Feedback shear layer control for bluff body drag reduction
By M. Pastoor, Lars Henning, Bernd R. Noack, R. King, Gilead Tadmor. Journal of Fluid Mechanics, 2008
360 citations recorded by the source corpus.
Subfields: Computational Mechanics, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/ceadfa4574f9ea3e8fd40539c5d88c4fde318b51
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Steven L. Brunton
- Clarence W. Rowley
- Bernd R. Noack
- Eurika Kaiser
- Thomas Duriez
- Sunil Ahuja
- J. Nathan Kutz
- Jacques Bor'ee
In Environmental Engineering
- Steven L. Brunton
- Clarence W. Rowley
- Sunil Ahuja
- Ionel M. Navon
- Bernd R. Noack
- Jacques Bor'ee
- Bharathram Ganapathisubramani
- Christopher C. Pain
Citations in the corpus
- Bluff body drag manipulation using pulsed jets and Coanda effect (2015, background): “Moreover, active flow control by pulsating and zero-net-mass-flux jets reveals positive effects on the model’s drag (Pastoor et al. 2008; Chaligné et al. 2013).”
- Feedback control of unstable steady states of flow past a flat plate using reduced-order estimators (2009, methodology): “Reduced-order models were obtained by first decoupling the dynamics on the stable and unstable subspaces, and then truncating the relatively uncontrollable and unobservable modes on each of the two subspaces.”
- Machine Learning Control – Taming Nonlinear Dynamics and Turbulence (2016, methodology): “The model has been used to explain the stabilizing effect of low-frequency forcing of a wake [209] and a resulting control design [6].”
- A POD reduced order unstructured mesh ocean modelling method for moderate Reynolds number flows (2009, methodology): “, eddy-viscosity terms) to the POD reduced order equations to account for unresolved fine-scale fluctuations in POD for higher Reynolds numbers of the order of Re = 10,000 (Bottaro et al., 2007; Favier et al., 2006; Noack et al., 2005; Stankiewicz et al., 2008; Pastoor et al., 2008).”
- Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control (2018, background): “To the knowledge of the authors, only few published examples of successful complex active control strategies are available with respect to the importance and extent of the field (Pastoor et al., 2008; Gautier et al., 2015; Li et al., 2017; Erdmann et al., 2011; Guéniat et al., 2016).”
- Cluster-based reduced-order modelling of a mixing layer (2013, background): “The Ahmed body is a generic car model to study the relationship between characteristic flow structures typically arising at passenger cars and their impact on the drag and lift forces (Ahmed et al. 1984; Lienhart & Becker 2003; Pastoor et al. 2008).”
- Optimal mode decomposition for unsteady flows (2013, methodology): “These methods are successful when carefully applied (Pastoor et al. 2008), but they lack the general applicability offered by a datadriven approach.”
- On the need for a nonlinear subscale turbulence term in POD models as exemplified for a high-Reynolds-number flow over an Ahmed body (2013, methodology): “Recently, the model has been subjected to intensive research for the pursuit of flow control methods capable of reducing the aerodynamic drag on the model, both passive control (Beaudoin & Aider 2008; Krajnović 2013), and active control (Brunn et al. 2008; Pastoor et al. 2008; Krajnović & Fernandes ”