Identification of Large-Scale Structure Fluctuations in IC Engines using POD-Based Conditional Averaging

Abstract

Cycle-to-Cycle Variations (CCV) in IC engines is a well-known phenomenon and the definition and quantification is well-established for global quantities such as the mean pressure. On the other hand, the definition of CCV for local quantities, e.g. the velocity or the mixture distribution, is less straightforward. This paper proposes a new method to identify and calculate cyclic variations of the flow field in IC engines emphasizing the different contributions from large-scale energetic (coherent) structures, identified by a combination of Proper Orthogonal Decomposition (POD) and conditional averaging, and small-scale fluctuations. Suitable subsets required for the conditional averaging are derived from combinations of the the POD coefficients of the second and third mode. Within each subset, the velocity is averaged and these averages are compared to the ensemble-averaged velocity field, which is based on all cycles. The resulting difference of the subset-average and the global-average is identified as a cyclic fluctuation of the coherent structures. Then, within each subset, remaining fluctuations are obtained from the difference between the instantaneous fields and the corresponding subset average. The proposed methodology is tested for two data sets obtained from scale resolving engine simulations. For the first test case, the numerical database consists of 208 independent samples of a simplified engine geometry. For the second case, 120 cycles for the well-established Transparent Combustion Chamber (TCC) benchmark engine are considered. For both applications, the suitability of the method to identify the two contributions to CCV is discussed and the results are directly linked to the observed flow field structures.