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A curated dataset for data-driven turbulence modelling.

Ryley McConkey1, Eugene Yee2, Fue-Sang Lien2

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This study introduces the first open-source dataset for machine learning-enhanced turbulence modeling. It aids in developing better corrective Reynolds-averaged Navier-Stokes (RANS) models by providing extensive simulation data.

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Area of Science:

  • Computational Fluid Dynamics
  • Machine Learning
  • Turbulence Modeling

Background:

  • Reynolds-averaged Navier-Stokes (RANS) models have limitations in accurately predicting turbulent flows.
  • Machine learning (ML) offers a promising avenue to augment and improve RANS models.
  • Existing datasets are not optimally structured for ML-augmented corrective turbulence closure modeling.

Purpose of the Study:

  • To develop and release the first open-source dataset specifically for machine learning-augmented corrective turbulence closure modeling.
  • To provide a structured resource for training, testing, and benchmarking new RANS models.
  • To facilitate advancements in turbulence modeling through accessible data.

Main Methods:

  • Curated a dataset comprising RANS simulations alongside matching Direct Numerical Simulation (DNS) and Large-Eddy Simulation (LES) data.
  • Included data from four turbulence models: k-ε, k-ε-ϕt-f, k-ω, and k-ω SST.
  • Compiled data across 29 cases for various DNS/LES reference cases, including periodic hills, square duct, and more, with 895,640 data points.

Main Results:

  • The dataset contains extensive RANS features with DNS/LES labels at each data point.
  • Feature set includes standard quantities and additional fields for novel feature generation.
  • The dataset encompasses diverse flow scenarios to ensure broad applicability.

Conclusions:

  • The released dataset significantly reduces the effort required for developing and validating ML-augmented RANS models.
  • This resource is expected to accelerate innovation in turbulence modeling.
  • The open-source nature promotes collaboration and reproducibility in the field.