Experimental analysis of the stability of the jet wiping process, part II: Multiscale modal analysis of the gas jet-liquid film interaction

Mendez, Miguel Alfonso; Gosset, Anne; Buchlin, Jean‐Marie

doi:10.1016/j.expthermflusci.2019.03.004

Cited by 19 publications

(26 citation statements)

References 43 publications

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“…In the presence of shear stress, the power correlation changes slightly with the substrate speed, while this remains unaltered if the shear stress is removed. This result is in remarkable agreement with the experimental correlations presented in previous experimental works (Gosset et al 2019;Mendez et al 2019). It is interesting to observe that these experimental works were carried out on a much more viscous mineral oil, producing similar wiping numbers (in the range Π g = [0.1, 0.8]) but much lower shear stress numbers (in the range T g = [1,8]).…”

Section: The Relative Contribution Of Forcessupporting

confidence: 90%

“…In a one-way coupling formulation, it is assumed that the presence of the liquid film does not influence the gas jet. This assumption has been extensively validated for the prediction of the averaged final coating thickness (Lacanette et al 2006;Gosset & Buchlin 2007), but it is certainly not able to simulate the complex interaction between the two flows analysed by Gosset et al (2019) and Mendez et al (2019). In this work, this formulation is used to de-couple the dynamics of the liquid from the one of the gas jet and to analyse the liquid film frequency response and possible mechanisms of undulation formation.…”

Section: The Integral Formulation For the Jet Wipingmentioning

confidence: 99%

“…On the other hand, the experiment recently conducted in the VKI Ondule laboratory (Gosset et al 2019;Mendez et al 2019)…”

Section: Reference Quantity Definition Expressionmentioning

confidence: 99%

“…These time-dependent jet perturbations were designed to mimic different oscillatory modes in the impinging jet flow. Jet flow oscillations in the wiping process are experimentally investigated in Mendez et al (2019), while a similar oscillatory mechanism was analysed in Mendez, Scelzo & Buchlin (2018) on a deformable interface reproducing both fixed and moving surfaces. While it is now known that jet oscillations are coupled to the interface instability, this work aimed at analysing the response of the liquid film to possible jet disturbances, such as oscillations and pulsations, disregarding coupling effects.…”

Section: The Wiping Actuatorsmentioning

confidence: 99%

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Dynamics of the jet wiping process via integral models

et al. 2021

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Section: The Relative Contribution Of Forcessupporting

confidence: 90%

Section: The Integral Formulation For the Jet Wipingmentioning

confidence: 99%

“…On the other hand, the experiment recently conducted in the VKI Ondule laboratory (Gosset et al 2019;Mendez et al 2019)…”

Section: Reference Quantity Definition Expressionmentioning

confidence: 99%

Section: The Wiping Actuatorsmentioning

confidence: 99%

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Dynamics of the jet wiping process via integral models

et al. 2021

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“…29,30 The resulting deep reinforcement learning (DRL) paradigm has been successfully deployed to resolve several high-profile, complex problems, such as playing a wide range of Atari game without hardcoding strategies, 31 generating realistic dialogs, 32 or controlling the dynamics of complex robots. 33 Compared with data-driven and supervised learning approaches, which have also found some applications in fluid mechanics within particle image velocimetry (PIV) measurement, [34][35][36] reduced-order modeling, 37,38 or predictions of flow features, [39][40][41] DRL allows us to find a solution through trial-anderror, even when no solution is known a priori. One can observe that challenging systems successfully controlled by DRL have remarkably similar properties of nonlinearity and high-dimension, similar to the features of flow phenomena that make AFC challenging.…”

Section: Articlementioning

confidence: 99%

Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning

Tang

Rabault

Kuhnle³

et al. 2020

Physics of Fluids

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This paper focuses on the active flow control of a computational fluid dynamics simulation over a range of Reynolds numbers using deep reinforcement learning (DRL). More precisely, the proximal policy optimization (PPO) method is used to control the mass flow rate of four synthetic jets symmetrically located on the upper and lower sides of a cylinder immersed in a two-dimensional flow domain. The learning environment supports four flow configurations with Reynolds numbers 100, 200, 300, and 400, respectively. A new smoothing interpolation function is proposed to help the PPO algorithm learn to set continuous actions, which is of great importance to effectively suppress problematic jumps in lift and allow a better convergence for the training process. It is shown that the DRL controller is able to significantly reduce the lift and drag fluctuations and actively reduce the drag by ∼5.7%, 21.6%, 32.7%, and 38.7%, at Re = 100, 200, 300, and 400, respectively. More importantly, it can also effectively reduce drag for any previously unseen value of the Reynolds number between 60 and 400. This highlights the generalization ability of deep neural networks and is an important milestone toward the development of practical applications of DRL to active flow control.

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