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20132025
most citedPoisson CNN: Convolutional neural networks for the solution of the Poisson equation on a Cartesian mesh

61 citations · 302 across the 13 of their papers we have counts for

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physics.flu-dyn2025

Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control

Andrew Mole, Max Weissenbacher, Georgios Rigas +1

Traditional wind farm control operates each turbine independently to maximize individual power output. However, coordinated wake steering across the entire farm can substantially i…

physics.flu-dyn2023★ 2 cited

FR3D: Three-dimensional Flow Reconstruction and Force Estimation for Unsteady Flows Around Extruded Bluff Bodies via Conformal Mapping Aided Convolutional Autoencoders

Ali Girayhan Özbay, Sylvain Laizet

In many practical fluid dynamics experiments, measuring variables such as velocity and pressure is possible only at a limited number of sensor locations, \textcolor{black}{for a fe…

physics.flu-dyn2022

Direct numerical simulations of shocklet-containing turbulent channel counter-flows

Arash Hamzehloo, David J. Lusher, Sylvain Laizet +1

Counter-flow or counter-current configurations can maintain high turbulence intensities and exhibit a significant level of mixing. We have previously introduced a wall-bounded coun…

physics.flu-dyn2022★ 20 cited

Deep learning fluid flow reconstruction around arbitrary two-dimensional objects from sparse sensors using conformal mappings

Ali Girayhan Özbay, Sylvain Laizet

The usage of neural networks (NNs) for flow reconstruction (FR) tasks from a limited number of sensors is attracting strong research interest, owing to NNs' ability to replicate hi…

physics.flu-dyn2021★ 13 cited

Lagrangian Coherent Track Initialisation (LCTI)

Ali Rahimi Khojasteh, Yin Yang, Dominique Heitz +1

Advances in time-resolved Particle Tracking Velocimetry (4D-PTV) techniques have been consistently revealed more accurate Lagrangian particle motions. A novel track initialisation…

physics.flu-dyn2019★ 7 cited

Flows Over Periodic Hills of Parameterized Geometries: A Dataset for Data-Driven Turbulence Modeling From Direct Simulations

Heng Xiao, Jin-Long Wu, Sylvain Laizet +1

Computational fluid dynamics models based on Reynolds-averaged Navier--Stokes equations with turbulence closures still play important roles in engineering design and analysis. Howe…

physics.flu-dyn2018

A model under location uncertainty to predict the mean velocity in wall bounded flows

Benoit Pinier, Etienne Mémin, Sylvain Laizet +1

To date no satisfying model exists to explain the mean velocity profile within the whole turbulent layer of canonical wall bounded flows. We propose a modification of the velocity…

physics.flu-dyn2018★ 41 cited

Turbulence-resolving simulations of wind turbine wakes

Georgios Deskos, Sylvain Laizet, Matthew D. Piggott

Turbulence-resolving simulations of wind turbine wakes are presented using a high--order flow solver combined with both a standard and a novel dynamic implicit spectral vanishing v…

physics.flu-dyn2017★ 7 cited

Representation of Reynolds Stress Perturbations with Application in Machine-Learning-Assisted Turbulence Modeling

Jinlong Wu, Rui Sun, Sylvain Laizet +1

Numerical simulations based on Reynolds-Averaged Navier--Stokes (RANS) equations are widely used in engineering design and analysis involving turbulent flows. However, RANS simulat…

physics.flu-dyn2014★ 51 cited

Influence of the spatial resolution on fine-scale features in DNS of turbulence generated by a single square grid

S. Laizet, J. Nedić, J. C. Vassilicos

We focus in this paper on the effect of the resolution of Direct Numerical Simulations (DNS) on the spatio-temporal development of the turbulence downstream of a single square grid…

physics.flu-dyn2013★ 44 cited

Vortex line density in counterflowing He II with laminar and turbulent normal fluid velocity profiles

A. W. Baggaley, S. Laizet

Superfluid helium is an intimate mixture of a viscous normal fluid, with continuous vorticity, and an inviscid superfluid, where vorticity is constrained to thin, stable topologica…