MOVEFREE

MOVEFREE

Multiscale modeling

Lagrangian methods are based on the concept of describing fluid flows by following the motion of fluid particles. This appears to overcome numerical difficulties associated with large deformations, which are present and difficult to resolve in Eulerian approaches. In MOVEFREE,…

Machine/Deep Learning

The increased computational effort inherent in DNS and other numerical formulations, both in time and hardware needs, has opened the way to adopt novel computational techniques, stemming from Machine Learning (ML). Aerodynamic coefficient prediction, turbulence modelling, transitional flow modelling, and…

Physics-Informed Neural Networks (PINNs)

Nowadays, the domain of fluid mechanics has seen significant advancements. Alongside traditional experimental datasets, Direct Numerical Simulations (DNS) of the Navier-Stokes equations have generated vast amounts of high-fidelity data​. This “wealth” of information has offered crucial insights for developing turbulence…