A high-fidelity biomechanical modeling framework for injury prediction during frontal car crash

Injuries arising from car crashes are ubiquitous across the globe and account for over 1.3 million fatalities annually. 93% of mortalities are observed in middle- and low-income countries owing to the lack of infrastructure in the safety assessment of car designs. It is therefore imperative to predict the extent of injuries to the occupants during car crashes, which would lead to safer vehicle design. To date, conventional computational testing methods use Hybrid III dummies, which fail to reproduce fracture and tear injuries. In this work, a full-frontal collision of a vehicle against a rigid wall with a highly biofidelic human body model of an occupant was simulated for the first time to investigate fractures and tears using a novel fracture modeling technique. Fractures were observed in ribs (5-7), which occurred at stresses of 120 MPa at the left lateral vertebrosternal region. In the lower extremity, tears in the ligaments at 70.80 MPa, and fractures in the tibia and femur at 236 MPa were quantified. Stresses in the skull were limited to 11 MPa, indicating a possibility of concussion rather than fractures. The developed computational model would be indispensable for car manufacturers to test the crash impact on the human body at all possible accident scenarios accurately, which will help design better solutions for automotive injury mitigation.