Zobrazeno 1 - 10
of 341
pro vyhledávání: '"Discetti, A."'
This study explores the potential of neuromorphic EBV cameras for fast latent coordinate representation in turbulent flows. Unlike conventional imaging systems, EBV cameras asynchronously capture changes in temporal contrast at each pixel, delivering
Externí odkaz:
http://arxiv.org/abs/2410.18940
Autor:
Cuéllar, Antonio, Amico, Enrico, Serpieri, Jacopo, Cafiero, Gioacchino, Baars, Woutijn J, Discetti, Stefano, Ianiro, Andrea
We present an experimental setup to perform time-resolved convective heat transfer measurements in a turbulent channel flow with air as the working fluid. We employ a heated thin foil coupled with high-speed infrared thermography. The measurement tec
Externí odkaz:
http://arxiv.org/abs/2410.12778
Autor:
Hou, Chang, Marra, Luigi, Maceda, Guy Y. Cornejo, Jiang, Peng, Chen, Jingguo, Liu, Yutong, Hu, Gang, Chen, Jialong, Ianiro, Andrea, Discetti, Stefano, Meilán-Vila, Andrea, Noack, Bernd R.
We propose a physics-informed data-driven framework for urban wind estimation. This framework validates and incorporates the Reynolds number independence for flows under various working conditions, thus allowing the extrapolation for wind conditions
Externí odkaz:
http://arxiv.org/abs/2410.02427
In this work we assess the impact of the limited availability of wall-embedded sensors on the full 3D estimation of the flow field in a turbulent channel with Re{\tau} = 200. The estimation technique is based on a 3D generative adversarial network (3
Externí odkaz:
http://arxiv.org/abs/2409.07348
Autor:
Cuéllar, Antonio, Güemes, Alejandro, Ianiro, Andrea, Flores, Óscar, Vinuesa, Ricardo, Discetti, Stefano
Publikováno v:
Cu\'ellar, A., G\"uemes, A., Ianiro, A., Flores, \'O., Vinuesa, R., Discetti, S.: Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements. J. Fluid Mech. 991, A1 (2024)
Different types of neural networks have been used to solve the flow sensing problem in turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements. Generative adversarial networks (GANs) are among the most promising me
Externí odkaz:
http://arxiv.org/abs/2409.06548
Complex phenomena can be better understood when broken down into a limited number of simpler "components". Linear statistical methods such as the principal component analysis and its variants are widely used across various fields of applied science t
Externí odkaz:
http://arxiv.org/abs/2407.03173
Autor:
Marra, Luigi, Maceda, Guy Y. Cornejo, Meilán-Vila, Andrea, Guerrero, Vanesa, Rashwan, Salma, Noack, Bernd R., Discetti, Stefano, Ianiro, Andrea
Publikováno v:
J. Fluid Mech. 996 (2024) A26
We propose a data-driven methodology to learn a low-dimensional actuation manifold of controlled flows. The starting point is resolving snapshot flow data for a representative ensemble of actuations. Key enablers for the actuation manifold are isomet
Externí odkaz:
http://arxiv.org/abs/2403.03653
This study presents a noise-robust closed-loop control strategy for wake flows employing model predictive control. The proposed control framework involves the autonomous offline selection of hyperparameters, eliminating the need for user interaction.
Externí odkaz:
http://arxiv.org/abs/2401.10826
Publikováno v:
British Food Journal, 2024, Vol. 126, Issue 12, pp. 4396-4416.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/BFJ-02-2024-0192
The convective heat transfer in a turbulent boundary layer (TBL) on a flat plate is enhanced using an artificial intelligence approach based on linear genetic algorithms control (LGAC). The actuator is a set of six slot jets in crossflow aligned with
Externí odkaz:
http://arxiv.org/abs/2304.12618