Zobrazeno 1 - 10
of 659
pro vyhledávání: '"Guastoni A."'
The wall cycle in wall-bounded turbulent flows is a complex turbulence regeneration mechanism that remains not fully understood. This study explores the potential of deep reinforcement learning (DRL) for managing the wall regeneration cycle to achiev
Externí odkaz:
http://arxiv.org/abs/2408.06783
In this work we compare different drag-reduction strategies that compute their actuation based on the fluctuations at a given wall-normal location in turbulent open channel flow. In order to perform this study, we implement and describe in detail the
Externí odkaz:
http://arxiv.org/abs/2309.02943
Autor:
Sanchis-Agudo, Marcial, Wang, Yuning, Arnau, Roger, Guastoni, Luca, Lim, Jasmin, Duraisamy, Karthik, Vinuesa, Ricardo
To improve the robustness of transformer neural networks used for temporal-dynamics prediction of chaotic systems, we propose a novel attention mechanism called easy attention which we demonstrate in time-series reconstruction and prediction. While t
Externí odkaz:
http://arxiv.org/abs/2308.12874
Autor:
Balasubramanian, Arivazhagan G., Guastoni, Luca, Schlatter, Philipp, Azizpour, Hossein, Vinuesa, Ricardo
The objective of this study is to assess the capability of convolution-based neural networks to predict wall quantities in a turbulent open channel flow. The first tests are performed by training a fully-convolutional network (FCN) to predict the 2D
Externí odkaz:
http://arxiv.org/abs/2303.00706
The objective of the present study is to provide a numerical database of thermal boundary layers and to contribute to the understanding of the dynamics of passive scalars at different Prandtl numbers. In this regard, a direct numerical simulation (DN
Externí odkaz:
http://arxiv.org/abs/2301.12915
We introduce a reinforcement learning (RL) environment to design and benchmark control strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The environment provides a framework for computationally-efficient, parallelized,
Externí odkaz:
http://arxiv.org/abs/2301.09889
Autor:
Guastoni, L., Balasubramanian, A. G., Foroozan, F., Güemes, A., Ianiro, A., Discetti, S., Schlatter, P., Azizpour, H., Vinuesa, R.
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 using quantities measured at the wall. In Guastoni et al. [J. Fluid Me
Externí odkaz:
http://arxiv.org/abs/2208.06024
The success of recurrent neural networks (RNNs) has been demonstrated in many applications related to turbulence, including flow control, optimization, turbulent features reproduction as well as turbulence prediction and modeling. With this study we
Externí odkaz:
http://arxiv.org/abs/2203.00974
Autor:
Balasubramanian, A. G., Guastoni, L., Güemes, A., Ianiro, A., Discetti, S., Schlatter, P., Azizpour, H., Vinuesa, R.
Modelling the near-wall region of wall-bounded turbulent flows is a widespread practice to reduce the computational cost of large-eddy simulations (LESs) at high Reynolds number. As a first step towards a data-driven wall-model, a neural-network-base
Externí odkaz:
http://arxiv.org/abs/2107.07340
Two models based on convolutional neural networks are trained to predict the two-dimensional velocity-fluctuation fields at different wall-normal locations in a turbulent open channel flow, using the wall-shear-stress components and the wall pressure
Externí odkaz:
http://arxiv.org/abs/2006.12483