132 citations · 360 across the 36 of their papers we have counts for
6 papers · 1 filter
Fully convolutional networks for velocity-field predictions based on the wall heat flux in turbulent boundary layers
L. Guastoni, A. G. Balasubramanian, F. Foroozan +6
Fully-convolutional neural networks (FCN) were proven to be effective for predicting the instantaneous state of a fully-developed turbulent flow at different wall-normal locations…
An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification
Iacopo Tirelli, Andrea Ianiro, Stefano Discetti
We introduce a novel end-to-end approach to improving the resolution of PIV measurements. The method blends information from different snapshots without the need for time-resolved…
Heat transfer enhancement in turbulent boundary layers with a pulsed slot jet in crossflow
Rodrigo Castellanos, Gianfranco Salih, Marco Raiola +2
The convective heat transfer enhancement in a turbulent boundary layer (TBL) employing a pulsed, slot jet in crossflow is investigated experimentally. A parametric study on actuati…
From snapshots to manifolds - A tale of shear flows
Ehsan Farzamnik, Andrea Ianiro, Stefano Discetti +4
We propose a novel non-linear manifold learning from snapshot data and demonstrate its superiority over Proper Orthogonal Decomposition (POD) for shedding-dominated shear flows. Ke…
Machine learning flow control with few sensor feedback and measurement noise
R. Castellanos, G. Y. Cornejo Maceda, I. de la Fuente +3
A comparative assessment of machine learning (ML) methods for active flow control is performed. The chosen benchmark problem is the drag reduction of a two-dimensional Kármán vorte…
Super-resolution GANs of randomly-seeded fields
Alejandro Güemes, Carlos Sanmiguel Vila, Stefano Discetti
Reconstruction of field quantities from sparse measurements is a problem arising in a broad spectrum of applications. This task is particularly challenging when the mapping between…