A combined data-driven and discrete modelling approach to predict particle flow in rotating drums

Li, Yaoyu; Bao, Jie; Yu, Aibing; Yang, Runyu

doi:10.1016/j.ces.2020.116251

Cited by 15 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The colour represents particle velocity. The flow pattern is a typical cascading regime as a clear 'S' shape flow [18]. The outermost layer of the particle flow has larger particle velocity than the inner part.…”

Section: Dem Model and Parametersmentioning

confidence: 99%

Acoustic signals of rotating drums generated based on DEM simulations

Bao

Yang

2021

EPJ Web Conf.

Self Cite

View full text Add to dashboard Cite

Acoustic emission (AE) or vibration signal has been applied in detecting operations of grinding mills in many industries. This paper proposes an approach to generate AE signals based on the particle-wall impacts. Through a combination of multi-mode vibrations and the calibration of the key parameters, the model was able to reproduce experimental data. The AE model was then implemented into a discrete element method (DEM) modelling of particle flow in a rotating mill. The AE signals of the mill under different filling levels and rotation speeds were generated and analysed, mainly focusing on the frequency and magnitude of each vibration mode. The link between the AE signals and the particle-wall impact energy was explored.

show abstract

Section: Dem Model and Parametersmentioning

confidence: 99%

Acoustic signals of rotating drums generated based on DEM simulations

Bao

Yang

2021

EPJ Web Conf.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Persistent lack of physical comprehension continuously stymies preferable prediction performance of the key parameters in multiphase flow and reactor systems, although scientists have made systematic contributions to experimentally formulated correlations throughout the past decades. − The correlations of the key parameters in multiphase units are commonly expressed by gas/liquid/solid phase properties, operating conditions (e.g., phase concentration, velocity, and temperature), devices configurations (e.g., height and diameter), or a combination of them in dimensionless forms such as Archimedes, Froude, Nusselt, Reynolds, Sherwood, and Weber numbers. However, the prediction discrepancies between the existing empirical correlations of key parameters such as the particle entrainment and minimum fluidization velocity in gas-particle riser flows can reach several orders of magnitude. , Fortunately, the advanced research and development of flexible ML tools have the potential to complement the incomplete knowledge to boost the prediction ability of key multiphase field parameters such as mass flow rate/flux, − minimum fluidization velocity, , mixing rate/index, , overall/local hold-up, − pressure/pressure drop, − velocity, ,− temperature, − and other parameters − in multiphase/particulate flows and reactors. Note that interested readers may be referred to a relatively comprehensive list of the existing literature summarized in Table S4.…”

Section: Current Status and Challengesmentioning

confidence: 99%

“…Further possible improvement may need to render the feature input variables in a dimensionless form to make their method have more universality and less complexity. Yang and co-workers , combined an SVM-based data-driven method and DEM modeling to predict key granular flow parameters such as the angle of repose and collision energy in a rotating drum. Zhong et al proposed an improved strategy beneficial for finding the optimal ANN to significantly enhance predictions of the particle phase fraction distributions in gas-particle CFB risers.…”

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.

View full text Add to dashboard Cite

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.

show abstract

“…Quantitative analysis of mixing process and particle motion law is the theoretical basis for structural design and parameter optimization of germinated brown rice tanks [14] . In using the discrete element method to study and analyze the mixing of particles in the mixer, many scholars study the hybrid conditions of rotating the particles in the rotating drum through the discrete element method [15,16] , divide the mixing area in the particles system in the drum, and explore the impact of speed and filling on hybrid performance [17,18] . For example, Li et al [19] utilized numerical simulation methods to change the rotating drum size, rotational speed, particle filling level and other conditions to predict the mixing flow behavior of particles in the drum.…”

Section: Introductionmentioning

confidence: 99%

Discrete element simulation of the grain mixing law in brown rice germination device

Yan,

Xie,

Bai

et al. 2024

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

The discrete element method (DEM) was used in this study to numerically simulate the mixing process and motion law of particles in brown rice germination device. And the reliability of simulation experiments was verified through physical experiments. In the discrete element simulation experiment, there were three mixing stages in the mixing process of the particles. The particle motion conditions at different rotational speeds were rolling, cascading, cataracting and centrifuging. The lower the filling degree, the higher the particle mixing efficiency. The radial trajectory of the particles was approximated as an elliptical helix that continuously shrank towards the axis. The research results indicated that under the same speed and filling conditions, the motion of brown rice particles in both the simulated and physical test environments is rolling and the drop height is the same.

show abstract

A combined data-driven and discrete modelling approach to predict particle flow in rotating drums

Cited by 15 publications

References 33 publications

Acoustic signals of rotating drums generated based on DEM simulations

Acoustic signals of rotating drums generated based on DEM simulations

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

Discrete element simulation of the grain mixing law in brown rice germination device

Contact Info

Product

Resources

About