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<p>We present a curated dataset comprising three-dimensional direct numerical simulations of Kolmogorov flow across multiple distinct parameter regimes. These simulations cover a wide range of Reynolds numbers and excitation modes, capturing diverse flow behaviours. The dataset is designed to support research in turbulence modelling, particularly for machine learning-based model development. Each case includes raw velocity fields and a Python-based interpolation code is provided for the interpolation of the velocity fields onto a structured grid in a format compatible with ParaView, accompanied by comprehensive metadata for ease of use. This open-access dataset aims to facilitate advances in turbulence modelling, a continuing and vital challenge in fluid mechanics research.</p>

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Direct numerical simulation of three-dimensional Kolmogorov flow for turbulence model development

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月26日

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  • Kinga Andrea Kovács,
  • Miklós Balogh,
  • Gergely Kristóf

摘要

We present a curated dataset comprising three-dimensional direct numerical simulations of Kolmogorov flow across multiple distinct parameter regimes. These simulations cover a wide range of Reynolds numbers and excitation modes, capturing diverse flow behaviours. The dataset is designed to support research in turbulence modelling, particularly for machine learning-based model development. Each case includes raw velocity fields and a Python-based interpolation code is provided for the interpolation of the velocity fields onto a structured grid in a format compatible with ParaView, accompanied by comprehensive metadata for ease of use. This open-access dataset aims to facilitate advances in turbulence modelling, a continuing and vital challenge in fluid mechanics research.