Over the last decade, crash test regulations have become more stringent, requiring a higher level of performance to achieve a good safety rating. For example, test speeds have been increased, and traditional automotive models were unable to handle this large amount of crash energy which leads to the collapse of the survival space and poor safety ratings. OEMs needed to increase the study of the structure, materials and restraint systems in order to achieve five star ratings again and this is where crashworthiness simulation plays a vital role.
High performance and high fuel economy are the two contradictory targets that push the automotive structure to the limit of a light-weight robust architecture. The arrival of newer vehicle platforms for electric, connected, and autonomous vehicles has shortened product design cycles. As regulations and OEM in-house safety targets tighten, the demand for crash simulations is increasing exponentially.
Crashworthiness relies on the construction of load-carrying structures and the deformation of the structural components which would absorb the crash energy efficiently. Therefore, the crashworthiness design should be started from the early concept design phase. The concept design model needs to be simple, to follow the rapid design change and to be computed in a short time to feedback the results to the design right away. Hence, a highly abstracted model and metadata from Machine-Learning are techniques that can be used effectively to accumulate a database of components for rapid crashworthiness assessment.
There are many technical challenges in achieving the best crash safety ratings and getting regulatory approvals. High fidelity and accurate simulations are essential to obtaining an optimized solution. Abaqus/Explicit supports engineers in accomplishing the safety targets for all vehicle platforms, in all scenarios, and at all speeds.
