期刊
APPLIED MECHANICS REVIEWS
卷 67, 期 5, 页码 -出版社
ASME
DOI: 10.1115/1.4031175
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资金
- Department of Mechanical Engineering
- Department of Energy
- French Agence Nationale de la Recherche (ANR) via the Senior Chair of Excellence TUCOROM
- French Agence Nationale de la Recherche (ANR) via the SepaCoDe project
- OpenLab PPRIME/PSA by Peugeot-Citroen
- region Poitou-Charentes
- German Science Foundation via the Collaborative Research Center [SFB 880]
- UNSW, Canberra, Australia
- US National Research Foundatiation via the PIRE from Ambrosys GmbH, Potsdam, Germany [OISE-0968313]
- Bernd Noack Cybernetics Foundation
- Boeing
Closed-loop turbulence control is a critical enabler of aerodynamic drag reduction, lift increase, mixing enhancement, and noise reduction. Current and future applications have epic proportion: cars, trucks, trains, airplanes, wind turbines, medical devices, combustion, chemical reactors, just to name a few. Methods to adaptively adjust open-loop parameters are continually improving toward shorter response times. However, control design for in-time response is challenged by strong nonlinearity, high-dimensionality, and time-delays. Recent advances in the field of model identification and system reduction, coupled with advances in control theory (robust, adaptive, and nonlinear) are driving significant progress in adaptive and in-time closed-loop control of fluid turbulence. In this review, we provide an overview of critical theoretical developments, highlighted by compelling experimental success stories. We also point to challenging open problems and propose potentially disruptive technologies of machine learning and compressive sensing.
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