Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Di Leoni, P. Clark"'
We analyzed the performance of Convolutional Autoencoders in generating reduced-order representations the temperature field of 2D Rayleigh-B\'enard flows at $Pr=1$ and Rayleigh numbers extending from $10^6$ to $10^8$, capturing the range where the fl
Externí odkaz:
http://arxiv.org/abs/2410.01496
Autor:
Angriman, Sofía, Smith, Sarah E., di Leoni, Patricio Clark, Cobelli, Pablo J., Mininni, Pablo D., Obligado, Martín
Active grids operated with random protocols are a standard way to generate large Reynolds number turbulence in wind and water tunnels. But anomalies in the decay and third-order scaling of active-grid turbulence have been reported. We combine Laser D
Externí odkaz:
http://arxiv.org/abs/2409.03919
We investigate the capabilities of Physics-Informed Neural Networks (PINNs) to reconstruct turbulent Rayleigh-Benard flows using only temperature information. We perform a quantitative analysis of the quality of the reconstructions at various amounts
Externí odkaz:
http://arxiv.org/abs/2301.07769
Volume-resolving imaging techniques are rapidly advancing progress in experimental fluid mechanics. However, reconstructing the full and structured Eulerian velocity and pressure fields from sparse and noisy particle tracks obtained experimentally re
Externí odkaz:
http://arxiv.org/abs/2210.04849
When modelling turbulent flows, it is often the case that information on the forcing terms or the boundary conditions is either not available or overly complicated and expensive to implement. Instead, some flow features, such as the mean velocity pro
Externí odkaz:
http://arxiv.org/abs/2209.04285
Publikováno v:
Physics of Fluids 34, no. 1 (2022): 015128
Nudging is a data assimilation technique that has proved to be capable of reconstructing several highly turbulent flows from a set of partial spatiotemporal measurements. In this study we apply the nudging protocol on the temperature field in a Rayle
Externí odkaz:
http://arxiv.org/abs/2201.02306
Deep operator networks (DeepONets) are trained to predict the linear amplification of instability waves in high-speed boundary layers and to perform data assimilation. In contrast to traditional networks that approximate functions, DeepONets are desi
Externí odkaz:
http://arxiv.org/abs/2105.08697
Autor:
Buzzicotti, Michele, Biferale, Luca, Bonaccorso, Fabio, di Leoni, Patricio Clark, Gustavsson, Kristian
We present theoretical and numerical results concerning the problem to find the path that minimizes the time to navigate between two given points in a complex fluid under realistic navigation constraints. We contrast deterministic Optimal Navigation
Externí odkaz:
http://arxiv.org/abs/2103.00329
Autor:
Buzzicotti, M., Di Leoni, P. Clark
Large Eddy Simulations of turbulent flows are powerful tools used in many engineering and geophysical settings. Choosing the right value of the free parameters for their subgrid scale models is a crucial task for which the current methods present sev
Externí odkaz:
http://arxiv.org/abs/2012.00690
Publikováno v:
Phys. Rev. Fluids 6, 050503 (2021)
We study the applicability of tools developed by the computer vision community for features learning and semantic image inpainting to perform data reconstruction of fluid turbulence configurations. The aim is twofold. First, we explore on a quantitat
Externí odkaz:
http://arxiv.org/abs/2006.09179