A direct transform method for the analysis of laser Doppler anemometry engine data

A new, computationally efficient method of fitting a specific parametric stochastic model of in-cylinder flow to real laser Doppler anemometry (LDA) internal combustion engine data, obtained either at a single position in space or over a range of spatial positions at effectively one point in the engine cycle (scanning data) is proposed. The model, which is fully specified in terms of a set of parameters, assumes that the velocity variations can be modelled as the sum of ail ensemble-averaged mean component, a non-stationary 'turbulence' component and a random 'cycle-to-cycle' component, the last of these being phase locked to the engine cycles. The fitting technique is based on a direct transform of the velocity data obtained in each cycle and leads to an identification of the mean-square magnitudes of the turbulence and cycle-to-cycle components. A unique advantage of this approach is that it is possible to determine, theoretically, confidence limits for the estimates of the statistics or the fluctuation components. This provides a theoretical means or designing experiments to achieve a prescribed level of accuracy. The estimation method, and the theoretical determination of confidence limits, is validated initially through application to some simulated data which fully capture all the characteristics inherent in real data. Finally some results of analysing real in-cylinder LDA data, of the scanning type, are presented. Although the approach proposed here is applied to a particular model of in-cylinder flow it is, in principle, extendable to other models.