Using multi-layer perceptrons to predict vehicle pass-by noise

All new vehicle designs have to pass a legislative, noise emission test - the 'pass-by noise' test. In the highly competitive automotive industry, it is important to predict the test result early in the design process, rather than waiting until a prototype is built. Engineers can 'guess' test results about as well as the best, although inadequate, analytical models. They achieve this by using experience and their knowledge of acoustics and of the vehicle's design. Neural networks should also be capable of pass-by noise prediction, learning from the results of previous tests. This paper describes a neural network approach to the problem. First, expert knowledge is used to select vehicle design and test parameters to present as inputs to a multi-layer perceptron. Since data is scarce, the problem is broken down into two stages, vehicle performance and pass-by noise. The two trained networks are evaluated and their performance discussed

Sound source contributions for the prediction of vehicle pass-by noise

Current European legislation aims to limit vehicle noise emissions since many people are exposed to road traffic noise in urban areas. Vehicle pass-by noise is measured according to the international standard ISO 362 in Europe. More recent investigations of urban traffic have led to the proposal of a revised ISO 362 which includes a constant-speed test in addition to the traditional accelerated test in order to determine the pass-by noise value. In order to meet the legal pass-by noise requirements, vehicle manufacturers and suppliers must analyse and quantify vehicle noise source characteristics during the development phase of the vehicle. In addition, predictive tools need to be available for the estimation of the final pass-by noise value. This thesis aims to contribute to the understanding of vehicle pass-by noise and of the characteristics of the vehicle noise sources contributing to pass-by noise. This is supported through an extensive literature review in which current pass-by noise prediction methods are reviewed as well. Furthermore, three vehicle noise sources are replicated experimentally under laboratory conditions. This involves an orifice noise source, represented by a specially designed loudspeaker on a moving trolley, shell noise, represented by a metal cylinder structure, and tyre cavity and sidewall noise, represented by an annular membrane mounted on a tyre-like structure. The experimentally determined directivity characteristics of the acoustically excited noise sources are utilised in the pass-by noise prediction method. The predictive results are validated against experimental measurements of the three vehicle-like noise sources made within an anechoic chamber