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This paper presents a statistical characterisation of the effects of variations in vehicle parameters on vehicle road load data using a quarter vehicle as a case study. A model of a quarter vehicle test rig constructed from a commercial SUV is created in a multi-body dynamics (MBD) simulation environment to reproduce the real-life behaviour of the SUV. The model is thereafter validated by correlating the response data collected from both the model and laboratory test rig to the same road input. In order to ensure that only the effects of the variation of the vehicle parameters are captured, a time domain drive signal for a kerb strike road event on the physical vehicle is generated from the proving ground data collected during durability testing of the vehicle.

This research proposes the use of Artificial Neural Networks (ANN) to predict the road input for road load data generation for variants of a vehicle as vehicle parameters are modified. This is important to the design engineers while the vehicle variant is still in the initial stages of development, hence no prototypes are available and accurate proving ground data acquisition is not possible. ANNs are, with adequate training, capable of representing the complex relationships between inputs and outputs. This research explores the implementation of the ANN to predict road input for vehicle variants using a quarter vehicle test rig. The training and testing data for this research are collected from a validated quarter vehicle model.

In the development of a more accurate laboratory scale method, the ability to replicate the thermal oxidative degradation mechanisms seen in gas turbine lubricants, is an essential requirement. This work describes an investigation into the influence of key reaction parameters and the equipment set up upon extent and mechanism of oil degradation. The air flow rate through the equipment was found to be critical to both degradation rate and extent of volatilization loss from the system. As these volatile species can participate in further reactions, it is important that the extent to which they are allowed to leave the test system is matched, where possible, to the conditions in the gas turbine. The presence of metal specimens was shown to have a small influence on the rate of degradation of the lubricant. Loss of metal from the copper and silver specimens due to the mild corrosive effect of the lubricant was seen.