Aims and Scope

The International Journal for Numerical Methods in Fluids publishes refereed papers describing significant developments in computational methods that are applicable to scientific and engineering problems in fluid mechanics, fluid dynamics, micro and bio fluidics, and fluid-structure interaction. Numerical methods for solving ancillary equations, such as transport and advection and diffusion, are also relevant. The Editors encourage contributions in the areas of multi-physics, multi-disciplinary and multi-scale problems involving fluid subsystems, verification and validation, uncertainty quantification, and model reduction.

Numerical examples that illustrate the described methods or their accuracy are in general expected. However, papers dealing strictly with applications of existing methods or dealing with areas of research that are not deemed to be cutting edge by the Editors will not be considered for review. Two-part papers are discouraged.

Readership

Engineers in fluid dynamics; researchers in numerical and computational methods; applied mathematicians.

Keywords

CFD, computational fluid dynamics, navier-stokes, fluid mechanics, fluid dynamics, finite element method, finite difference method, spectral method, numerical methods, computational science, partial differential equations, computer simulation, journal, online journal, Wiley Online Library.

Special Issue on Machine Learning for Turbulence Modeling
Submission Date: 2026-05-01

Turbulent flows exist in a variety of applications, and many techniques have been developed to simulate such flows, ranging from completely ignoring viscous effects to resolving all turbulent scales.

In between lie statistically steady-state methods based on Reynolds averaging (RANS), and unsteady, partially scale-resolving methods such as large-eddy and detached-eddy simulations (LES, DES). Whereas inexpensive RANS methods generally adequate for vehicles at on-design conditions, high-fidelity simulations at off-design conditions, often characterized by regions of separated flow, generally require expensive scale-resolving simulations.

While the creation of a general turbulence model that is accurate across multiple flow regimes continues to be an elusive quest, recent research has sought to improve turbulence models, mainly RANS, through the use of additional data and machine learning. These techniques have been used to reduce uncertainties, to quantify errors, to extend the models' domain of applicability, to increase their accuracy, and to enable efficient sensitivity calculations. Challenges remain in generalizing these improvements across flow regimes and in interpreting the resulting machine-learning based models.

This Special Issue seeks contributions in the general area of machine learning for turbulence modeling. Specific topics of interest include:

    Field inversion and machine learning (FIML) in RANS
    Physics-informed machine learning for turbulence models
    Incorporating experimental and scale-resolved computational data into lower-fidelity models
    Data-driven subgrid models for large-eddy simulations
    Machine learning for modeling transition