A machine learning-based particle-particle collision model for non-spherical particles with arbitrary shape

Hwang, Soohwan; Pan, Jian; Sunny, Ashin A.; Fan, Liang‐Shih

doi:10.1016/j.ces.2022.117439

Cited by 11 publications

(4 citation statements)

References 26 publications

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“…It is possible to leverage the advantage of ML in modelling granular matter. At the particulate scale, the direct use of ML techniques ranges from shape recognition, characterization 186,187 and contact resolution 188,189 , to multiphysics fields such as particle-fluid interaction 190,191 . Particle…”

Section: Machine Learningmentioning

confidence: 99%

The role of particle shape in computational modelling of granular matter

Zhao,

Luding

2023

Nat Rev Phys

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Granular matter is ubiquitous in nature and is present in diverse forms in important engineering, industrial and natural processes. Particle-based computational modelling has become indispensable to understand and predict the complex behaviour of granular matter in these processes. The success of modern computational models requires realistic and efficient consideration of particle shape. Realistic particle shapes in naturally occurring and engineered materials offer diverse challenges owing to their multiscale nature in both length and time. Furthermore, the complex interactions with other materials, such as interstitial fluids, are highly nonlinear and commonly involve multiphysics coupling. This Technical Review presents a comprehensive appraisal of state-of-the-art computational models for granular particles of either naturally occurring shapes or engineered geometries. It focuses on particle shape characterization, representation and implementation, as well as its important effects. In addition, the particles may be hard, highly deformable, crushable or phase transformable; they might change their behaviour in the presence of interstitial fluids and are sensitive to density, confining stress and flow state. We describe generic methodologies that capture the universal features of granular matter and some unique approaches developed for special but important applications. Sections• Consideration of shape effects in coupled simulations of granular particles and environmental fluids requires revamped theories and methods to faithfully reflect their underpinning multiphase, multiphysics nature.• Incorporating realistic particle shapes in granular matter modelling must harness the latest advances in parallel computing and machine learning for effective large-scale computations.Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Section: Machine Learningmentioning

confidence: 99%

The role of particle shape in computational modelling of granular matter

Zhao,

Luding

2023

Nat Rev Phys

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“…The recent application of machine learning in engineering science, motivated by the need for process modeling, prediction, design, optimization, and control, has led to significant advancement in this field. − In the evolving landscape of process engineering, concepts such as digital twins and surrogate models have become pivotal, offering dynamic virtual replicas of physical systems. These models, widely applied across various sectors, enable quick response and prediction, enhancing efficiency in pharmaceutical manufacturing and energy management. − This study’s exploration of surrogate models in granular flow modeling aligns with this trend, demonstrating the potential of digital models in addressing complex engineering challenges.…”

Section: Introductionmentioning

confidence: 99%

Integrating Graph Neural Network-Based Surrogate Modeling with Inverse Design for Granular Flows

Jiang,

Byrne,

Glassey

et al. 2024

Ind. Eng. Chem. Res.

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Granular flows are central to a wide range of natural phenomena and industrial processes such as landslides, industrial mixing, and material handling and present intricate particle dynamics challenges. This study introduces a novel approach utilizing a Graph Neural Network-based Simulator (GNS) integrated with an inverse design for optimizing Discrete Element Method (DEM) parameters in granular flow simulations. The GNS model, trained on data sets generated from high-fidelity DEM simulations, exhibits enhanced predictive accuracy and generalization capabilities across various materials and granular collapse scenarios. Methodologically, the study contrasts the GNS approach with conventional Design of Experiment (DoE) methods, highlighting its enhanced computational efficiency and dynamic optimization capacity for complex parameter interactions in granular flows. The results demonstrate the GNS method superiority over the DoE in terms of computational speed and handling intricate parameter relationships. This work offers an advancement in computational techniques for granular flow studies, showing the potential of using differential simulations for realistic problems.

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“…In recent years, machine-learning (ML) approaches have also gained extensive application in solving the classification and regression problems in particle systems. For particulate flow at normal temperatures, researchers used a ML-based model to identify the particle form in a circulating fluidized bed (CFB) or to predict the flow characteristics of CFB. − Some coupled artificial neural networks (ANNs) with energy minimization multi-scale drag for fluidized particle simulation. , Some applied convolutional neural networks (CNNs) in a filtered two-fluid model and increased the accuracy of a coarse mesh simulation. , At a more detailed scale, Hwang et al used ANN to model the particle–particle collision for particles with an arbitrary shape. Similarly, Lu et al used CNN to replace the direct calculation of particle–particle and particle–boundary collisions and accelerated the discrete element model (DEM) by orders of magnitude.…”

Section: Introductionmentioning

confidence: 99%

“…29,30 Some applied convolutional neural networks (CNNs) in a filtered twofluid model and increased the accuracy of a coarse mesh simulation. 31,32 At a more detailed scale, Hwang et al 33 used ANN to model the particle−particle collision for particles with an arbitrary shape. Similarly, Lu et al 34 used CNN to replace the direct calculation of particle−particle and particle−boundary collisions and accelerated the discrete element model (DEM) by orders of magnitude.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Combustion Characteristics of Particles in Transient Gas–Solid Reacting Flow via a Machine Learning Approach

Zhang

You

2022

Ind. Eng. Chem. Res.

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Particle group combustion presents a strong temporal and spatial inhomogeneity owing to the complicated interphase interactions. Based on the data set from the fictitious domain method, the recurrent fully connected and convolutional parallel neural network (R-FC&CNN) architecture and its two comparable simplified models, that is, the recurrent fully connected neural network (R-FCNN) and the recurrent convolutional neural network (R-CNN) architectures, were constructed for predicting the gas−solid momentum exchange coefficient, β (kg•s −1 • m −3 ), average combustion rate per unit surface area of particles, r A / c (kg• s −1 •m −2 ), and comprehensive NaCl release parameter, γ, selectively. A time sequence of average particle temperature, T̅ (K), and particle volume fraction, ε, which can be extended in the matrix form, were constructed as the features selectively according to their correlation with the target physical quantity. The average relative error, δ̅ , and coefficient of determination, R 2 , were used as the evaluators. Through final testing, in the mild combustion domain, the R-CNN and R-FCNN models with simple structures showed good performance for β(δ̅ = 0.13, R 2 = 0.8) and r A / c (δ̅ = 0.04, R 2 = 0.84), respectively, while in the severe combustion domain, the R-FC&CNN model, with more complete features and functional structure, performed the best (for β, δ̅ = 0.12, R 2 = 0.85; for r A / c , δ̅ = 0.05, R 2 = 0.92; and for γ, = = R 0.05, 0.96 2). A fine-tuning and interpolated prediction method was developed to investigate further the model's expansibility. Both ensured acceptable performance on a new similar problem. In summary, the feasibility of physical meaningoriented machine learning--based model(s) for predicting the combustion characteristics of nonuniformly distributed particles was confirmed.

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A machine learning-based particle-particle collision model for non-spherical particles with arbitrary shape

Cited by 11 publications

References 26 publications

The role of particle shape in computational modelling of granular matter

The role of particle shape in computational modelling of granular matter

Integrating Graph Neural Network-Based Surrogate Modeling with Inverse Design for Granular Flows

Modeling of Combustion Characteristics of Particles in Transient Gas–Solid Reacting Flow via a Machine Learning Approach

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