3 citations · 5 across the 4 of their papers we have counts for
4 papers
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…
Recent developments and research needs in turbulence modeling of hypersonic flows
Pratikkumar Raje, Eric Parish, Jean-Pierre Hickey +2
Hypersonic flow conditions pose exceptional challenges for Reynolds-Averaged Navier-Stokes (RANS) turbulence modeling. Critical phenomena include compressibility effects, shock/tur…
Machine-learning-assisted Blending of Data-Driven Turbulence Models
Mourad Oulghelou, Soufiane Cherroud, Xavier Merle +1
We present a machine learning-based framework for blending data-driven turbulent closures in the Reynolds-Averaged Navier-Stokes (RANS) equations, aimed at improving their generali…
Data-driven turbulence modeling
Paola Cinnella
This chapter provides an introduction to data-driven techniques for the development and calibration of closure models for the Reynolds-Averaged Navier--Stokes (RANS) equations. RAN…