ANN prediction of particle flow characteristics in a drum based on synthetic acoustic signals from DEM simulations

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

doi:10.1016/j.ces.2021.117012

Cited by 5 publications

(1 citation statement)

References 38 publications

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“…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.

102

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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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Section: Current Status and Challengesmentioning

confidence: 99%