstrated exceptional accuracy for permeability prediction from the DCT-processed rock images. Our approach offers versatility and efficiency in handling diverse rock samples, from nanoscale shale to microscale sandstone. This work contributes to the comprehension and exploitation of unconventional resources, especially those preserved in nanoscale pore structures.

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“…Additionally, Taichi stands out as a user-friendly option for newcomers interested in numerical computing due to its relatively low learning curve, distinguishing it from other programming languages. From an academic perspective, Taichi has been used in a variety of research projects, including simulations of fluid dynamics, molecular dynamics, image processing, and machine learning [27][28][29]. Overall, Taichi's combination of high-performance, flexibility, ease of use, and active community makes it a promising language for high-performance numerical computing, particularly on modern hardware architectures.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Explicit Moving Particle Simulation Solver for Simulating Free Surface Flow on Multicore CPU/GPUs

Zhao,

Jiang,

Mochizuki

2024

Modelling

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The moving particle simulation (MPS) method is a simulation technique capable of calculating free surface and incompressible flows. As a particle-based method, MPS requires significant computational resources when simulating flow in a large-scale domain with a huge number of particles. Therefore, improving computational speed is a crucial aspect of current research in particle methods. In recent decades, many-core CPUs and GPUs have been widely utilized in scientific simulations to significantly enhance computational efficiency. However, the implementation of MPS on different types of hardware is not a trivial task. In this study, we present an implementation method for the explicit MPS that utilizes the Taichi parallel programming language. When it comes to CPU computing, Taichi’s computational efficiency is comparable to that of OpenMP. Nevertheless, when GPU computing is utilized, the acceleration of Taichi in parallel computing is not as fast as the CUDA implementation. Our developed explicit MPS solver demonstrates significant performance improvements in simulating dam-break flow dynamics.

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Taichi-LBM3D: A Single-Phase and Multiphase Lattice Boltzmann Solver on Cross-Platform Multicore CPU/GPUs

Cited by 8 publications

References 43 publications

Insights Into Estimation of Sand Permeability: From Empirical Relations to Microstructure-based Methods

Insights Into Estimation of Sand Permeability: From Empirical Relations to Microstructure-based Methods

Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks

An Efficient Explicit Moving Particle Simulation Solver for Simulating Free Surface Flow on Multicore CPU/GPUs

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