The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors
Abstract
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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Cites
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Metadata
- Authors
- Zhu, L.-T., Chen, X.-Z., Ouyang, B., Yan, W.-C., Lei, H., Chen, Z., & Luo, Z.-H.
- Venue
- Industrial & Engineering Chemistry Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-07-07
- Volume
- 61
- Issue
- 28
- Pages
- 9901-9949
- ISSN
- 0888-5885 (ISSN-L)
- 1520-5045
- OpenAlex
- W4284709982
- Cited by
- 294
- References
- 17
- FWCI
- 20.64
- Type
- journal article
- Language
- en
- Open access
- closed
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