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Analysis of the in-cylinder flow of a DISI engine using high-speed particle image velocimetry

J. Knöll, E. Mäteling, M. Braun, M. Klaas, W. Schröder

Chair of Fluid Mechanics and Institute of Aerodynamics, RWTH Aachen University, Germany

In addition to exploring electric drives, it is crucial to minimize emissions and improve the efficiency of transport based on internal combustion engines (ICE). For these, in particular, a significant reduction of emissions as well as a considerable efficiency of ICE are required. A reliable formation of an effective mixture prior to ignition and a fast, steady combustion process are two key elements that influence emissions and efficiency. While on the one hand, the air-fuel mixture is formed by strongly three-dimensional, time-dependent flow phenomena during the intake and compression stroke, the turbulence level of the flow near the approaching flame front has a direct impact on turbulent flame propagation. The flow field in the combustion chamber, on the other hand, is highly turbulent and sensitive to cycle-to-cycle variations (CCV). In order to understand the fundamental processes and interactions of the flow field, flow measurements with high spatial and temporal resolution are required. To analyze CCV, the resulting flow field data must be decomposed into its scales, since CCVs are different from random fluctuations due to turbulent flow motions. Besides velocity decomposition based on traditional Reynolds decomposition, which provides an average flow velocity and a fluctuation velocity, most studies include statistical and linear approaches such as proper orthogonal decomposition (POD) and Fast Fourier transformation (FFT). In this paper, time-resolved high-speed stereoscopic PIV (HS-SPIV) measurements on an optical single-cylinder engine are processed using a new nonlinear and transient decomposition method, namely the multivariate empirical mode decomposition (NA-MEMD) based on a data-driven decomposition algorithm. The decomposition is based on the inherent scales of the data and generates physically valid modal representations known as intrinsic mode functions (IMFs), which are arranged with respect to the contained scale values. The approach and the IMFs generated are described in detail using one engine cycle as an example. According to the results, three modes are obtained, the measurement noise and extremely tiny short-term scales, the turbulent scales, and the large-scale tumble vortex.

20th Edition
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