2 papers
physics.flu-dyn2026
Deep Learning-based Algebraic Reynolds Stress Closures for RANS Simulations of Turbulent Flows
Daniel Dehtyriov, Jonathan F. MacArt, Justin Sirignano
Turbulence is ubiquitous in engineering and science, yet direct simulation is prohibitively expensive. The Reynolds-averaged Navier-Stokes (RANS) equations provide savings exceedin…
physics.flu-dyn2025
oRANS: Online optimisation of RANS machine learning models with embedded DNS data generation
Daniel Dehtyriov, Jonathan F. MacArt, Justin Sirignano
Deep learning (DL) has demonstrated promise for accelerating and enhancing the accuracy of flow physics simulations, but progress is constrained by the scarcity of high-fidelity tr…