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Steering pull during high speed braking of heavy commercial vehicles possesses a potential danger to the occupants. Even with negligible wheel-to-wheel brake torque variation, steering pull during the high speed braking has been observed. If the steering pull (i.e. steering rotation) is forcibly held at zero degree during high speed braking, the phenomena called axle twist, wheel turn and shock absorber deflection arise. In this work the data have been collected on the mentioned measures with an intention to develop a mathematical model which uses real time data, coming from feedback mechanism to predict the values of the measures in coming moments in order to aid steering system to ‘auto-correct’. Driven by the intention, ‘Time Series Analysis’, a well-known statistical methodology, has been explored to see how suitable it is in building the kind of model.

This paper presents a systematic approach for designing an experiment in situations where expensive and time consuming computer simulations are used to evaluate product characteristics. In the presence of many design parameters, the critical step is to find the best possible experimental set up with minimum number of simulations. Usually in such situations, designers use their intuition and experience to carry out a number of simulation runs and choose the design that gives better performance. This intuitive approach can be considerably improved by using statistical methods. “Classical experimental designs” were compared with “space filling designs” in terms of their results and requirements. A typical clutch booster bracket is used as an example to demonstrate the methodology.