7 papers
Bayesian neural network correction of RANS turbulence models with uncertainty quantification in separated flows
Tyler Buchanan, Ali Eidi, Richard P. Dwight
Data-driven correction of turbulence models offers a promising route for improving Reynolds-averaged Navier-Stokes (RANS) predictions, but quantifying uncertainty in such correctio…
The Closure Challenge: a benchmark task for machine learning in turbulence modelling
Ryley McConkey, Tyler Buchanan, Tess Smidt +3
We introduce a field-wide benchmark challenge for machine learning in Reynolds-averaged Navier-Stokes (RANS) turbulence modelling. Though open-source datasets exist for training da…
Discovery of a Physically Interpretable Data-Driven Wind-Turbine Wake Model
Kherlen Jigjid, Ali Eidi, Nguyen Anh Khoa Doan +1
This study presents a compact data-driven Reynolds-averaged Navier-Stokes (RANS) model for wind turbine wake prediction, built as an enhancement of the standard \(k\)-\(\varepsilon…
Uncertainty analysis of URANS simulations coupled with an anisotropic pressure fluctuation model
Ali Eidi, Richard P. Dwight
Accurate prediction of pressure and velocity fluctuations in turbulent flows is essential for understanding flow-induced vibration and structural fatigue. This study investigates t…
Physics-guided Bayesian neural networks for zonal corrections and uncertainty quantification in separated flows
Ali Eidi, Tyler Buchanan, Letian Jiang +1
Data-driven techniques have improved the accuracy of Reynolds-averaged Navier-Stokes (RANS) models in fluid dynamics. However, modeling separated flows remains challenging due to t…
Data-Driven RANS Closures Using a Relative Importance Term Analysis Based Classifier for 2D and 3D Separated Flows
Tyler Buchanan, Monica LÄcÄtuÅ, Alastair West +1
This study presents a novel approach for enhancing Reynolds-averaged Navier-Stokes (RANS) turbulence modeling through the application of a Relative Importance Term Analysis (RITA)…