6 papers
Learning turbulent transport via Mori--Zwanzig graph neural networks
André Freitas, Xander M. de Wit, Alessandro Gabbana +4
We introduce a Mori--Zwanzig graph neural network (MZ--GNN) framework for learning reduced-order Lagrangian dynamics of tracer particles in homogeneous isotropic turbulence. The mo…
On the importance of stochasticity in closures of turbulence
André Freitas, Luca Biferale, Mathieu Desbrun +3
Deterministic closures for coarse-grained turbulence models help reproduce mean statistics, but often fail to capture the finite-time growth of uncertainty. Using the framework of…
A posteriori closure of turbulence models: are symmetries preserved?
André Freitas, Kiwon Um, Mathieu Desbrun +2
Turbulence modeling remains a longstanding challenge in fluid dynamics. Recent advances in data-driven methods have led to a surge of novel approaches aimed at addressing this prob…
Dynamics of small bubbles in turbulence in non-dilute conditions
Xander M. de Wit, Hessel J. Adelerhof, André Freitas +3
Turbulent flows laden with small bubbles are ubiquitous in many natural and industrial environments. From the point of view of numerical modeling, to be able to handle a very large…
Intermittency suppression in turbulence via forced light particles
André Freitas, Xander M. de Wit, Ziqi Wang +2
We investigate how turbulence is reshaped by the presence of externally forced light particles, using high-resolution direct numerical simulations with four-way coupling. The parti…
Solver-in-the-loop approach to closure of shell models of turbulence
André Freitas, Kiwon Um, Mathieu Desbrun +2
This work studies an a posteriori data-driven approach (known as solver-in-the-loop) for sub-grid modeling of a shell model for turbulence. This approach takes advantage of the dif…