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TECHNICAL PAPERS

Quantification of Structural Loading During Off-Road Cycling

[+] Author and Article Information
D. S. De Lorenzo, M. L. Hull

Department of Mechanical Engineering, University of California, Davis, Davis, CA 95616

J Biomech Eng 121(4), 399-405 (Aug 01, 1999) (7 pages) doi:10.1115/1.2798337 History: Received February 12, 1998; Revised February 22, 1999; Online October 30, 2007

Abstract

To provide data for fatigue life prediction and testing of structural components in off-road bicycles, the objective of the research described herein was to quantify the loads input to an off-road bicycle as a result of surface-induced loads. A fully instrumented test bicycle was equipped with dynamometers at the pedals, handlebars, and hubs to measure all in-plane structural loads acting through points of contact between the bicycle and both the rider and the ground. A portable data acquisition system carried by the standing rider allowed, for the first time, this loading information to be collected during extended off-road testing. In all, seven experienced riders rode a downhill trail test section with the test bicycle in both front-suspension and full-suspension configurations. The load histories were used quantitatively to describe the load components through the computation of means, standard deviations, amplitude probability density functions, and power spectral density functions. For the standing position, the coefficients of variation for the load components normal to the ground were greater than 1.2 for handlebar forces and 0.3 and 0.5–0.6 for the pedal and hub forces, respectively. Thus, the relative contribution of the dynamic loading was much greater than the static loading at the handlebars but less so at the pedals and hubs. As indicated by the rainflow count, high amplitude loading was developed approaching 3 and 5 times the weight of the test subjects at the front and rear wheels, respectively. The power spectral densities showed that energy was concentrated in the band 0–50 Hz. Through stress computations and knowledge of material properties, the data can be used analytically to predict the fatigue life of important structural components such as those for steering. The data can also be used to develop a fatigue testing protocol for verifying analytical predictions of fatigue life.

Copyright © 1999 by The American Society of Mechanical Engineers
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