Guest Editors
Dr. Dang Khoi Le
Email: dkle91@seoultech.ac.kr
Affiliation: Research Center for Energy Convergence, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea
Homepage:
Research Interests: modeling and simulation of fluid dynamics, combustion, chemical reaction and multiphase systems
Summary
This Special Issue focuses on recent advances at the intersection of machine learning (ML), deep learning (DL), and Computational Fluid Dynamics (CFD) in the modeling and optimization of reacting systems and multiphase flows. We aim to showcase innovative research that blends data-driven techniques with physics-based simulations to tackle challenges in chemical reactors, combustion processes, and complex transport phenomena.
We invite contributions that develop or apply hybrid modeling strategies, surrogate models, and optimization frameworks—particularly those integrating CFD with ML/DL methods—to gain new insights into fluid dynamics, reaction kinetics, and materials processing. Emphasis will be placed on applications such as hydrogen and ammonia combustion, catalytic reaction engineering, and the design and optimization of separation devices.
Topics of interest include, but are not limited to:
1. Coupling ML/DL with CFD for modeling reacting flows and multiphase systems
2. Physics-informed neural networks (PINNs) for solving fluid dynamics equations in reacting systems
3. Multi-objective optimization of fluid and reacting systems using ML or evolutionary algorithms
4. CFD-based analysis of fluid flow and heat/mass transfer in reactive systems using alternative fuels (e.g., hydrogen, ammonia)
5. ML-assisted geometric optimization of equipment such as cyclone separators
6. Deep learning approaches to turbulent flow and transport phenomena analysis
We welcome original research articles, high-quality review papers, and technical notes that contribute to the advancement of this emerging interdisciplinary field.
Keywords
computational fluid dynamics; machine learning; deep learning; optimization; combustion; reacting; multiphase; data-driven; physics-informed
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