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pro vyhledávání: '"Azizpour H"'
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
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., 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
Autor:
Sirmacek, B., Gupta, S., Mallor, F., Azizpour, H., Ban, Y., Eivazi, H., Fang, H., Golzar, F., Leite, I., Melsion, G. I., Smith, K., Nerini, F. Fuso, Vinuesa, R.
In this chapter we extend earlier work (Vinuesa et al., Nature Communications 11, 2020) on the potential of artificial intelligence (AI) to achieve the 17 Sustainable Development Goals (SDGs) proposed by the United Nations (UN) for the 2030 Agenda. T
Externí odkaz:
http://arxiv.org/abs/2202.07424
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
A fully-convolutional neural-network model is used to predict the streamwise velocity fields at several wall-normal locations by taking as input the streamwise and spanwise wall-shear-stress planes in a turbulent open channel flow. The training data
Externí odkaz:
http://arxiv.org/abs/1912.12969
Publikováno v:
In International Journal of Heat and Fluid Flow October 2023 103
In the present work we assess the capabilities of neural networks to predict temporally evolving turbulent flows. In particular, we use the nine-equation shear flow model by Moehlis et al. [New J. Phys. 6, 56 (2004)] to generate training data for two
Externí odkaz:
http://arxiv.org/abs/1905.03634
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