Aims and scope

Engineering Applications of Computational Fluid Mechanics is a fully open access journal of numerical methods in fluid mechanics. The journal includes applications to aeronautic, civil, environmental, hydraulic and mechanical engineering.

Engineering Applications of Computational Fluid Mechanics is a publication of the Department of Civil & Environmental Engineering, The Hong Kong Polytechnic University.

Engineering Applications of Computational Fluid Mechanics provides an international, interdisciplinary forum for innovative, practical and industrial research in computational techniques to address a range of fluid mechanics problems. The journal publishes papers that address practical problem-solving by means of robust numerical techniques to generate precise flow prediction and optimum design, and those that further understanding of the physics of fluid motion.

Engineering Applications of Computational Fluid Mechanics covers:

    Innovative computational strategies, architectures, algorithms and techniques for engineering problems
    Analysis and simulation techniques and systems
    Quality and reliability, i.e. control of the accuracy, stability and efficiency of computational process
    New computing environments, such as distributed heterogeneous and collaborative computing
    Advanced visualization techniques, virtual environments and prototyping
    Automatic generation of model and mesh
    Application of object-oriented technology to engineering problems
    Applications of artificial intelligence, knowledge-based systems, decision-support systems, fuzzy logic, neural networks and evolutionary computations
    Computer-aided design and integrated systems
    Industrial experiences in the application of the above techniques, including case studies or bench-marking exercises

Engineering Applications of Computational Fluid Mechanics publishes research papers, review articles and discussions.

The journal operates a double-anonymized peer review policy.

Special Issue on Machine Learning for Fluid Dynamics
Submission Date: 2024-09-30

Article collection guest advisor(s)

Professor Weiwei Zhang, School of Aeronautics, Northwestern Polytechnical University, China
aeroelastic@nwpu.edu.cn

Assistant Professor Lu Lu, Yale University, USA
lu.lu@yale.edu

Professor Jiaqing Kou, Northwestern Polytechnical University, China
jqkou@nwpu.edu.cn

In recent decade, the transformative power of data science and machine learning, coupled with the burst of massive flow data, has shaped an artificial intelligence paradigm in fluid mechanics. This intersection of fluid mechanics and machine learning is spearheading a wave of groundbreaking advancements, especially in research areas such as aerospace, civil engineering, mechanical engineering, wind engineering, power energy, and naval engineering. Recent endeavors in this area have demonstrated the remarkable capabilities of these approaches.

So far, machine learning for fluid dynamics has unlocked unprecedented potential for understanding the mechanism of complex fluid behaviors, simulating complex flow physics with greater accuracy, as well as optimizing engineering designs more efficiently. This synergy is not merely an enhancement but a revolutionary step that redefines the boundaries and methodologies in fluid mechanics, opening a realm of possibilities for advanced research and application in the field. There is a pressing need for comprehensive research to refine and extend the applicability of these innovative methods, to improve the current research paradigms in fluid dynamics. However, significant efforts are yet to be made in order to fully embed this paradigm across a broader spectrum of the research community. Moreover, challenges persist, particularly regarding the generalizability and explainability of these methods.

This special collection communicates the recent advances of machine learning for fluid dynamics, with an emphasis on computational fluid dynamics. All topics that demonstrate the integration of machine learning in fluid mechanics, showcases novel methodologies, or applies these innovations to real-world engineering challenges, are welcomed. Potential topics include (but not limited to): mechanism analysis, numerical methods, data-driven flow modelling, turbulence modelling, multiphysics simulation, flow control, design optimization, etc.

Keywords:

    Machine Learning
    Computational Fluid Dynamics
    Data-Driven Methods
    Turbulence Modeling
    Numerical Methods

Wei-Wei Zhang is Chang Jiang Scholars and Excellent Young Scientists in Northwestern Polytechnical University. He received his PhD degree at NPU in 2006. He research on artificial intelligence applications in fluid mechanics including unsteady aerodynamics, aeroelastics, and flow control. He has published over 100 articles in premier international journals. He is now Director of the International Joint Institute of Intelligence in Fluid Mechanics, vice-chairman of CARS and deputy director of fluid-structure interaction mechanics committee of CSTAM. He is Associate Editors of 5 international Journals and several Chinese Journals.

Lu Lu is an Assistant Professor in the Department of Statistics and Data Science at Yale University. Prior to joining Yale, he was an Assistant Professor in the Department of Chemical and Biomolecular Engineering at University of Pennsylvania from 2021 to 2023, and an Applied Mathematics Instructor in the Department of Mathematics at Massachusetts Institute of Technology from 2020 to 2021. He obtained his Ph.D. degree in Applied Mathematics at Brown University in 2020. His current research interest lies in scientific machine learning, including theory, algorithms, software, and its applications to engineering, physical, and biological problems. His broad research interests focus on multiscale modeling and high performance computing for physical and biological systems.

Jiaqing Kou now is a full Professor of Northwestern Polytechnical University. He received his PhD in ETSIAE-School of Aeronautics, Universidad Polit´ecnica de Madrid, and got Alexander von Humboldt Postdoctoral Research Fellow in Aachen University. Now he is the Associate Editor of Aerospace Science and Technology, and Young Editorial Board of Applied Mathematics and Mechanics (Chinese).

All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisor for this Collection will not be handling the manuscripts (unless they are an Editorial Board member).

Please review the journal scope and author submission instructions prior to submitting a manuscript.

The deadline for submitting manuscripts is [30/09/2024].

Please contact Agnes Zhou at Agnes.Zhou@taylorandfrancis.com with any queries and discount codes regarding this Article Collection.

Please be sure to select the appropriate Article Collection from the drop-down menu in the submission system.