Zobrazeno 1 - 10
of 607
pro vyhledávání: '"Cinnella, P."'
Hypersonic flow conditions pose exceptional challenges for Reynolds-Averaged Navier-Stokes (RANS) turbulence modeling. Critical phenomena include compressibility effects, shock/turbulent boundary layer interactions, turbulence-chemistry interaction i
Externí odkaz:
http://arxiv.org/abs/2412.13985
A machine learning-based methodology for blending data-driven turbulent closures for the Reynolds-Averaged Navier-Stokes (RANS) equations is proposed to improve the generalizability across different flow scenarios. Data-driven models based on sparse
Externí odkaz:
http://arxiv.org/abs/2410.14431
Autor:
Yagoubi, Mouadh, Danan, David, Leyli-abadi, Milad, Brunet, Jean-Patrick, Mazari, Jocelyn Ahmed, Bonnet, Florent, gmati, maroua, Farjallah, Asma, Cinnella, Paola, Gallinari, Patrick, Schoenauer, Marc
The integration of machine learning (ML) techniques for addressing intricate physics problems is increasingly recognized as a promising avenue for expediting simulations. However, assessing ML-derived physical models poses a significant challenge for
Externí odkaz:
http://arxiv.org/abs/2407.01641
Autor:
Vinuesa, Ricardo, Rabault, Jean, Azizpour, Hossein, Bauer, Stefan, Brunton, Bingni W., Elofsson, Arne, Jarlebring, Elias, Kjellstrom, Hedvig, Markidis, Stefano, Marlevi, David, Cinnella, Paola, Brunton, Steven L.
Technological advancements have substantially increased computational power and data availability, enabling the application of powerful machine-learning (ML) techniques across various fields. However, our ability to leverage ML methods for scientific
Externí odkaz:
http://arxiv.org/abs/2405.04161
Autor:
Cinnella, Paola
This chapter provides an introduction to data-driven techniques for the development and calibration of closure models for the Reynolds-Averaged Navier--Stokes (RANS) equations. RANS models are the workhorse for engineering applications of computation
Externí odkaz:
http://arxiv.org/abs/2404.09074
In this manuscript, we combine non-intrusive reduced order models (ROMs) with space-dependent aggregation techniques to build a mixed-ROM. The prediction of the mixed formulation is given by a convex linear combination of the predictions of some prev
Externí odkaz:
http://arxiv.org/abs/2403.05710
A priori tests of turbulence models for the compressible Reynolds-Averaged Navier--Stokes (RANS) are performed by using Direct Numerical Simulations (DNS) data of zero-pressure-gradient flat-plate turbulent boundary layers. The DNS database covers a
Externí odkaz:
http://arxiv.org/abs/2310.09895
A stochastic Machine-Learning approach is developed for data-driven Reynolds-Averaged Navier-Stokes (RANS) predictions of turbulent flows, with quantified model uncertainty. This is done by combining a Bayesian symbolic identification methodology for
Externí odkaz:
http://arxiv.org/abs/2306.16996
Autor:
G. Spinazzola, S. Spadaro, G. Ferrone, S. Grasso, S. M. Maggiore, G. Cinnella, L. Cabrini, G. Cammarota, J. G. Maugeri, R. Simonte, N. Patroniti, L. Ball, G. Conti, D. De Luca, A. Cortegiani, A. Giarratano, C. Gregoretti
Publikováno v:
Journal of Anesthesia, Analgesia and Critical Care, Vol 4, Iss 1, Pp 1-15 (2024)
Abstract Background Discomfort can be the cause of noninvasive respiratory support (NRS) failure in up to 50% of treated patients. Several studies have shown how analgosedation during NRS can reduce the rate of delirium, endotracheal intubation, and
Externí odkaz:
https://doaj.org/article/5e41e7da4f75448eb902286ec63133e8
In this article, we propose a data-driven methodology for combining the solutions of a set of competing turbulence models. The individual model predictions are linearly combined for providing an ensemble solution accompanied by estimates of predictiv
Externí odkaz:
http://arxiv.org/abs/2301.09013