Session: 07-01-01: Injury Risk Assessment due to Blunt Impact

Paper Number: 165305

Cause-and-Effect Relationship Between the Loading Conditions and Hybrid III 50th Dummy Responses 

This report details the development and application of a linear regression model designed to predict chest deflection in a Hybrid III 50th percentile male crash test dummy during simulated full-frontal vehicle collisions. Chest deflection is a critical indicator of occupant safety and is directly related to the potential for thoracic injuries. Accurate prediction of chest deflection is essential for optimizing restraint system designs and evaluating vehicle crashworthiness. The analysis presented herein utilized a validated computer-aided engineering (CAE) sled model to simulate a range of crash scenarios and quantify the influence of various loading parameters on dummy response. This approach allows for a detailed investigation of the complex interactions between the occupant and the restraint system.

The predictive model was constructed using data generated from a series of CAE simulations employing a Hybrid III 50th percentile male dummy within a generic driver and passenger vehicle environment. The simulations were conducted using a validated CAE sled model, allowing for precise control and manipulation of key crash parameters. The model incorporated a representative vehicle interior, including the seat, seatbelt, body structure , driver and passenger airbags, a load-limiting seatbelt system, and the instrument panel. The vehicle data and impact pulse used in the simulations were derived from a current production body-on-frame vehicle.

To systematically assess the impact of various loading conditions on chest deflection, a parametric study was conducted, varying the following parameters:

Impact Velocity: Five distinct impact velocities were simulated: 16 mph , 22 mph , 25 mph and 35 mph. These velocities represent a range of typical crash scenarios and allowed for the assessment of the relationship between impact severity and chest deflection.

Airbag Characteristics: The influence of airbag stiffness and shape on chest deflection was investigated by simulating both driver and passenger airbags with dual-stage inflator outputs. This allowed for the evaluation of the effectiveness of different airbag deployment strategies in mitigating chest injuries.

Seat Belt Load Limiter: The effect of the seat belt load limiter was examined by simulating three different constant load limiter settings: 2.5 kN, 4.5 kN, and 6.0 kN. These settings represent a range of load-limiting capabilities and allowed for the optimization of the seatbelt system to minimize chest loading while maintaining occupant restraint.

To maintain simplicity and facilitate quantitative analysis, a simplified restraint system was employed. This approach allowed for a focused assessment of the influence of the key parameters under investigation, minimizing the potential for confounding effects from more complex restraint technologies.

Chest deflection was measured directly using an internal linear chest potentiometer incorporated within the Hybrid III dummy. This sensor provides a continuous measurement of chest compression throughout the crash event, allowing for a detailed characterization of the dummy's response. The force imparted to the chest was calculated based on the resultant loads acting on the dummy, considering contributions from the clavicle, airbag, neck, lumbar spine, and shoulder belt.

The collected data, consisting of calculated chest forces and corresponding chest deflection measurements, was then used to develop a linear regression model. The model was constructed using standard statistical techniques to establish a relationship between the independent variables (impact velocity, airbag characteristics, seat belt load limiter setting) and the dependent variable (chest deflection).

The linear regression model developed in this study provided a quantitative assessment of the relationship between various crash parameters and chest deflection in the Hybrid III dummy. The analysis revealed the relative importance of each parameter in influencing chest loading. The model's predictive capability was evaluated by comparing predicted chest deflection values with those obtained from the CAE simulations. The results of this comparison demonstrated a reasonable level of agreement, indicating that the model can be used to estimate chest deflection under a range of simulated crash conditions.

It is important to acknowledge the limitations of this study. The analysis was based on a simplified restraint system, and the results may not be directly applicable to vehicles equipped with more advanced restraint technologies. Furthermore, the model was validated using a limited set of crash scenarios, and its predictive capability may be reduced under more extreme or atypical crash conditions. The study focuses primarily on the predictive capabilities of the methodology from standard CAE evaluations and typical test boundary conditions. It is crucial to recognize that the simulations do not fully represent real-world injury modes, as simplified assumptions about the restraint system were implemented to emphasize the analytical methodology.

This study successfully developed a linear regression model for predicting chest deflection in a Hybrid III dummy during simulated full-frontal vehicle collisions. The model provides a quantitative assessment of the influence of various crash parameters on chest loading and can be used to optimize restraint system designs. However, it is important to recognize the limitations of the study and to interpret the results with caution. Future research should focus on validating the model using a broader range of crash scenarios and incorporating more advanced restraint technologies.

Presenting Author: murugan sundaram ramasamy Ford Motor Company

Presenting Author Biography: Bachelor of Engineering in Mechanical Engineering from Anna University, India
M.S Aerospace Engineering from University of Texas at Arlington
Experience since 2012 as crash safety Engineer at Ford Motor Company
Currently pursuing PHD at Lawrence Technological University.

Authors:

murugan sundaram ramasamy Ford Motor Company
Krishnakanth Aekbote Ford Motor Company
Ulises Herrera Ezquivel Ford Motor Company
Vernon Fernandez Lawrence Technological University
Badih Jawad Lawrence Technological University
Selin Arslan Lawrence Technological University
Liping Liu Lawrence Technological University
Sabah Abro Lawrence Technological University