Publication

Human body modeling for integrated safety analyses using THUMS

To successfully apply human body models to analyze real life safety systems, they have to: (1) be numerically robust in a wide range of crash loading conditions, (2) be computationally efficient to enable analyses with full car models, (3) represent the human population with respect to age, gender and anthropometry, (4) maintain its posture in a gravitational field for pre-crash events, (5) predict the onset of tissue injury and organ failure, and (6) simulate muscle tension due to bracing and muscle reflexes. Therefore, work is ongoing to model the active muscle response and improve the injury predictability of human body models.  The Total Human Model for Safety (THUMS) versions 1.4, 3.0 and 4.0, were used with the explicit capabilities in the FE code LS-DYNA. It is a model of a 50th percentile adult male occupant. To study thoracic injuries, the responses of the THUMS were compared to several cadaver experiments. Then, a sensitivity study was performed to evaluate the influence of belt interaction and tissue parameters on the predicted thoracic response.  Lastly, several candidates to predict rib cage fractures were compared in loading conditions relevant to frontal car crashes.  The central nervous system controls the muscle contraction and was modeled using feedback proportional, integral, and derivative (PID) control. The reference signals are joint angles defining a body position. The neural delays, time needed for the nerve signals to travel back and forth from the muscle to the central nervous system, and muscle activation dynamics are included. The active THUMS was compared to passenger kinematics in autonomous braking events. It was seen that by changing the controller gains, the model can capture differences in the muscle response when the human is relaxed compared to tensed. To provide validation data for the active muscle model, volunteer experiments were performed where the muscle activation, occupant kinematics and boundary conditions were collected during autonomous and driver initiated braking during normal driving.

Author(s)
Karin Brolin, Manuel Mendoza-Vazquez, Jonas Östh, Jona Marin Olafsdottir, Ruth Paas, Johan Davidsson
Research area
Human body protection
Publication type
Conference paper
Published in
THUMS European Users’ Meeting, June 2013, Manchester
Project
Improved injury prediction using HBM (B7)
Year of publication
2013