Search Results

Computational analysis of occupant safety has become an efficient tool to reduce the development time for a new product. Multi-body computer models (e.g. Madymo models) that simulate vehicle interior, restraint system and occupants in various crash modes have been widely used in the occupant safety area. To ensure public safety, many injury numbers, such as head injury criteria, chest acceleration, chest deflection, femur loads, neck load, and neck moment, are monitored. Deterministic optimization methods have been employed to meet various safety requirements. However, with the further emphasis on product quality and consistency of product performance, variations in modeling, simulation, and manufacturing, need to be considered.

Computer modeling has played important roles and gained great momentum in product development as numerical methods, computer software and hardware technologies advance rapidly. Computer models (e.g. MADYMO) that simulate vehicle interior, restraint system and occupants in various crash modes have been widely used to improve occupant safety. However, to build good occupant models, engineers often have to spend tremendous time on model correlation. The challenge of model correlation for occupant safety is that it requires matching numerous injury curves with tests, for examples: head G, chest G, chest deflection, shoulder belt load, femur loads, neck load and moment. Traditionally, this model correlation task is done by a trial and error method. This paper attempts to solve the problem systematically by using a genetic algorithm. It demonstrates that the genetic algorithm is a valuable optimization tool to obtain a high quality MADYMO model.