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Tire Experimental Characterization Using Contactless Measurement Methods
Technical Paper
2021-01-1114
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In the frame of automotive Noise Vibration and Harshness (NVH) evaluation, inner cabin noise is among the most important indicators. The main noise contributors can be identified in engine, suspensions, tires, powertrain, brake system, etc. With the advent of E-vehicles and the consequent absence of the Internal Combustion Engine (ICE), tire/road noise has gained more importance, particularly at mid-speed driving and in the spectrum up to 300 Hz. At the state of the art, the identification and characterization of Noise and Vibration sources rely on pointwise sensors (microphones, accelerometers, strain gauges). Optical methods such as Digital Image Correlation (DIC) and Laser Doppler Vibrometer (LDV) have recently received special attention in the NVH field because they can be used to obtain full-field measurements. Moreover, these same techniques could also allow to characterize the tire behavior in operating conditions, which would be practically impossible to derive with standard techniques. In this paper we will demonstrate how non-contact full-field measurement techniques can be used to reliably and robustly characterize the tire behavior up to 300 Hz, focusing on static conditions. Experimental modal analysis will extract the modal characteristic of the tire in both free-free and statically preloaded boundary conditions, using both DIC and LDV. The extracted natural frequencies, damping ratios and full-field mode shapes will be used on one side to improve the accuracy of tire models (either by deriving FRF based models or updating FE ones) but also as a reference for future investigation on the tire behavior characterization in rotating conditions.
Authors
- Davide Mastrodicasa - Siemens Digital Industries Software
- Domenico Minervini - Siemens Digital Industries Software
- Emilio Di Lorenzo - Siemens Digital Industries Software
- Simone Manzato - Siemens Digital Industries Software
- Bart Peeters - Siemens Digital Industries Software
- Theo Geluk - Siemens Digital Industries Software
- Miriana Totaro - Politecnico di Torino
Topic
Citation
Mastrodicasa, D., Minervini, D., Di Lorenzo, E., Manzato, S. et al., "Tire Experimental Characterization Using Contactless Measurement Methods," SAE Technical Paper 2021-01-1114, 2021, https://doi.org/10.4271/2021-01-1114.Also In
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