Machine Learning in Fluid Flow and Heat Transfer
A special issue of Fluids (ISSN 2311-5521). This special issue belongs to the section "Mathematical and Computational Fluid Mechanics".
Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 3137
Special Issue Editors
Interests: convective flow and heat transfer; thermal management and protection
Interests: computational heat transfer; thermal control; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Flow and heat transfer phenomena widely exist in industry and nature. Recently, researchers have been able to obtain high-precision physical fields by computational and experimental techniques, which has helped further our understanding of the mechanisms and guide engineering design. Through decades of studies, although people have accumulated myriad computational and experimental data, when working on new problems, even similar problems with different conditions, it is usually still necessary to re-simulate or re-experiment. In recent years, deep learning has demonstrated great ability to extract features from, and thus accurately predicting, physical fields, and prediction speed by deep learning is usually several orders of magnitude higher. Furthermore, attributed to the strong nonlinear feature extraction capability of deep learning, deep reinforcement learning is shedding light on solving flow and heat transfer control problems under complex conditions.
This Special Issue aims to collect the latest advances in artificial intelligence-coupled reactive fluids, heat transfer, and flow control, including (but not limited to) deep learning-based dimensional reduction models, physics-informed neural networks, and deep reinforcement learning of heat transfer and flow control.
Prof. Dr. Hongbin Yan
Prof. Dr. Wei-Tao Wu
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fluids is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- deep learning
- reinforcement learning
- fluid flow
- heat transfer
- flow and heat transfer control
- optimization
- artificial intelligence
