Aims & Scope

Computers & Fluids is multidisciplinary. The term 'fluid' is interpreted in the broadest sense. Hydro- and aerodynamics, high-speed and physical gas dynamics, turbulence and flow stability, multiphase flow, rheology, tribology, aeroacoustics and fluid-structure interaction are all of interest, provided that computer technique plays a significant role in the associated studies or design methodology.

Applications will be found in most branches of engineering and science: mechanical, civil, chemical, aeronautical, medical, geophysical, nuclear and oceanographic. These will involve problems of air, sea and land vehicle motion and flow physics, energy conversion and power, chemical reactors and transport processes, ocean and atmospheric effects and pollution, biomedicine, noise and acoustics, and magnetohydrodynamics amongst others.

The development of numerical methods relevant to fluid flow computations, computational analysis of flow physics and fluid interactions and novel applications to flow systems and to design are pertinent to Computers & Fluids.

The journal also accepts papers dealing with uncertainty quantification in fluid flow simulations, reduced-order and surrogate models for fluid flows, optimization and control.

Papers dealing with machine learning approaches applied to fluid flow modeling are welcome, provided they show excellent scientific character. In particular, the authors are encouraged to perform comparisons with traditional numerical reconstruction methods, to provide a clear presentation of training vs validation cases, together with sufficient diversity in these cases, to analyze the physical consistency/theoretical analysis of the ML model, and to discuss the limitations of the method as well as its merits.

Special Issue on The Proceedings of the 21st International Conference for Mesoscopic Methods in Engineering and Science
截稿日期: 2026-02-20

This special issue (SI) consists of contributions from the participants to the 21st International Conference for Mesoscopic methods in Engineering and Science held during July 21—25, 2025, in the city of Wuhan, China. The articles in this proceedings cover the subjects related to modeling and simulation with the lattice Boltzmann equaton (LBE) and the applications of the LBE for fluid flows and other physical systems.

Guest editors:

Professor Maoqiang Jiang
Wuhun University of Technology, Wuhan, China
jiangmq@whut.edu.cn 

Professor Zunhua Zhang
Wuhun University of Technology, Wuhan, China
zunhuazhang@whut.edu.cn 

Professor Li-Shi Luo
Old Dominion University, Norfolk, USA
lluo@odu.edu 

Professor Manfred Krafczyk
TU Braunschweig University, Braunschweig, Germany
manfred.krafczyk@tu-braunschweig.de 

Special issue information:

The full scope the Speical issue is on mesoscopic/kinetic methods for computational mechanics and the computational mathematics and algorithms required for these new methods. Specific application areas of mesoscopic/kinetic methods include, but are not limited to:

Computational Fluid Dynamics (CFD), including Direct Numerical Simulations (DNS), Large-Eddy Simulations (LES), and turbulence based on kinetic approach;
Rheology for complex fluids, such as suspensions, multi-phase and multi-component fluids, non-Newtonian fluids, smart fluids such as electro-rheological (ER) and magneto-rheological (MR) fluids;
Biologic-fluids, Lab on a Chip systems, and microfluidics;
Micro/nano-scale phenomena involving non-continuum, surface-dominated, low-Reynolds-number, non-Newtonian, multi-scale and multi-physics effects;
Thermal-chemically nonequilibrium flows with Mach-number and/or Knudsen-number effects, and reactive flows;
Computational mechanics of solids and structures;
Computational multi-physics dynamics, such as flow-structure interactions;
Computational method for naval Architecture and ocean Engineering, such wave, hydrodynamic, bubble, free-surface;
Computational mathematics and algorithms for High Performance Computing (HPC) and tailored for challenging applications;
Novel computational hardware for large-scale scientific computing including GP-GPUs, FPGAs, etc.
Adapting Mesh Refinement technique(AMR).
Quantum computing and machine learning

Manuscript submission information:

Contributions to this Special Issue will be by invitation only.

The deadline for manuscript submission is Feb 20, 2026. 
Please ensure you read the Guide for authors - Computers & Fluids - ISSN 0045-7930 | ScienceDirect.com by Elsevier before writing your manuscript. Use this link to submit the manuscript: Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on Computers & Fluids | Journal | ScienceDirect.com by Elsevier . When submitting your manuscript please select the article type “VSI: The ICMMES-2025 Proceedings”. 
For any inquiries about the appropriateness of contribution topics, please contact Professor Maoqiang Jiang via jiangmq@whut.edu.cn.

Keywords:

Lattice Boltzmann Equation (LBE), Computational Fluid Dynamics (CFD), Gas-Kinetic Scheme, Multiphase Flow

Special Issue on Proceedings of the AI and Fluid Mechanics Symposium
截稿日期: 2026-03-30

Fluid mechanics has been explored by experimental, theoretical and traditional computational methods. Recently, there has been a resurgence of data-driven and machine learning methods to provide improved understanding and control of fluid flows. At the same time, novel approaches of solving the fluid flow conservation equations based on AI techniques are under development. As AI continues to reshape traditional modelling approaches, this special volume articulates ongoing cutting-edge works in the field covering both fundamentals and a wide range of industrial applications.

Guest editors:

Professor Emmanouil (Manolis) Gavaises
City St George University London, UK
m.gavaises@city.ac.uk 

Associate Prof Miguel Alfonso Mendez
von Karman Institute for Fluid Dynamics, Belgium
miguel.alfonso.mendez@vki.ac.be 

Professor George Karniadakis
Brown University, USA
george_karniadakis@brown.edu 

Special issue information:

Fluid mechanis and AI, machine learning, data-driven methods for analysis, modeling and control of fluid flows, including but not limited to:

- AI assisted turbulence modeling
- data assimilation and uncertainty quantification
- AI assisted surrogate models
- Generative AI in Fluid Mechanics
- Reinforcement Learning and its hybrid variants
- Physics Informed Machine Learning

Manuscript submission information:

Contributions to this Special Issue will be by invitation only.

The deadline for manuscript submission is 30/03/2026. 
Please ensure you read the Guide for authors - Computers & Fluids - ISSN 0045-7930 | ScienceDirect.com by Elsevier before writing your manuscript. Use this link to submit the manuscript: Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on Computers & Fluids | Journal | ScienceDirect.com by Elsevier . When submitting your manuscript please select the article type “VSI: AIFLUIDS”. 
For any inquiries about the appropriateness of contribution topics, please contact Professor Emmanouil (Manolis) Gavaises via m.gavaises@city.ac.uk.

Keywords:

AI assisted turbulence modeling
data assimilation and uncertainty quantification
AI assisted surrogate models
Generative AI in Fluid Mechanics
Reinforcement Learning and its hybrid variants
Physics Informed Machine Learning