<p>The understanding of cycle-to-cycle variation of engine in-cylinder flow is critical for the improvement of engine performance. Proper orthogonal decomposition (POD), with its ability to extract the more energetic fluctuation structures, is widely used to analyze the in-cylinder flow and understand the variation of its evolution in different engine cycles. However, neither of the two prevailing approaches to use POD for engine flow analysis is satisfactory for this purpose. Phase-dependent POD decomposes separate flow data sets, each containing samples taken at the same phase of different engine cycles, but the POD results from neighboring engine phases do not necessarily evolve smoothly. On the other hand, phase-invariant POD includes flow field samples taken at different engine phases and cycles within a single data set, but the method necessitates extensive flow data manipulations that complicate straightforward physical interpretation of the POD results. In this paper, we adapt the recently proposed conditional space-time POD to the analysis of engine flow, with data recorded over a range of phases within each engine cycle considered as one sample. It will be shown that the low-order modes obtained with this new POD approach capture fluctuation patterns that evolve smoothly over engine phases, and these results are compared and contrasted with those of the prevailing POD approaches. A low-order model of the engine in-cylinder flow will be constructed based on the partial sum of the modes and coefficients obtained from the conditional space-time POD, and it will be shown that this new low-order model identifies fluctuation flow structures that are coherent both spatially and temporally.</p>

Application of Conditional Space-Time Proper Orthogonal Decomposition to Engine In-Cylinder Flow Analysis

Paper Proceedings: https://asmedigitalcollection.asme.org/ICEF/ICEF2021/volume/85512

ASME 2021 Internal Combustion Engine Division Fall Technical Conference (ICEF2021)

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