# Computers & Fluids

- **Publisher**: Elsevier
- **ISSN**: 0045-7930
- **Impact factor**: 3.0
- **Trackers**: 1
- **Canonical page**: https://www.myhuiban.com/journal/365

## Special issues

| Title | Submission deadline |
|---|---|
| Proceedings of the AI and Fluid Mechanics Symposium | 2026-03-30 |
| The Proceedings of the 21st International Conference for Mesoscopic Methods in Engineering and Science | 2026-02-20 |

## Call for papers

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.

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Source: Conference Partner — https://www.myhuiban.com/journal/365 (rankings reproduced from CCF / ICORE / QUALIS; data cached up to 1 hour)