Autocorrelation Based Analysis of Ensemble Averaged LDA Engine Data for Bias-Free Turbulence Estimates: A Unified Approach 910479

Earlier work has shown that the in-cylinder flow in internal combustion engines can be modelled, with reasonable accuracy, as the sum of an ensemble averaged mean component, a non-stationary ‘turbulence’ component and a ‘cycle-to-cycle’ variation component, the latter being phase-locked to the engine cycles. The development of the LDA technique has enabled direct measurements of the in-cylinder velocity field to be taken, either at a single position in space over the engine cycle, or over a range of spatial positions, at effectively one point in the engine cycle (scanning LDA).
Previously, different approaches have been developed for separating the various flow components in the model described above, dependent on the type of data acquired. In this paper a single ‘unified’ method is presented, based on the computation of autocorrelation functions and a completely parametric representation of the various components in the flow model. This method can be applied to both single position and scanning LDA data, and enables the magnitude of the various flow components to be quantified.
Results of applying the method to some experimental engine data are presented. For this purpose both single position and scanning LDA data, acquired under the same engine conditions, were processed. A quantitative comparison between the extracted flow component magnitudes and estimated length scales, from the two types of data, is given. Through the use of a comprehensive simulation model of the data, an estimate of the level of the statistical uncertainty associated with the extracted flow components is obtained.


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