New Simulation Method Using Experimental Modal Analysis for Prediction of Body Deformation during Operation

Isomura, Yoshitaka; Ogawa, Toshiya; Monna, Hiroshi

doi:10.4271/2001-01-0494

Cited by 5 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the model development process, the flexibility of the frame was considered. The mode shapes [11] and necessary mechanical data were extracted from a FE model. The model was used to simulate special maneuvers, allowing the observation of the effects of flexibility on dynamic performance.…”

Section: Methodological Approach and Workflowmentioning

confidence: 99%

“…Simulation models for predicting lateral forces of go-karts need to consider the specific parameters and settings of the go-kart, including the effects of frame stiffness [9][10][11][12][13][14][15][16]. Based on the literature, it can be said that the application of flexible MBD simulation is essential for complete vehicle dynamics analysis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of a multibody model for go-karts considering frame flexibility

Horváth,

Zelei

2024

Pollack

View full text Add to dashboard Cite

This study focuses on the optimization dynamics of racing go-karts, which is heavily influenced by the frame's stiffness. Lacking suspensions and differentials, go-karts rely on the frame stiffness for wheel balancing and skid prevention by lifting the inner rear wheel during turns. Utilizing a rigid-flexible model in MSC Software ADAMS View, validated by frame deformation measurements, this research integrates finite element analysis with multibody techniques. The model, leverages computer aided design files for frame geometry and employs finite element analysis for frame validation. It facilitates evaluating go-kart dynamics through simulations, aiding in maneuver testing and design optimization. This approach provides a comprehensive framework for advancing go-kart designs.

show abstract

Section: Methodological Approach and Workflowmentioning

confidence: 99%