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Bicycle Frame Load Estimation Using a Controlled Semi-Analytical Simulation of an Unconstrained Multi-Body System

EasyChair Preprint no. 13402

2 pagesDate: May 21, 2024


Semi-analytical simulation methods have the potential to significantly enhance the development of bicycles,as they enable the reproduction of operating loads within the system with high accuracy. To overcome theneed for a numerical model of the human, tires, and track, measured loads at the connection points betweenthe bicycle system and the environment are applied to the bicycle as a load excitation within a multi-bodysimulation. The replacement of constraints with excitation loads leads to the simulation of an unconstrainedsystem. To prevent the occurrence of unwanted, motion-dependent loads, that could potentially compromisethe accuracy of the load calculations, it is essential to prevent a model drift by stabilizing the simulation.Hence, this paper presents the stabilization of an unconstrained bicycle system with the use of differentcontrol concepts. Furthermore, a numerical method is presented to distribute the control loads over thebicycle system, to increase the accuracy of the calculated component loads and enhance the comparabilityfor elastic multi-body simulations. It is demonstrated that the stabilization of unconstrained multi-bodysystems with control loads leads to highly accurate system loads. Furthermore, it is found that for bicycles,a PI-control to minimize the angular velocity of the system is most suitable to calculate accurate results,without the need for data of the system trajectory.

Keyphrases: Bicycle, control loop, Multibody Simulation, semi-analytical approach, unconstrained system

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Johannes Bolk and Oliver Stockemer and Burkhard Corves},
  title = {Bicycle Frame Load Estimation Using a Controlled Semi-Analytical Simulation of an Unconstrained Multi-Body System},
  howpublished = {EasyChair Preprint no. 13402},

  year = {EasyChair, 2024}}
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