Iterative learning control applied to pulsed blowing for lift enhancement on a trailing-edge flap
Iterative learning control applied to pulsed blowing for lift enhancement on a trailing-edge flap
A novel iterative learning control algorithm was developed and applied to an active flow control problem. The technique used pulsed air jets applied to a trailing-edge flap to enhance the lift. The iterative learning control algorithm used position-based pressure measurements to update the actuation. The method was experimentally tested on a two-element high-lift wing in a low-speed wind tunnel. Compressed air and fast switching solenoid valves were used as actuators to excite the flow, and the pressure distribution around the chord of the wing was measured as a feedback control signal for the iterative learning controller. Experimental results showed that the actuation was able to delay the separation and increase the overall lift by ΔCL =0.3 over the angle of attack range and increase CLmax
from 2.7 to 3.0 compared to the nonactuated case. By using the iterative learning control algorithms, the controller was able to track the target lift, and by using an optimum control algorithm with an extended reference, the controller was able to maximize the lift enhancement.
1969-1979
Cai, Zhonglun
dd8dd525-19a5-4792-a048-617340996afe
Angland, David
b86880c6-31fa-452b-ada8-4bbd83cda47f
Zhang, Xin
3056a795-80f7-4bbd-9c75-ecbc93085421
Chen, Peng
4683e371-2a83-41ca-b212-3f9585556905
2015
Cai, Zhonglun
dd8dd525-19a5-4792-a048-617340996afe
Angland, David
b86880c6-31fa-452b-ada8-4bbd83cda47f
Zhang, Xin
3056a795-80f7-4bbd-9c75-ecbc93085421
Chen, Peng
4683e371-2a83-41ca-b212-3f9585556905
Cai, Zhonglun, Angland, David, Zhang, Xin and Chen, Peng
(2015)
Iterative learning control applied to pulsed blowing for lift enhancement on a trailing-edge flap.
AIAA Journal, 53 (7), .
(doi:10.2514/1.J053556).
Abstract
A novel iterative learning control algorithm was developed and applied to an active flow control problem. The technique used pulsed air jets applied to a trailing-edge flap to enhance the lift. The iterative learning control algorithm used position-based pressure measurements to update the actuation. The method was experimentally tested on a two-element high-lift wing in a low-speed wind tunnel. Compressed air and fast switching solenoid valves were used as actuators to excite the flow, and the pressure distribution around the chord of the wing was measured as a feedback control signal for the iterative learning controller. Experimental results showed that the actuation was able to delay the separation and increase the overall lift by ΔCL =0.3 over the angle of attack range and increase CLmax
from 2.7 to 3.0 compared to the nonactuated case. By using the iterative learning control algorithms, the controller was able to track the target lift, and by using an optimum control algorithm with an extended reference, the controller was able to maximize the lift enhancement.
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Published date: 2015
Organisations:
Aerodynamics & Flight Mechanics Group
Identifiers
Local EPrints ID: 366731
URI: http://eprints.soton.ac.uk/id/eprint/366731
ISSN: 0001-1452
PURE UUID: b4d86752-0744-4c46-940f-589d9fd3229b
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Date deposited: 08 Jul 2014 15:40
Last modified: 14 Mar 2024 17:15
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Author:
Zhonglun Cai
Author:
Xin Zhang
Author:
Peng Chen
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