Machine-learning-based feedback control for drag reduction in a turbulent channel flow

Park, Jong‐Hwan; Choi, Haecheon

doi:10.1017/jfm.2020.690

Cited by 44 publications

(29 citation statements)

References 47 publications

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“…In fact, the seminal work by Lee et al (1997) used a shallow multi-layer perceptron to aid the opposition control (Choi et al, 1994) of the channel flow. In addition to this study, several reports have demonstrated the applicability of the aforementioned combination based on the concept that estimates a velocity field on the detection plane from the wall measurements using a machine-learning model (Han and Huang, 2020;Park and Choi, 2020;Li et al, 2021). However, it is also true that there are several remaining issues including the applicability of a model trained with uncontrolled cases to controlled flows in an online manner (Park and Choi, 2020) and the limitation of the sensor availability in terms of both the number and the quality.…”

Section: Discussionmentioning

confidence: 84%

“…In addition to this study, several reports have demonstrated the applicability of the aforementioned combination based on the concept that estimates a velocity field on the detection plane from the wall measurements using a machine-learning model (Han and Huang, 2020;Park and Choi, 2020;Li et al, 2021). However, it is also true that there are several remaining issues including the applicability of a model trained with uncontrolled cases to controlled flows in an online manner (Park and Choi, 2020) and the limitation of the sensor availability in terms of both the number and the quality. We believe that the present investigation can directly address these issues from the perspective on the robustness against noise and lack of the sensors.…”

Section: Discussionmentioning

confidence: 84%

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Robust training approach of neural networks for fluid flow state estimations

Nakamura,

Fukagata

2021

Preprint

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State estimation from limited sensor measurements is ubiquitously found as a common challenge in a broad range of fields including mechanics, astronomy, and geophysics. Fluid mechanics is no exception -state estimation of fluid flows is particularly important for flow control and processing of experimental data. However, strong nonlinearities and spatio-temporal high degrees of freedom of fluid flows cause difficulties in reasonable estimations. To handle these issues, neural networks (NNs) have recently been applied to the fluid flow estimation instead of conventional linear methods. The present study focuses on the capability of NNs to various fluid flow estimation problems from a practical viewpoint regarding robust training. Three types of unsteady laminar and turbulent flows are considered for the present demonstration: 1. square cylinder wake, 2. turbulent channel flow, and 3. laminar to turbulent transitional boundary layer. We utilize a convolutional neural network (CNN) to estimate velocity fields from sectional sensor measurements. To assess the practicability of the CNN models, physical quantities required for the input and robustness against lack of sensors are investigated. We also examine the effectiveness of several considerable approaches for model training to gain more robustness against the lack of sensors. The knowledge acquired through the present study in terms of effective training approaches can be transferred towards practical machine learning in fluid flow modeling.

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Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 84%

Robust training approach of neural networks for fluid flow state estimations

Nakamura,

Fukagata

2021

Preprint

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“…techniques such as Reinforcement Learning (RL) have been shown to outperform even optimal flow control strategies. Indeed, there are several classes of flow control and optimization problems where learning algorithms can be the methods to choose and to be applied [79] [80] [81].Unlike flow modeling, optimization and control learning algorithms interact with the data sampling process in several ways [82] [83] [84] [85].…”

Section: Related Workmentioning

confidence: 99%

Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model

Amroun¹,

Ammi

Hafid³

2021

IEEE Access

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In this paper, we present a new approach to use machine learning (ML) for the calibration of a physical model allowing the reproduction of the vibratory behavior of an overhead line conductor. This physical model known as Strip Theory (ST) has the advantage of being very precise but very complicated and cumbersome in its software operations and manipulations. A second model known as the Wake Oscillator (WO) has been implemented in order to meet the limitations of the ST model. In order to be able to use the WO model instead of the ST model, very heavy manual adjustments are required, which makes its use complicated.Precisely, the WO must be able to generate a time series similar to a time series generated by the ST model. In order to respond to this limitation, a machine learning model known as ENS has been proposed. The machine learning model will therefore take as input the data from the WO model and output the data from the ST model. A series of Machine learning models have been implemented and tested. The ENS algorithm was retained with a best Pearson's linear coefficient of determination (R2 Score) of almost 0.7 and a Root mean square deviation (RMSE) of 7.57e-09. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.

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“…With the recent developments in machine learning (ML) methods and their successful application to classical engineering problems, various advances have been made to accelerate numerical methods (Kachrimanis, Karamyan, & Malamataris 2003;Ariana, Vaferi, & Karimi 2015;Benvenuti, Kloss, & Pirker 2016;Chaurasia & Nikkam 2017;Liang et al 2018a,b;Figueiredo et al 2019;Brevis, Muga, & van der Zee 2020;Prieto 2020). This capacity has also been extended to problems related to fluid dynamics and granular flow (Radl & Sundaresan 2014;Kutz 2017;Wan & Sapsis 2018;Fukami, Fukagata & Taira 2019;Li et al 2020a;Park & Choi 2020;Aghaei Jouybari et al 2021) where its applications towards the former has been extensively reviewed (Brenner, Eldredge, & Freund 2019;Brunton, Noack, & Koumoutsakos 2020;Fukami, Fukagata, & Taira 2020a). For example, a ML approach was used for the estimation of gravitational solid flows (Garbaa et al 2014).…”

Section: Introductionmentioning

confidence: 99%

High-resolution fluid–particle interactions: a machine learning approach

Davydzenka

Tahmasebi

2022

J. Fluid Mech.

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Modelling of fluid–particle interactions is a major area of research in many fields of science and engineering. There are several techniques that allow modelling of such interactions, among which the coupling of computational fluid dynamics (CFD) and the discrete element method (DEM) is one of the most convenient solutions due to the balance between accuracy and computational costs. However, the accuracy of this method is largely dependent upon mesh size, where obtaining realistic results always comes with the necessity of using a small mesh and thereby increasing computational intensity. To compensate for the inaccuracies of using a large mesh in such modelling, and still take advantage of rapid computations, we extended the classical modelling by combining it with a machine learning model. We have conducted seven simulations where the first one is a numerical model with a fine mesh (i.e. ground truth) with a very high computational time and accuracy, the next three models are constructed on coarse meshes with considerably less accuracy and computational burden and the last three models are assisted by machine learning, where we can obtain large improvements in terms of observing fine-scale features yet based on a coarse mesh. The results of this study show that there is a great opportunity in machine learning towards improving classical fluid–particle modelling approaches by producing highly accurate models for large-scale systems in a reasonable time.

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Machine-learning-based feedback control for drag reduction in a turbulent channel flow

Abstract: Abstract

Cited by 44 publications

References 47 publications

Robust training approach of neural networks for fluid flow state estimations

Robust training approach of neural networks for fluid flow state estimations

Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model

High-resolution fluid–particle interactions: a machine learning approach

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