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Assessing Tire Performance from Vehicle Dynamic Transfer Functions
Technical Paper
2018-32-0048
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The aim of this study is to develop techniques which can be used to assess tire performance for a motorcycle in a race track and correlate them with subjective perceptions of race riders. This approach focusses on using vehicle level performance parameters and transfer functions to assess tire performance. A subjective assessment study is performed to understand rider’s perception. Tire behavior is then studied by assessing the dynamic performance of the motorcycle in a race track. Analysis techniques are then developed to interpret the data measured and understand tire performance. Based on these techniques, vehicle dynamic parameters and transfer functions that can be used to asses tire performance are developed. Correlations between objective findings and subjective perceptions are then identified. These studies show that the choice of a tire in a race track is not only decided by tire grip but also by factors like steering effort, roll rates and feedbacks perceived by the rider from tires. The influence of these parameters on tire performance is analyzed and presented. The feedback perceived by riders from tires during maneuvers is analyzed using transfer functions and it is found that the slope of the curve between roll factor and lateral acceleration can be used to understand this perception. Influence of tires on high speed stability is then studied by developing a novel test procedure to test weave stability at the maximum speed of the motorcycle. The impact of tires on maneuverability and stability is then quantified using the results of these analysis techniques. Though this study is used to understand tire performance, it is shown that it can be extended to analyze the overall dynamic behavior of the motorcycle in a race track.
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Authors
Citation
Mohan, B., Velagapudi, S., and Raju, K., "Assessing Tire Performance from Vehicle Dynamic Transfer Functions," SAE Technical Paper 2018-32-0048, 2018, https://doi.org/10.4271/2018-32-0048.Data Sets - Support Documents
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References
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