Cause-and-Effect Relationship Between the Loading Conditions and Hybrid III 50th Anthropomorphic Test Device Responses

Abstract

Abstract. This paper details the development and application of a linear regression model to predict chest deflection in a Hybrid III 50th percentile male crash test Anthropomorphic Test Device (ATD) during simulated full-frontal vehicle collisions. Accurate prediction of chest deflection is critical for optimizing restraint system designs and assessing vehicle crashworthiness, given its direct to potential thoracic injuries. The study employed a validated computer-aided engineering (CAE) sled model to simulate a range of crash scenarios within a generic vehicle environment. A parametric investigation systematically varied key loading parameters: impact velocity (16, 22, 25, and 35 mph), airbag characteristics (stiffness, shape, and dual-stage inflator outputs for driver and passenger), and seat belt Constant Force Retractor (CFR) (2.5, 4.5, and 6.0 kN). A simplified restraint system was utilized to isolate the influence of these parameters. Chest deflection was collected from CAE, and chest forces were calculated. The collected data formed the basis for the linear regression model. The developed model quantitatively assessed the relationship between various crash parameters and chest deflection, indicating the relative importance of each. Model predictions demonstrated reasonable agreement with CAE simulation results, confirming its utility for estimating chest deflection under simulated frontal crash conditions. However, the study acknowledges limitations, including the simplified restraint system and a limited set of validation scenarios, suggesting caution when applying results to more advanced systems or extreme conditions. This methodology could be implemented as a design tool earlier in a vehicle program development to decide restraint content that would reduce development time.