2 papers
physics.flu-dyn2024
Data-driven turbulent heat flux modeling with inputs of multiple fidelity
Matilde Fiore, Enrico Saccaggi, Lilla Koloszar +2
Data-driven RANS modeling is emerging as a promising methodology to exploit the information provided by high-fidelity data. However, its widespread application is limited by challe…
physics.flu-dyn2022
Physics-constrained machine learning for thermal turbulence modelling at low Prandtl numbers
Matilde Fiore, Lilla Koloszar, Miguel Alfonso Mendez +2
Liquid metals play a central role in new generation liquid metal cooled nuclear reactors, for which numerical investigations require the use of appropriate thermal turbulence model…