Guest Editors
Prof. Dr Fei Dong
Email: jsdxdf@163.com
Affiliation: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, 212013, China
Homepage:
Research Interests: multiphase flow and heat transfer, research and development and application of new power systems
Dr. Zhihan Li
Email: lizhihan@nuist.edu.cn
Affiliation: School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Homepage:
Research Interests: multiphase fluid dynamics, computational fluid dynamics, biomimetic underwater propulsion mechanism
Dr. Tongwei Zhang
Email: zhtw@ujs.edu.cn
Affiliation: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, 212013, China
Homepage:
Research Interests: surface wettability and flow control, numerical simulation of multiphase flow, thermal management system for new energy vehicles
Summary
This special issue explores the forefront of research at the convergence of high-resolution computational methods for complex flows, multiphase interfacial physics, and emerging data-driven intelligence. We invite original contributions that advance the theory and practice of high-order numerical methods, focusing on efficient and accurate time integration, improved abilities to capture complex flow spatial features, stability on complex geometries and unstructured meshes, and scalable implementations on modern high-performance computing platforms. Submissions demonstrating these methods in challenging applications, including transition and turbulence prediction, aeroacoustics, and other multiscale phenomena, are particularly encouraged.
We also welcome studies addressing multiphase systems, including droplets, bubbles, and microfluidic flows, with emphasis on interfacial dynamics, breakup and coalescence, phase change, and applications such as fuel cells. Contributions that combine state-of-the-art experiments, direct numerical simulations, and theoretical modeling to elucidate multiphase behavior are strongly encouraged.
In addition, we seek innovative research leveraging machine learning and deep reinforcement learning for adaptive mesh and solver control, data-driven closure modeling, surrogate acceleration, inverse design, and active flow control in both single- and multiphase settings.
The goal of this special issue is to foster an integrated approach that unites numerical methods, physical modeling, and learning-based strategies, enabling predictive, robust, and reproducible simulations. By bridging theory, computation, and data-driven approaches, this collection aims to advance both engineering applications and our understanding of natural multiphase flow phenomena.
Keywords
high-order numerical methods, shock capturing, unstructured grids, bubble dynamics (breakup/coalescence), microfluidic multiphase flows, deep reinforcement learning, computational fluid dynamics
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