Research Papers: Techniques and Procedures

Control of Blow-Down Wind Tunnel Using Combined Extended Kalman and Nonlinear Predictive Filters

[+] Author and Article Information
Ruixiang Zheng, Zhaoguang Wang

University of Michigan—Shanghai Jiao Tong
University Joint Institute,
Shanghai Jiao Tong University,
Shanghai 200240, China

Mian Li

University of Michigan—Shanghai Jiao Tong
University Joint Institute,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: mianli@sjtu.edu.cn

Qiang Zhang

University of Michigan—Shanghai Jiao Tong
University Joint Institute,
Shanghai Jiao Tong University,
Shanghai 200240, China;
Department of Mechanical Engineering,
Aeronautics School of Engineering and
Mathematical Sciences,
City University London,
Northampton Square EC1V 0HB, London

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received November 17, 2015; final manuscript received October 27, 2016; published online February 8, 2017. Assoc. Editor: Mark F. Tachie.

J. Fluids Eng 139(4), 041401 (Feb 08, 2017) (9 pages) Paper No: FE-15-1850; doi: 10.1115/1.4035243 History: Received November 17, 2015; Revised October 27, 2016

A blow-down wind tunnel is a typical nonlinear time-varying system facing the coupling effects between the pressure and temperature during the short-time test procedure. The control of blow-down wind tunnels has been discussed for a long time and a satisfactory general solution to this problem is still of interests. This paper aims to model the internal relationship of the state variables of the wind tunnel by using thermodynamic theories. With the developed model, a new control method combining extended Kalman filter (EKF) together with auxiliary nonlinear predictive filter (NPF) is proposed to improve the control performance of the blow-down wind tunnel controller, in terms of accuracy and robustness. The transient coupling effects between the pressure and temperature are fully considered in the proposed approach. The results from the simulation and experiments are consistent and demonstrate that the controller based on EKF combined together with NPF can work better than previously proposed EKF-based controller.

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Fig. 1

Transonic blow-down wind tunnel in this work

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Fig. 2

Structure of the control loop of the control system

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Fig. 3

Flow chart of NPF-EKF

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Fig. 4

Implementing NPF-EKF-based controller into general systems

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Fig. 5

Schematic of the blow-down wind tunnel in this work

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Fig. 6

Flow chart of NPF-EKF-based controller to control the valve

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Fig. 7

Results of Cv versus travel from different working conditions (Pset = 0.18 MPa and 0.22 MPa)

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Fig. 8

NPF-EKF initial parameters: WNPF = 1 × 10−7, PNC = 0.05, MNC = 10 × 105 (standard case)

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Fig. 9

NPF-EKF initial parameters: WNPF = 1 × 10−5, PNC = 0.05, MNC = 10 × 105

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Fig. 10

NPF-EKF initial parameters: WNPF = 1 × 10−7, PNC = 0.005, MNC = 10 × 105

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Fig. 11

NPF-EKF initial parameters: WNPF = 1 × 10−7, PNC = 0.5, MNC = 10e5

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Fig. 12

NPF-EKF initial parameters: WNPF = 1 × 10−7, PNC = 0.05, MNC = 10 × 106

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Fig. 13

Control performance of unheated condition when set points are: (a) 0.18 MPa (NPF-EKF), (b) 0.2 MPa (NPF-EKF), (c) 0.18 MPa (EKF), and (d) NPF-EKF versus EKF

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Fig. 14

Heated working condition when the set point is 0.18 MPa, where the initial upstream pressure is 2.0 MPa




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