A machine learning-based interaction force model for non-spherical and irregular particles in low Reynolds number incompressible flows

Hwang, Soohwan; Pan, Jian; Fan, Liang‐Shih

doi:10.1016/j.powtec.2021.07.050

Cited by 19 publications

(6 citation statements)

References 25 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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“…Here, we suggest that a further increase of prediction accuracy by adding more data points is necessary despite their prediction improvement being evident. In fact, many studies similar to the data-driven drag closure above only consider a fluid that bypasses the fixed spherical or nonspherical particles, − while the particle velocity fluctuations are not considered. It is thereby suggested that the reliability of the constructed data-driven model using the data source from such a way still needs further verification and improvement.…”

Section: Current Status and Challengesmentioning

confidence: 99%

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

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Artificial intelligence (AI), machine learning (ML), and data science are leading to a promising transformative paradigm. ML, especially deep learning and physics-informed ML, is a valuable toolkit that complements incomplete domain-specific knowledge in conventional experimental and computational methods. ML can provide flexible techniques to facilitate the conceptual development of new robust predictive models for multiphase flows and reactors by finding hidden pattern/information/mechanism in a data set. Due to such emergence, we thereby comprehensively survey, explore, analyze, and discuss key advancements of recent ML applications to hydrodynamics, heat and mass transfer, and reactions in single-phase and multiphase flow systems from different aspects: (1) development of multiphase closure models of drag force, turbulence stresses and heat/mass transfer to improve the accuracy and efficiency of typical CFD simulations; (2) image reconstruction, regime identification, key parameter predictions, and optimization of multiphase flow and transport fields; (3) reaction kinetics modeling (e.g., predictions of reaction networks, kinetic parameters, and species production) and reaction condition optimization. These sections also discuss and analyze the key advantages and weakness of ML for solving the problems in the domain of multiphase flows and reactors. Finally, we summarize the under-solving challenges and opportunities in order to identify future directions that would be useful for the research community. Future development and study of multiphase flows and reactors are envisaged to be accelerated by ML and data science.

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“…CFD simulations). Some examples are fine modelling of turbulence [13], interaction forces [14], [15] and friction [16]. A second type of uncertainty that arises in mechatronic systems is where the exact physics are known, however, some physical quantities are not known accurately.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Based Dual Extended Kalman Filter with Application on a Cam Follower System

Heck¹,

Vandeputte²,

Coene³

et al. 2023

Preprint

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<p>This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</p> <p>Online monitoring of mechatronic systems and production environments helps operators to steer their processes into regions of optimal performance. Filtering methods, such as the Kalman filter, emerge in this setting thanks to their ability to process sensor data in real-time. Extensive engineering efforts are required to attain a behavioral model on the system dynamics for this filter. What is more, the model needs updating when facing varying conditions that are prevalent in mechatronic and production systems during operation. At the same time, datadriven methods excel in adapting models to match measurements without relying heavily on expert knowledge, but suffer from main drawbacks such as the lack of interpretability and the batchwise approach. This paper proposes a novel methodology relying on two key ideas. First a dual extended Kalman filter allows to jointly estimate and adapt model parameters in an online setting. Secondly, within this filter a hybrid model is embedded that depends on physics-based and data-driven parameters that are being updated, leading up to increased prediction capabilities and hence improved estimations on the state of the system. We evaluate the presented hybrid dual Extended Kalman filter on a cam follower system where it shows the ability to learn the cam shape during operation whilst tracking the state with an increased accuracy of more than 22% compared to other filtering techniques.</p>

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A machine learning-based interaction force model for non-spherical and irregular particles in low Reynolds number incompressible flows

Cited by 19 publications

References 25 publications

The role of particle shape in computational modelling of granular matter

The role of particle shape in computational modelling of granular matter

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

A Hybrid Model Based Dual Extended Kalman Filter with Application on a Cam Follower System

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