scholarly journals Wing Tip Drag Reduction at Nominal Take-Off Mach Number: An Approach to Local Active Flow Control with a Highly Robust Actuator System

Aerospace ◽  
2016 ◽  
Vol 3 (4) ◽  
pp. 36 ◽  
Author(s):  
Matthias Bauer ◽  
Thomas Grund ◽  
Wolfgang Nitsche ◽  
Vlad Ciobaca
Keyword(s):  
Mach Number ◽  
Drag Reduction ◽  
Flow Control ◽  
Active Flow Control ◽  
Actuator System ◽  
Active Flow ◽  
Wing Tip

scholarly journals Wing Tip Drag Reduction at Nominal Take-Off Mach Number: An Approach to Local Active Flow Control with a Highly Robust Actuator System

2016 ◽  
Author(s):  
Matthias Bauer ◽  
Thomas Grund ◽  
Wolfgang Nitsche ◽  
Vlad Ciobaca
Keyword(s):  
Mach Number ◽  
Flow Control ◽  
Active Flow Control ◽  
Wind Tunnel Test ◽  
Complex Flow ◽  
Momentum Coefficient ◽  
Fluidic Actuators ◽  
Actuator System ◽  
Active Flow ◽  
Wing Tip

This paper discusses wind tunnel test results aimed at advancing active flow control technology to increase the aerodynamic efficiency of an aircraft during take-off. A model of the outer section of a representative civil airliner wing was equipped with two-stage fluidic actuators between the slat edge and wing tip, where mechanical high-lift devices fail to integrate. The experiments were conducted at a nominal take-off Mach number of M = 0.2. At this incidence velocity, separation on the wing section, accompanied by increased drag, is triggered by the strong slat edge vortex at high angles of attack. On the basis of global force measurements and local static pressure data, the effect of pulsed blowing on the complex flow is evaluated, considering various momentum coefficients and spanwise distributions of the actuation effort. It is shown that through local intensification of forcing, a momentum coefficient of less than cμ = 0.6% suffices to offset the stall by 2.4°, increase the maximum lift by more than 10%, and reduce the drag by 37% compared to the uncontrolled flow.

scholarly journals Reinforcement learning for bluff body active flow control in experiments and simulations

2020 ◽  
Vol 117 (42) ◽  
pp. 26091-26098
Author(s):  
Dixia Fan ◽  
Liu Yang ◽  
Zhicheng Wang ◽  
Michael S. Triantafyllou ◽  
George Em Karniadakis
Keyword(s):  
Reinforcement Learning ◽  
Fluid Mechanics ◽  
Drag Reduction ◽  
Flow Control ◽  
Bluff Body ◽  
Control Strategies ◽  
Active Flow Control ◽  
Reduction Process ◽  
Automatic Sequence ◽  
Active Flow

We have demonstrated the effectiveness of reinforcement learning (RL) in bluff body flow control problems both in experiments and simulations by automatically discovering active control strategies for drag reduction in turbulent flow. Specifically, we aimed to maximize the power gain efficiency by properly selecting the rotational speed of two small cylinders, located parallel to and downstream of the main cylinder. By properly defining rewards and designing noise reduction techniques, and after an automatic sequence of tens of towing experiments, the RL agent was shown to discover a control strategy that is comparable to the optimal strategy found through lengthy systematically planned control experiments. Subsequently, these results were verified by simulations that enabled us to gain insight into the physical mechanisms of the drag reduction process. While RL has been used effectively previously in idealized computer flow simulation studies, this study demonstrates its effectiveness in experimental fluid mechanics and verifies it by simulations, potentially paving the way for efficient exploration of additional active flow control strategies in other complex fluid mechanics applications.

Generic Transport Aft-body Drag Reduction using Active Flow Control

2007 ◽  
Vol 78 (3-4) ◽  
pp. 365-382 ◽  
Author(s):  
Eli Ben-Hamou ◽  
Eran Arad ◽  
Avi Seifert
Keyword(s):  
Drag Reduction ◽  
Flow Control ◽  
Active Flow Control ◽  
Body Drag ◽  
Active Flow
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