Editorial Type:
Article Category: Research Article
 | 
Online Publication Date: 04 Jun 2019

3D Modal Analysis of a Loaded Tire with Binary Random Noise Excitation

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Page Range: 207 – 223
DOI: 10.2346/tire.19.170166
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ABSTRACT

Modal analysis of tires has been a fundamental part of tire research aimed at capturing the dynamic behavior of a tire. An accurate expression of tire dynamics leads to an improved tire model and a more accurate prediction of tire behavior in real-life operations. Therefore, the main goal of this work is to improve the tire-testing techniques and data range to obtain the best experimental data possible using the current technology. With this goal in mind, we propose novel testing techniques such as piezoelectric excitation, high-frequency bandwidth data, and noncontact vibration measurement. High-frequency data enable us to capture the coupling between the wheel and tire as well as the coupling between airborne and structure-borne noise. Piezoelectric excitation eliminates the dynamic coupling of shakers and the inconsistency of force magnitude and direction of impact hammers as well as added mass effect. Noncontact vibration measurements using three-dimensional (3D) scanning laser Doppler vibrometer (SLDV) are superior to accelerometers because of no mass loading, a high number of measurement points in three dimensions, and high sensitivity. In this work, a modal analysis is carried out for a loaded tire in a static condition. Because of the highly damped nature of tires, multiple input excitation with binary random noise signal is used to increase the signal strength. Mode shapes of the tire are obtained and compared using both accelerometers and SLDV measurements.

Keywords: tire modal analysis; multiple input excitation; piezoelectric excitation; tire vibration; tire testing; noncontact measurement; scanning laser Doppler vibrometer
FIG. 1 —
FIG. 1 —

Conceptual sketch of the rotating modal rig. The image is courtesy of Hankook Tire [14].

FIG. 2 —
FIG. 2 —

Testing tire with low profile and no tread pattern.

FIG. 3 —
FIG. 3 —

(Left) MFC orientation inside the tire. (Right) MFC locations inside the tire.

FIG. 4 —
FIG. 4 —

Uncorrelated noise signal generated by MATLAB. (Left) Pure white random noise with 0.009 cross-correlation. (Right) Binary white random noise with 0.01 cross-correlation.

FIG. 5 —
FIG. 5 —

Spectrum of the measured white random noise signals.

FIG. 6 —
FIG. 6 —

Measurement points of the loaded tire.

FIG. 7 —
FIG. 7 —

(a) Measurement points. (b) Test setup for sidewall measurement. An area of the rotating rig is blurred for proprietary reasons.

FIG. 8 —
FIG. 8 —

Auto-MAC values for accelerometer measurements.

FIG. 9 —
FIG. 9 —

Mode shapes at 106.37 Hz with accelerometer measurement.

FIG. 10 —
FIG. 10 —

Mode shapes at 171.92 Hz with accelerometer measurements.

FIG. 11 —
FIG. 11 —

Mode shapes at 290.72 Hz with accelerometer measurements.

FIG. 12 —
FIG. 12 —

Mode shapes at 440.80 Hz with accelerometer measurements.

FIG. 13 —
FIG. 13 —

Auto-MAC values for SLDV measurements.

FIG. 14 —
FIG. 14 —

Mode shapes at 194.01 Hz with SLDV measurements.

FIG. 15 —
FIG. 15 —

Mode shapes at 266.44 Hz with SLDV measurements.

FIG. 16 —
FIG. 16 —

Mode shapes at 296.30 Hz with SLDV measurements.

FIG. 17 —
FIG. 17 —

Mode shapes at 432.55 Hz with SLDV measurements.

Contributor Notes

1 Department of Mechanical Engineering, Virginia Polytechnic Institute and State University, 445 Goodwin Hall, 635 Price's Fork Road, Blacksburg, VA 24061, USA
2 Corresponding author. Email: ipar@vt.edu
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