Development, Calibration, and Validation of a Head–Neck Complex of THOR Mod Kit Finite Element Model

Abstract

Introduction/Objective: In an effort to continually improve upon the design of the test device for human occupant restraint (THOR) dummy, a series of modifications have recently been applied. The first objective of this study was to update the THOR head–neck finite element (FE) model to the specifications of the latest dummy modifications. The second objective was to develop and apply a new optimization-based methodology to calibrate the FE head–neck model based on experimental test data. The calibrated head–neck model was validated against both frontal and lateral impact test data. Finally, the sensitivities of the model, in terms of head and neck injury criteria, to pretest positioning conditions were evaluated in a frontal crash test simulation. Methods: The updated parts of the head–neck THOR FE model were remeshed from CAD geometries of the modified parts. In addition, further model modifications were made to improve the effectiveness of the model (e.g., model stability). A novel calibration methodology, which incorporates the CORA (CORelation and Analysis) rating system with an optimization algorithm implemented in Isight software, was developed to improve both kinematic and kinetic responses of the model in various THOR dummy certification and biomechanical response tests. A parametric study was performed to evaluate head and neck injury criteria values in the calibrated head–neck model during a 40 km/h frontal crash test with respect to variation in the THOR model upper body and belt pretest position. Results: Material parameter optimization was shown to greatly improve the updated model response by increasing the average rating score from 0.794 ± 0.073 to 0.964 ± 0.019. The calibrated neck showed the biggest improvement in the pendulum flexion simulation from 0.681 in the original model up to 0.96 in the calibrated model. The fully calibrated model proved to be effective at predicting dummy response in frontal and lateral loading conditions during the validation phase (0.942 average score). Upper body position was shown to have a greater effect on head–neck response than belt position. The pretest positioning variation resulted in a 10 percent maximum change in HIC₃₆ values and 14 percent maximum change in N_IJ values. Conclusion: The optimization-based calibration methodology was effective as it markedly improved model performance. The calibrated head–neck model demonstrated application in a crash safety analysis, showing slight head–neck injury sensitivity to pretest positioning in a frontal crash impact scenario.