Closed-loop separation control over a sharp edge ramp using genetic programming

Debien, Antoine; Krbek, Kai A. A. F. von; Mazellier, Nicolas; Duriez, Thomas; Cordier, Laurent; Noack, Bernd R.; Abel, Markus; Kourta, Azeddine

doi:10.1007/s00348-016-2126-8

Cited by 47 publications

(25 citation statements)

References 37 publications

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“…Sensor-based feedback has been shown to outperform periodic forcing for drag reduction of a D-shaped body (Pastoor et al 2008). A very general method for sensor-based feedback is provided by genetic programming control (GPC) (Gautier et al 2015;Debien et al 2016;Parezanović et al 2016). Yet, the advantages of filtering out noise has hardly been explored in GPC ).…”

Section: Discussionmentioning

confidence: 99%

“…The regression problem is solved with symbolic genetic programming using the plant (experiment) to evolve the control law. This model-free control strategy can detect and exploit nonlinear actuation mechanisms in an unsupervised manner as evidenced in several shear flow control experiments (Gautier et al 2015;Debien et al 2016;Parezanović et al 2016).…”

Section: Introductionmentioning

confidence: 98%

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Drag reduction of a car model by linear genetic programming control

Noack

Cordier

et al. 2017

Exp Fluids

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We investigate open-and closed-loop active control for aerodynamic drag reduction of a car model. Turbulent flow around a blunt-edged Ahmed body is examined at Re H ≈ 3 × 10 5 based on body height. The actuation is performed with pulsed jets at all trailing edges combined with a Coanda deflection surface. The flow is monitored with pressure sensors distributed at the rear side. We apply a model-free control strategy building on Dracopoulos & Kent (1997) and Gautier et al. (2015). The optimized control laws comprise periodic forcing, multi-frequency forcing and sensor-based feedback including also time-history information feedback and combination thereof. Key enabler is linear genetic programming as simple and efficient framework for multiple inputs (actuators) and multiple outputs (sensors). The proposed linear genetic programming control can select the best open-or closed-loop control in an unsupervised manner. Approximately 33% base pressure recovery associated with 22% drag reduction is achieved in all considered classes of control laws. Intriguingly, the feedback actuation emulates periodic high-frequency forcing by selecting one pressure sensor in the optimal control law. Our control strategy is, in principle, applicable to all multiple actuators and sensors experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Drag reduction of a car model by linear genetic programming control

Noack

Cordier

et al. 2017

Exp Fluids

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“…The two-dimensional instantaneous vector fields as measured by S-PIV can be used to get a better view of the flow topology through the detection of large coherent structures. The algorithm of Debien et al (2016) was employed to quantify both their size and location. Contrary to classical vortex extraction algorithms, this approach does not require any arbitrary threshold to define a vortex region and only makes use of the vector field topology.…”

Section: Vortex Detectionmentioning

confidence: 99%

Investigation of an impinging heated jet for a small nozzle-to-plate distance and high Reynolds number: An extensive experimental approach

Grenson¹,

Léon²,

Reulet³

et al. 2016

International Journal of Heat and Mass Transfer

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The present work aims at investigating a particular impinging jet configuration throughout a comprehensive experimental approach. A preheated air jet at 130 • C issues a fully developed circular pipe at Reynolds number 60,000 and discharges in the laboratory room to impinge a flat plate located 3 diameters downstream. The description of the velocity field and the complete Reynolds stress tensor is provided by stereoscopic particle image velocimetry (S-PIV) and laser Doppler velocimetry (LDV) measurements. For the first time, data are reported for the mean and fluctuating temperature of an impinging jet configuration with the help of cold-wire thermometry (CWT) measurements. The heat transfer distribution on the impinged plate is determined through an inverse method based on infrared thermography measurements on the rear face of the plate. The agreement between SPIV and LDV measurements is shown excellent over the whole flow field. The measured Nusselt number distribution exhibits a secondary maximum at r/D = 2, as observed in previous experiments for short impinging distances. Flow dynamics is characterized through a spectral analysis of time-resolved measurements while flow topology features are identified through coherent structure detection based on SPIV spatiallyresolved instantaneous velocity fields. This analysis shows that the jet column mode, associated with a Strouhal number of 0.4, plays a key role in the primary structure dynamics.

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“…Thus, even with moderate complexity problems, the computation cost can still be prohibitive. A number of model identification methods have been presented to improve the capabilities of the mathematical models such as state calibration with 4D Var, 1 neural networks, 2 dynamics with radial basis functions, 3 genetic programming, 2,4,5 and the traditional Galerkin method. 1,6 Reduced order modelling, one of model identification methods, has proven to be a powerful tool to reduce the complexity and large dimensional size of the full discretised dynamical system.…”

mentioning

confidence: 99%

Model identification of reduced order fluid dynamics systems using deep learning

Wang

Xiao

Fang

et al. 2017

Numerical Methods in Fluids

168

106

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SUMMARYThis paper presents a novel model reduction method: Deep Learning Reduced Order Model (DLROM), which is based on proper orthogonal decomposition (POD) and deep learning methods. The deep learning approach is a recent technological advancement in the field of artificial neural networks. It has the advantage of learning the non-linear system with multiple levels of representation and predicting data. In this work, the training data are obtained from high fidelity model solutions at selected time levels. The Long Short-Term Memory network (LSTM) is used to construct a set of hypersurfaces representing the reduced fluid dynamic system. The model reduction method developed here is independent of the source code of the full physical system. The reduced order model (ROM) based on deep learning has been implemented within an unstructured mesh finite element fluid model. The performance of the new ROM is evaluated using two numerical examples: an ocean gyre and flow past a cylinder. These results illustrate that the CPU cost is reduced by several orders of magnitude whilst providing reasonable accuracy in predictive numerical modelling.

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Closed-loop separation control over a sharp edge ramp using genetic programming

Cited by 47 publications

References 37 publications

Drag reduction of a car model by linear genetic programming control

Drag reduction of a car model by linear genetic programming control

Investigation of an impinging heated jet for a small nozzle-to-plate distance and high Reynolds number: An extensive experimental approach

Model identification of reduced order fluid dynamics systems using deep learning

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