Mixing Layer Manipulation Experiment From Open-Loop Forcing to Closed-Loop Machine Learning Control

被引：29

作者：

Parezanovic, Vladimir ^{[1
]}

Laurentie, Jean-Charles ^{[1
]}

Fourment, Carine ^{[1
]}

Delville, Joel ^{[1
]}

Bonnet, Jean-Paul ^{[1
]}

Spohn, Andreas ^{[1
]}

Duriez, Thomas ^{[1
]}

Cordier, Laurent ^{[1
]}

Noack, Bernd R. ^{[1
]}

Abel, Markus ^{[2
,3
,4
]}

Segond, Marc ^{[2
]}

Shaqarin, Tamir ^{[5
]}

Brunton, Steven L. ^{[6
]}

机构：

[1] CNRS, Inst PPRIME, UPR 3346, F-86000 Poitiers, France

[2] Ambrosys GmbH, Potsdam, Germany

[3] LEMTA, Vandoeuvre Les Nancy, France

[4] Univ Potsdam, Potsdam, Germany

[5] Tafila Tech Univ, Tafila, Jordan

[6] Univ Washington, Seattle, WA 98195 USA

来源：

FLOW TURBULENCE AND COMBUSTION | 2015年 / 94卷 / 01期

关键词：

Shear flow; Turbulence; Active flow control; Extremum seeking; POD; Machine learning; Genetic programming; ACTUATORS;

D O I：

10.1007/s10494-014-9581-1

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

Open- and closed-loop control of a turbulent mixing layer is experimentally performed in a dedicated large scale, low speed wind-tunnel facility. The flow is manipulated by an array of fluidic micro-valve actuators integrated into the trailing edge of a splitter plate. Sensing is performed using a rake of hot-wire probes downstream of the splitter plate in the mixing layer. The control goal is the manipulation of the local fluctuating energy level. The mixing layer's response to the control is tested with open-loop forcing with a wide range of actuation frequencies. Results are discussed for different closed-loop control approaches, such as: adaptive extremum-seeking and in-time POD mode feedback control. In addition, we propose Machine Learning Control (MLC) as a model-free closed-loop control method. MLC arrives reproducibly at the near-optimal in-time control.

引用

页码：155 / 173

页数：19

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