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Direct numerical simulation of three-dimensional Kolmogorov flow for turbulence model development
Kinga Andrea Kovács1, Miklós Balogh2, Gergely Kristóf2
1Department of Fluid Mechanics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111, Budapest, Hungary. kingaandrea.kovacs@edu.bme.hu.
Scientific Data
|February 26, 2026
Summary
A new dataset of 3D direct numerical simulations for Kolmogorov flow is released. This open-access data supports turbulence modelling research, especially for machine learning applications in fluid mechanics.
Area of Science:
- Fluid mechanics
- Computational physics
- Turbulence research
Background:
- Turbulence modelling is a significant challenge in fluid mechanics.
- Developing accurate turbulence models, especially for machine learning, requires high-quality data.
- Direct numerical simulations (DNS) provide detailed flow information but are computationally expensive.
Purpose of the Study:
- To present a curated dataset of 3D direct numerical simulations of Kolmogorov flow.
- To provide data across diverse parameter regimes (Reynolds numbers, excitation modes).
- To support the development of machine learning-based turbulence models.
Main Methods:
- Conducted three-dimensional direct numerical simulations of Kolmogorov flow.
- Covered a wide range of Reynolds numbers and excitation modes.
- Included raw velocity fields and Python-based interpolation code for grid compatibility.
Main Results:
- Generated a comprehensive dataset capturing diverse flow behaviours.
- Ensured data compatibility with analysis tools like ParaView.
- Provided metadata for enhanced usability.
Conclusions:
- The open-access dataset facilitates research in turbulence modelling.
- It is particularly valuable for advancing machine learning approaches in fluid dynamics.
- This resource aims to accelerate progress in understanding and predicting turbulent flows.
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