Zobrazeno 1 - 10
of 2 343
pro vyhledávání: '"Vinuesa, R"'
Publikováno v:
J. Fluid Mech., 1001, A9 (2024)
A gas bubble sitting at a liquid-gas interface can burst following the rupture of the thin liquid film separating it from the ambient, owing to the large surface energy of the resultant cavity. This bursting bubble forms capillary waves, a Worthingto
Externí odkaz:
http://arxiv.org/abs/2409.14897
Designing active-flow-control (AFC) strategies for three-dimensional (3D) bluff bodies is a challenging task with critical industrial implications. In this study we explore the potential of discovering novel control strategies for drag reduction usin
Externí odkaz:
http://arxiv.org/abs/2405.17210
This study presents novel active-flow-control (AFC) strategies aimed at achieving drag reduction for a three-dimensional cylinder immersed in a flow at a Reynolds number based on freestream velocity and cylinder diameter of (Re_D=3900). The cylinder
Externí odkaz:
http://arxiv.org/abs/2405.17655
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:
Suárez, Pol, Alcántara-Ávila, Francisco, Miró, Arnau, Rabault, Jean, Font, Bernat, Lehmkuhl, Oriol, Vinuesa, R.
This paper presents for the first time successful results of active flow control with multiple independently controlled zero-net-mass-flux synthetic jets. The jets are placed on a three-dimensional cylinder along its span with the aim of reducing the
Externí odkaz:
http://arxiv.org/abs/2309.02462
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
Conditional averages are used to evaluate the effect of sweeps and ejections on amplitude modulation. This is done numerically with a direct numerical simulation (DNS) of a channel flow at friction Reynolds number $Re_{\tau} = 1000$ in a minimal stre
Externí odkaz:
http://arxiv.org/abs/2109.09486
Adaptive mesh refinement (AMR) in the high-order spectral-element method code Nek5000 is demonstrated and validated with well-resolved large-eddy simulations (LES) of the flow past a wing profile. In the present work, the flow around a NACA 4412 prof
Externí odkaz:
http://arxiv.org/abs/2108.12317