Research Papers: Flows in Complex Systems

Instantaneous Wind-Tunnel Model Vibration Measurements Using a Kalman Filtering Approach

[+] Author and Article Information
Robert R. Long

Department of Aerospace Engineering,
Texas A&M University,
College Station, TX 77840
e-mail: robertlong13@tamu.edu

Edward B. White

Department of Aerospace Engineering,
Texas A&M University,
College Station, TX 77840

Nathan R. Tichenor, Kevin Kremeyer

PM & AM Research, LLC,
Bryan, TX 77802

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received September 15, 2015; final manuscript received March 13, 2017; published online April 28, 2017. Assoc. Editor: Peter Vorobieff.

J. Fluids Eng 139(7), 071106 (Apr 28, 2017) (7 pages) Paper No: FE-15-1669; doi: 10.1115/1.4036270 History: Received September 15, 2015; Revised March 13, 2017

Experiments in ground-test facilities are required to develop and mature aero-optic platforms. However, structural vibrations of the facility can introduce significant error in optical experiments, making it difficult to separate wind-tunnel dynamics from the aero-optic measurements of interest. As a means of performing this separation, a Kalman filtering scheme, tuned using the autocovariance least-squares method, is implemented to measure the dynamic motion of a generalized aero-optic model geometry. An example is presented for the case of a laser diode mounted on a flexible post undergoing vibration. Using a single three-axis accelerometer, combined with a priori knowledge of the system's structural dynamics, pitch angle fluctuations on the order of 0.01 deg were successfully estimated within 0.002 deg uncertainty.

Copyright © 2017 by ASME
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Fig. 1

Experimental configuration

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Fig. 2

Test section arrangement

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Fig. 3

Wind-fixed coordinate system

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Fig. 4

Diagram of electronics setup

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Fig. 5

A three-axis accelerometer chip assembled on a breakout board

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Fig. 6

Calibration x-direction impulse response

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Fig. 7

Calibration 45 deg impulse response

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Fig. 8

Calibration y-direction impulse response

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Fig. 9

Kalman filter output for U = 27 m/s

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Fig. 10

Kalman filter output for U = 39 m/s

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Fig. 11

Kalman filter output for U = 55 m/s

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Fig. 12

Kalman filter error for U = 27 m/s. The dashed lines compare the measured error bounds to the error estimated by the Kalman filter.

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Fig. 13

Kalman filter error for U = 39 m/s. The dashed lines compare the measured error bounds to the error estimated by the Kalman filter.

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Fig. 14

Kalman filter error for U = 55 m/s. The dashed lines compare the measured error bounds to the error estimated by the Kalman filter.



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