activity
20102024
most citedPhysics-informed neural networks for solving Reynolds-averaged Navier$\unicode{x2013}$Stokes equations

481 citations · 1.2k across the 57 of their papers we have counts for

collaborators

58 papers

cs.PF2024★ 10 cited

Understanding the Impact of Synchronous, Asynchronous, and Hybrid In-Situ Techniques in Computational Fluid Dynamics Applications

Yi Ju, Adalberto Perez, Stefano Markidis +2

High-Performance Computing (HPC) systems provide input/output (IO) performance growing relatively slowly compared to peak computational performance and have limited storage capacit…

physics.flu-dyn2023

Widest scales in turbulent channels

Ramón Pozuelo, André V. G. Cavalieri, Philipp Schlatter +1

The widest spanwise scales in turbulent channel flows are studied through the use of three periodic channel-flow simulations at friction Reynolds number . The le…

physics.flu-dyn2023

Instability of the optimal edge trajectory in the Blasius boundary layer

Miguel Beneitez, Yohann Duguet, Philipp Schlatter +1

In the context of linear stability analysis, considering unsteady base flows is notoriously difficult. A generalisation of modal linear stability analysis, allowing for arbitrarily…

physics.flu-dyn2023★ 13 cited

Predicting the wall-shear stress and wall pressure through convolutional neural networks

Arivazhagan G. Balasubramanian, Luca Guastoni, Philipp Schlatter +2

The objective of this study is to assess the capability of convolution-based neural networks to predict wall quantities in a turbulent open channel flow. The first tests are perfor…

physics.flu-dyn2023★ 1 cited

Deep reinforcement learning for turbulent drag reduction in channel flows

L. Guastoni, J. Rabault, P. Schlatter +2

We introduce a reinforcement learning (RL) environment to design and benchmark control strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The environ…

physics.flu-dyn2023★ 12 cited

Direct numerical simulation of a zero-pressure-gradient thermal turbulent boundary layer up to

Arivazhagan G. Balasubramanian, Luca Guastoni, Philipp Schlatter +1

The objective of the present study is to provide a numerical database of thermal boundary layers and to contribute to the understanding of the dynamics of passive scalars at differ…