AUTHOR=Yang Xiuju , Shi Jingjing , Fu Qianying , Pu Shanshan , Lian Chunxiao , Li Kui , Yin Zhiyong , Liu Shengxiong , Wang Guixue TITLE=Optimization of the driver’s seat belt and injury biomechanical analysis in real-world minivan small offset impact accident scenarios JOURNAL=Frontiers in Bioengineering and Biotechnology VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2022.965206 DOI=10.3389/fbioe.2022.965206 ISSN=2296-4185 ABSTRACT=

To minimize injuries and protect the safety of the driver in minivan small offset collisions, an optimized pre-tensioned force-limiting seat belt was proposed herein. An accident with detailed information, such as medical reports, vehicle inspection reports, and accident scene photographs, was reconstructed using HyperMesh software. The effectiveness of both the accident model and the pre-tensioned force-limiting seat belt was evaluated. To obtain the optimal seat belt parameters for driver protection, first, force-limiting A, pre-tensioned force B, and pre-tensioned time C factors were selected in designing an orthogonal test with different factor levels. The influence laws of each factor on the injury biomechanical characteristics of the driver were analyzed via the direct analysis method. Moreover, each kind of critical injury value of the human body was synthesized, and the radial basis function surrogate model was constructed. The three seat belt parameters were optimized using the NSGA-II multi-objective genetic algorithm. The results showed that the optimal balance variable parameter of the seat belt was 4751.618 N–2451.839 N–17.554 ms (A–B–C). Finally, the optimal scheme was verified in a system simulating a minivan small offset collision. The results showed that after optimization, the skull von Mises stress was reduced by 36.9%, and the stress of the cervical vertebra cortical bone and cancellous bone decreased by 29.1% and 30.8%, respectively. In addition, the strains of the ribs and lungs decreased by 31.2% and 30.7%, respectively.