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Journal Article

A New Data-Driven Design Method for Thin-Walled Vehicular Structures Under Crash Loading

A new design methodology based on data mining theory has been proposed and used in the vehicle crashworthiness design. The method allows exploring the big dataset of crash simulations to discover the underlying complicated relationships between response and design variables, and derive design rules based on the structural response to make decisions towards the component design. An S-shaped beam is used as an example to demonstrate the performance of this method. A large amount of simulations are conducted and the results form a big dataset. The dataset is then mined to build a decision tree. Based on the decision tree, the interrelationship among the geometric design variables are revealed, and then the design rules are derived to produce the design cases with good energy absorbing capacity. The accuracy of this method is verified by comparing the data mining model prediction and simulation data.
Technical Paper

Characterization of Human Rib Biomechanical Responses due to Three-Point Bending

In the elderly population, rib fracture is one of the most common injuries sustained in motor vehicle crashes. The current study was conducted to predict the biomechanical fracture responses of ribs with respect to age, gender, height, weight and percentage of ash content. Three-point bending experiments were conducted on 278 isolated rib samples extracted from 82 cadaver specimens (53 males and 29 females between the ages of 21 and 87 years) for 6th and 7th levels of ribs. Statistical analyses were carried out to identify differences based on age and gender. It was found that, in comparison to males, females had significantly lower values for maximum bending moments, slopes of bending moment-angle curves, and average cortical-bone thickness (p < 0.05). Samples of ribs taken from elderly specimens failed at lower values of fracture moments than those from younger specimens, and had lower slopes of bending moment-angle curves, both in males and females (p < 0.05).
Technical Paper

Effect of Boot Compliance in Numerical Model of Hybrid III in Vertical Loading

Numerical models of Hybrid III had been widely used to study the effect of underbody blast loading on lower extremities. These models had been primarily validated for automotive loading conditions of shorter magnitude in longer time span which are different than typical blast loading conditions of higher magnitude of shorter duration. Therefore, additional strain rate dependent material models were used to validate lower extremity of LSTC Hybrid III model for such loading conditions. Current study focuses on analyzing the mitigating effect of combat boots in injury responses with the help of validated LSTC Hybrid III model. Numerical simulations were run for various impactor speeds using validated LSTC Hybrid III model without any boot (bare foot) and with combat boot.
Technical Paper

Introduction of Two New Pediatric Finite Element Models for Pedestrian and Occupant Protections

To help predict the injury responses of child pedestrians and occupants in traffic incidents, finite element (FE) modeling has become a common research tool. Until now, there was no whole-body FE model for 10-year-old (10 YO) children. This paper introduces the development of two 10 YO whole-body pediatric FE models (named CHARM-10) with a standing posture to represent a pedestrian and a seated posture to represent an occupant with sufficient anatomic details. The geometric data was obtained from medical images and the key dimensions were compared to literature data. Component-level sub-models were built and validated against experimental results of post mortem human subjects (PMHS). Most of these studies have been mostly published previously and briefly summarized in this paper. For the current study, focus was put on the late stage model development.
Technical Paper

On the Development of a New Design Methodology for Vehicle Crashworthiness based on Data Mining Theory

This paper represents the development of a new design methodology based on data mining theory for decision making in vehicle crashworthy components (or parts) development. The new methodology allows exploring the big crash simulation dataset to discover the underlying complicated relationships between vehicle crash responses and design variables at multi-levels, and deriving design rules based on the whole vehicle safety requirements to make decisions towards the component and sub-component level design. The method to be developed will resolve the issue of existing design approaches for vehicle crashworthiness, i.e. limited information exploring capability from big datasets, which may hamper the decision making and lead to a nonoptimal design. A preliminary design case study is presented to demonstrate the performance of the new method. This method will have direct impacts on improving vehicle safety design and can readily be applied to other complex systems.