Efficient simulation strategy to design a safer motorcycle

Maier, Steffen; Fehr, Jörg

doi:10.1007/s11044-023-09879-8

Cited by 6 publications

(10 citation statements)

References 25 publications

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“…As omnidirectional tools, HBMs can be used in a wide range of applications. In the following, this is demonstrated with a comprehensive summary of the application of state-of-the-art FE HBMs as motorcyclists in crash impacts from [77].…”

Section: Case Study -Safe Motorcyclementioning

confidence: 99%

“…The imposed motion is a multi-axial linear and angular motion. It is derived from a multibody simulation of the vehicle interaction of a motorcycle, a rider, and a passenger car as the accident opponent, explained in [74], as part of a simulation strategy that involves multiple models [73]. The prescribed model is a motorcycle with additional passive safety equipment, airbags, thigh belts, and leg impact protection, as explained in [76,75].…”

Section: Positioning Hbms For Motorcycle Rider Posturesmentioning

confidence: 99%

“…FIGURE13 Rider trajectories of Hybrid III 50 th percentile ATD (top) and VIVA+ 50M HBM (bottom) for lateral car impact ([77]). …”

mentioning

confidence: 99%

“…FIGURE 14Rider trajectories (top) and comparison (bottom) of male HBMs for lateral car impact ([77]). …”

mentioning

confidence: 99%

“…FIGURE15 Resulting head COG linear accelerations for ATDs and HBMs. Note: THUMS AM50 does not provide a head COG acceleration sensor ([77]). …”

mentioning

confidence: 99%

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Dynamic Human Body Models in Vehicle Safety: An Overview

Fahse¹,

Kempter²,

Maier³

et al. 2023

Preprint

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Significant trends in the vehicle industry are autonomous driving, micromobility, electrification and the increased use of shared mobility solutions. These new vehicle automation and mobility classes lead to a larger number of occupant positions, interiors and load directions. As safety systems interact with and protect occupants, it is essential to place the human, with its variability and vulnerability, at the center of the design and operation of these systems. Digital human body models (HBMs) can help meet these requirements and are therefore increasingly being integrated into the development of new vehicle models. This contribution provides an overview of current HBMs and their applications in vehicle safety in different driving modes.The authors briefly introduce the underlying mathematical methods and present a selection of HBMs to the reader. An overview table with guideline values for simulation times, common applications and available variants of the models is provided.To provide insight into the broad application of HBMs, the authors present three case studies in the field of vehicle safety: (i) in-crash finite element simulations and injuries of riders on a motorcycle; (ii) scenario-based assessment of the active precrash behavior of occupants with the Madymo multibody HBM; (iii) prediction of human behavior in a take-over scenario using the EMMA model.

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Section: Case Study -Safe Motorcyclementioning

confidence: 99%

Section: Positioning Hbms For Motorcycle Rider Posturesmentioning

confidence: 99%

“…FIGURE13 Rider trajectories of Hybrid III 50 th percentile ATD (top) and VIVA+ 50M HBM (bottom) for lateral car impact ([77]). …”

mentioning

confidence: 99%

“…FIGURE 14Rider trajectories (top) and comparison (bottom) of male HBMs for lateral car impact ([77]). …”

mentioning

confidence: 99%

“…FIGURE15 Resulting head COG linear accelerations for ATDs and HBMs. Note: THUMS AM50 does not provide a head COG acceleration sensor ([77]). …”

mentioning

confidence: 99%

See 3 more Smart Citations

Dynamic Human Body Models in Vehicle Safety: An Overview

Fahse¹,

Kempter²,

Maier³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Dynamic human body models in vehicle safety: An overview

et al. 2023

View full text Add to dashboard Cite

Significant trends in the vehicle industry are autonomous driving, micromobility, electrification and the increased use of shared mobility solutions. These new vehicle automation and mobility classes lead to a larger number of occupant positions, interiors and load directions. As safety systems interact with and protect occupants, it is essential to place the human, with its variability and vulnerability, at the center of the design and operation of these systems. Digital human body models (HBMs) can help meet these requirements and are therefore increasingly being integrated into the development of new vehicle models. This contribution provides an overview of current HBMs and their applications in vehicle safety in different driving modes. The authors briefly introduce the underlying mathematical methods and present a selection of HBMs to the reader. An overview table with guideline values for simulation times, common applications and available variants of the models is provided. To provide insight into the broad application of HBMs, the authors present three case studies in the field of vehicle safety: (i) in‐crash finite element simulations and injuries of riders on a motorcycle; (ii) scenario‐based assessment of the active pre‐crash behavior of occupants with the Madymo multibody HBM; (iii) prediction of human behavior in a take‐over scenario using the EMMA model.

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Cushioning performance analysis of multilayered rubber materials with nonuniform friction coefficients of corrugated contact surface under the bipotential framework

Tao,

Feng

2023

Multibody Syst Dyn

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Efficient simulation strategy to design a safer motorcycle

Cited by 6 publications

References 25 publications

Dynamic Human Body Models in Vehicle Safety: An Overview

Dynamic Human Body Models in Vehicle Safety: An Overview

Dynamic human body models in vehicle safety: An overview

Cushioning performance analysis of multilayered rubber materials with nonuniform friction coefficients of corrugated contact surface under the bipotential framework

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