A Data-Based Modeling Approach for the Prediction of Front Impact (NCAP) Safety Performance of a Passenger Vehicle

Designing a vehicle for superior crash safety performance in consumer rating tests such as US-NCAP is a compelling target in the design of passenger vehicles. In today’s context, there is also a high emphasis on making a vehicle as lightweight as possible which calls for an efficient design. In modern vehicle design, these objectives can only be achieved through Computer-Aided Engineering (CAE) for which a detailed CAD (Computer-Aided Design) model of a vehicle is a pre-requisite. In the absence of the latter (i.e. a matured CAD model) at the initial and perhaps the most crucial phase of vehicle body design, a rational approach to design would be to resort to a knowledge-based methodology which can enable crash safety assessment of an assumed design using artificial intelligence techniques such as neural networks. In the current study, the implied objective is achieved by building a training database with the aid of US-NCAP simulations of validated real-world full vehicle finite element models developed by the National Crash Analysis Center (NCAC), The George Washington University, Ashburn, Virginia, USA. By using a Bayesian Regularization Backpropagation neural network, a key crash safety parameter, namely peak deceleration in NCAP crash pulse for a vehicle, is predicted, given the values of several packaging and structural parameters pertaining to its front structure. Additionally, the “pulse waveform efficiency” factor is predicted for assessing the efficiency of the design i.e. the degree of impact energy absorption with respect to the available crush space. The latter parameter can be helpful in reducing the weight of a vehicle for a target safety performance.