Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

This study proposes a newly developed deep-learning-based method to generate turbulent inflow conditions for spatially developing turbulent boundary layer (TBL) simulations. A combination of a transformer and a multiscale-enhanced super-resolution generative adversarial network is utilised to predict velocity fields of a spatially developing TBL at various planes normal to the streamwise direction. Datasets of direct numerical simulation (DNS) of flat plate flow spanning a momentum thickness-based Reynolds number, $Re_\theta = 661.5\unicode{x2013}1502.0$ , are used to train and test the model. The model shows a remarkable ability to predict the instantaneous velocity fields with detailed fluctuations and reproduce the turbulence statistics as well as spatial and temporal spectra with commendable accuracy as compared with the DNS results. The proposed model also exhibits a reasonable accuracy for predicting velocity fields at Reynolds numbers that are not used in the training process. With the aid of transfer learning, the computational cost of the proposed model is considered to be effectively low. Furthermore, applying the generated turbulent inflow conditions to an inflow–outflow simulation reveals a negligible development distance for the TBL to reach the target statistics. The results demonstrate for the first time that transformer-based models can be efficient in predicting the dynamics of turbulent flows. They also show that combining these models with generative adversarial networks-based models can be useful in tackling various turbulence-related problems, including the development of efficient synthetic-turbulent inflow generators.

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“…2020; Park & Choi 2020; Vinuesa et al. 2022), non-intrusive sensing (Guastoni et al. 2021; Güemes et al.…”

Section: Introductionmentioning

confidence: 99%

A transformer-based synthetic-inflow generator for spatially developing turbulent boundary layers

Yousif

Zhang

et al. 2023

J. Fluid Mech.

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“…Recently, machine learning (ML) has offered a third option to enrich the knowledge we have about this subject, also thanks to the development of more powerful deep neural networks (DNNs) over the last years. Some examples include improved modelling results for Reynold-averaged Navier-Stokes (RANS) (Vinuesa et al, 2020) and large-eddy simulations (LESs), flow predictions (Kutz, 2017;Jiménez, 2018;Duraisamy et al, 2019;Brunton et al, 2020;Jiang et al, 2021;Guastoni et al, 2021), flow control and optimization strategies (Rabault et al, 2019;Raibaudo et al, 2020;Vinuesa et al, 2022), generation of inflow conditions (Fukami et al, 2019b), extraction of flow patterns (Raissi et al, 2020;Eivazi et al, 2021a,b), machine-learning-based reduced-order models (Nakamura et al, 2021;Vinuesa and Brunton, 2021) and prediction of the temporal dynamics (Srinivasan et al, 2019;Eivazi et al, 2020). The capability of a network to predict the temporal evolution of a turbulent flow is the focus of this study.…”

Section: Introductionmentioning

confidence: 99%

Predicting the temporal dynamics of turbulent channels through deep learning

Borrelli¹,

Guastoni²,

Eivazi³

et al. 2022

Preprint

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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 aim to assess the capability of these networks to reproduce the temporal evolution of a minimal turbulent channel flow. We first obtain a data-driven model based on a modal decomposition in the Fourier domain (which we denote as FFT-POD) of the time series sampled from the flow. This particular case of turbulent flow allows us to accurately simulate the most relevant coherent structures close to the wall. Long-short-term-memory (LSTM) networks and a Koopman-based framework (KNF) are trained to predict the temporal dynamics of the minimal-channel-flow modes. Tests with different configurations highlight the limits of the KNF method compared to the LSTM, given the complexity of the flow under study. Long-term prediction for LSTM show excellent agreement from the statistical point of view, with errors below 2% for the best models with respect to the reference. Furthermore, the analysis of the chaotic behaviour through the use of the Lyapunov exponents and of the dynamic behaviour through Poincaré maps emphasizes the ability of the LSTM to reproduce the temporal dynamics of turbulence. Alternative reduced-order

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Deep Reinforcement Learning for Flow Control Exploits Different Physics for Increasing Reynolds Number Regimes

et al. 2022

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The increase in emissions associated with aviation requires deeper research into novel sensing and flow-control strategies to obtain improved aerodynamic performances. In this context, data-driven methods are suitable for exploring new approaches to control the flow and develop more efficient strategies. Deep artificial neural networks (ANNs) used together with reinforcement learning, i.e., deep reinforcement learning (DRL), are receiving more attention due to their capabilities of controlling complex problems in multiple areas. In particular, these techniques have been recently used to solve problems related to flow control. In this work, an ANN trained through a DRL agent, coupled with the numerical solver Alya, is used to perform active flow control. The Tensorforce library was used to apply DRL to the simulated flow. Two-dimensional simulations of the flow around a cylinder were conducted and an active control based on two jets located on the walls of the cylinder was considered. By gathering information from the flow surrounding the cylinder, the ANN agent is able to learn through proximal policy optimization (PPO) effective control strategies for the jets, leading to a significant drag reduction. Furthermore, the agent needs to account for the coupled effects of the friction- and pressure-drag components, as well as the interaction between the two boundary layers on both sides of the cylinder and the wake. In the present work, a Reynolds number range beyond those previously considered was studied and compared with results obtained using classical flow-control methods. Significantly different forms of nature in the control strategies were identified by the DRL as the Reynolds number Re increased. On the one hand, for Re≤1000, the classical control strategy based on an opposition control relative to the wake oscillation was obtained. On the other hand, for Re=2000, the new strategy consisted of energization of the boundary layers and the separation area, which modulated the flow separation and reduced the drag in a fashion similar to that of the drag crisis, through a high-frequency actuation. A cross-application of agents was performed for a flow at Re=2000, obtaining similar results in terms of the drag reduction with the agents trained at Re=1000 and 2000. The fact that two different strategies yielded the same performance made us question whether this Reynolds number regime (Re=2000) belongs to a transition towards a nature-different flow, which would only admits a high-frequency actuation strategy to obtain the drag reduction. At the same time, this finding allows for the application of ANNs trained at lower Reynolds numbers, but are comparable in nature, saving computational resources.

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Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

Cited by 5 publications

References 99 publications

A transformer-based synthetic-inflow generator for spatially developing turbulent boundary layers

A transformer-based synthetic-inflow generator for spatially developing turbulent boundary layers

Predicting the temporal dynamics of turbulent channels through deep learning

Deep Reinforcement Learning for Flow Control Exploits Different Physics for Increasing Reynolds Number Regimes

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