Optimization and sensitivity analysis of active drag reduction of a square-back Ahmed body using machine learning control

2020 ◽  
Vol 32 (12) ◽  
pp. 125117
Author(s):  
Dewei Fan ◽  
Bingfu Zhang ◽  
Yu Zhou ◽  
Bernd R. Noack
2021 ◽  
Author(s):  
Yu Zhou ◽  
Bingfu Zhang

Abstract This is a compendium of recent progresses in the development of wake dynamics and active drag reduction of three-dimensional simple automotive models, largely focused on the generic Ahmed body. It covers our new understanding of involved instabilities, predominant frequencies, pressure distribution and unsteady flow structures in the high- (12.5° < f < 30°) and low-drag (f > 30°) bodies and the square-back body (f = 0°), where f is the rear slant angle of the body. Various drag reduction methods and their performances are reviewed, including open- and closed-loop controls along with machine-learning control. The involving drag reduction mechanisms, net saving and efficiencies are discussed. Comments are made for the areas that deserve more attention and future investigation.


2018 ◽  
Vol 856 ◽  
pp. 351-396 ◽  
Author(s):  
B. F. Zhang ◽  
K. Liu ◽  
Y. Zhou ◽  
S. To ◽  
J. Y. Tu

Active drag reduction of an Ahmed body with a slant angle of $25^{\circ }$, corresponding to the high-drag regime, has been experimentally investigated at Reynolds number $Re=1.7\times 10^{5}$, based on the square root of the model cross-sectional area. Four individual actuations, produced by steady blowing, are applied separately around the edges of the rear window and vertical base, producing a drag reduction of up to 6–14 %. However, the combination of the individual actuations results in a drag reduction 29 %, higher than any previous drag reductions achieved experimentally and very close to the target (30 %) set by automotive industries. Extensive flow measurements are performed, with and without control, using force balance, pressure scanner, hot-wire, flow visualization and particle image velocimetry techniques. A marked change in the flow structure is captured in the wake of the body under control, including the flow separation bubbles, over the rear window or behind the vertical base, and the pair of C-pillar vortices at the two side edges of the rear window. The change is linked to the pressure rise on the slanted surface and the base. The mechanisms behind the effective control are proposed. The control efficiency is also estimated.


2021 ◽  
Vol 11 (12) ◽  
pp. 5468
Author(s):  
Elizaveta Shmalko ◽  
Askhat Diveev

The problem of control synthesis is considered as machine learning control. The paper proposes a mathematical formulation of machine learning control, discusses approaches of supervised and unsupervised learning by symbolic regression methods. The principle of small variation of the basic solution is presented to set up the neighbourhood of the search and to increase search efficiency of symbolic regression methods. Different symbolic regression methods such as genetic programming, network operator, Cartesian and binary genetic programming are presented in details. It is shown on the computational example the possibilities of symbolic regression methods as unsupervised machine learning control technique to the solution of MLC problem of control synthesis for obtaining the stabilization system for a mobile robot.


Author(s):  
Marek Sierotowicz ◽  
Nicola Lotti ◽  
Laura Nell ◽  
Francesco Missiroli ◽  
Ryan Alicea ◽  
...  

2000 ◽  
Author(s):  
Michael Kerho ◽  
Joseph Heid ◽  
Brian Kramer ◽  
Terry Ng
Keyword(s):  
Low Rate ◽  

2010 ◽  
Vol 213 (8) ◽  
pp. 1309-1319 ◽  
Author(s):  
M. J. McHenry ◽  
K. B. Michel ◽  
W. Stewart ◽  
U. K. Muller

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