scholarly journals Drag reduction of a bluff body using adaptive control methods

2006 ◽  
Vol 18 (8) ◽  
pp. 085107 ◽  
Author(s):  
Jean-François Beaudoin ◽  
Olivier Cadot ◽  
Jean-Luc Aider ◽  
José-Eduardo Wesfreid
Keyword(s):  
Adaptive Control ◽  
Drag Reduction ◽  
Bluff Body ◽  
Control Methods

scholarly journals Lattice Boltzmann study on drag reduction of a bluff body by slip boundary

2019 ◽  
Vol 1300 ◽  
pp. 012036
Author(s):  
Liuming Yang ◽  
Yuan Gao ◽  
Shuai Zhao ◽  
Yang Yu ◽  
Guoxiang Hou
Keyword(s):  
Drag Reduction ◽  
Lattice Boltzmann ◽  
Bluff Body ◽  
Slip Boundary

scholarly journals Bionic shape design of electric locomotive and aerodynamic drag reduction

2018 ◽  
Vol 4 (48) ◽  
pp. 99-109
Author(s):  
Zhenfeng WU ◽  
Yanzhong HUO ◽  
Wangcai DING ◽  
Zihao XIE
Keyword(s):  
Flow Field ◽  
Drag Reduction ◽  
Bluff Body ◽  
Aerodynamic Drag ◽  
Geometric Characteristic ◽  
Shape Design ◽  
Shape Optimisation ◽  
Electric Locomotive ◽  
Geometric Models ◽  
Characteristic Lines

Bionics has been widely used in many fields. Previous studies on the application of bionics in locomotives and vehicles mainly focused on shape optimisation of high-speed trains, but the research on bionic shape design in the electric locomotive field is rare. This study investigated a design method for streamlined electric locomotives according to the principles of bionics. The crocodiles were chosen as the bionic object because of their powerful and streamlined head shape. Firstly, geometric characteristic lines were extracted from the head of a crocodile by analysing the head features. Secondly, according to the actual size requirements of the electric locomotive head, a free-hand sketch of the bionic electric locomotive head was completed by adjusting the position and scale of the geometric characteristic lines. Finally, the non-uniform rational B-splines method was used to establish a 3D digital model of the crocodile bionic electric locomotive, and the main and auxiliary control lines were created. To verify the drag reduction effect of the crocodile bionic electric locomotive, numerical simulations of aerodynamic drag were performed for the crocodile bionic and bluff body electric locomotives at different speeds in open air by using the CFD software, ANSYS FLUENT16.0. The geometric models of crocodile bionic and bluff body electric locomotives were both marshalled with three cars, namely, locomotive + middle car + locomotive, and the size of the two geometric models was uniform. Dimensions and grids of the flow field were defined. And then, according to the principle of motion relativity, boundary conditions of flow field were defined. The results indicated that the crocodile bionic electric locomotive demonstrated a good aerodynamic performance. At the six sampling speeds in the range of 40–240 km/h, the aerodynamic drag coefficient of the crocodile bionic electric locomotive decreased by 7.7% on the average compared with that of the bluff body electric locomotive.

Design Framework for Context-Adaptive Control Methods Applied to Vehicle Power Management

2021 ◽  
Author(s):  
Sudarsan Kumar Venkatesan ◽  
Rian Beck ◽  
Sorin Bengea ◽  
Joram Meskens ◽  
Bruno Depraetere
Keyword(s):  
Adaptive Control ◽  
Power Management ◽  
Design Framework ◽  
Control Methods

Experimental Study on Bluff Body Drag Reduction with Fluidic Oscillators

2014 ◽  
Author(s):  
Rene Woszidlo ◽  
Timo Stumper ◽  
C. Nayeri ◽  
Christian O. Paschereit
Keyword(s):  
Experimental Study ◽  
Drag Reduction ◽  
Bluff Body ◽  
Body Drag

Active Control Methods for Drag Reduction in Flow Over Bluff Bodies (Keynote)

2002 ◽  
Author(s):  
Haecheon Choi
Keyword(s):  
Circular Cylinder ◽  
Drag Reduction ◽  
Active Control ◽  
High Frequency ◽  
Free Stream ◽  
Free Stream Velocity ◽  
Stream Velocity ◽  
Control Methods ◽  
Periodic Forcing ◽  
Time Periodic

In this paper, we present two successful results from active controls of flows over a circular cylinder and a sphere for drag reduction. The Reynolds number range considered for the flow over a circular cylinder is 40∼3900 based on the free-stream velocity and cylinder diameter, whereas for the flow over a sphere it is 105 based on the free-stream velocity and sphere diameter. The successful active control methods are a distributed (spatially periodic) forcing and a high-frequency (time periodic) forcing. With these control methods, the mean drag and lift fluctuations decrease and vortical structures are significantly modified. For example, the time-periodic forcing with a high frequency (larger than 20 times the vortex shedding frequency) produces 50% drag reduction for the flow over a sphere at Re = 105. The distributed forcing applied to the flow over a circular cylinder results in a significant drag reduction at all the Reynolds numbers investigated.

Stability Behavior of Model Reference Adaptive Control Methods in Presence of Aircraft Structural Damage

2007 ◽  
Author(s):  
Pascal Nespeca ◽  
Nesrin Sarigul-Klijn
Keyword(s):  
Adaptive Control ◽  
Numerical Study ◽  
Function Analysis ◽  
Model Reference Adaptive Control ◽  
Approximation Technique ◽  
Control Methods ◽  
Model Reference ◽  
Stability Behavior ◽  
The Stability ◽  
Model Reference Adaptive

Any classical control design starts by first satisfying stability and then looking towards satisfying transient requirements. Similarly, a Model Reference Adaptive Control (MRAC) Method should start with a stability analysis. Lyapunov function analysis is first used to justify the stability of the adaptive scheme. Next, a numerical study is conducted to predict the stability behavior of three different MRAC methods in the presence of large unanticipated changes in the dynamics of an aircraft. The Model reference adaptive control methods studied are: Method:1, an adaptive gain method; Method:2, a Neural Network (NN) approximation technique; and, Method:3, a linear approximation technique. For comparison purposes, the aircraft is assumed to have Linear Time Invariant, LTI dynamics. Each algorithm is given full state feedback, an inaccurate reference model and a poor Linear Quadratic Regulator, LQR design for the true plant. It is seen that when the LQR stabilizes the true plant, the three algorithms all achieve the same steady state error to a step command. Numerical results predict the different types of stability behavior that the algorithms provide. It is seen that the Methods: 2 and 3 can only provide a bounded stability, whereas Method: 1 can provide an asymptotic stability. A robust static controller can satisfy stability, but a robust static controller that accommodates variations in plant dynamics might not always be able to match transient requirements as expected. Although there may be no analytical guarantee from adaptive controllers of transient performance, one might look at anecdotal performances.

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