Proper orthogonal decomposition of in-cylinder engine flow into mean component, coherent structures and random Gaussian fluctuations

Abstract

A snapshot proper orthogonal decomposition (POD) is performed from 2D time-resolved PIV measurements obtained in the tumble plane of a Spark Ignition engine flow. Based on this filtering approach, the in-cylinder flow field is decomposed into a mean part, a coherent part and a turbulent incoherent part. The analysis of the one-point statistical moments of orders 3 and 4 (skewness and flatness coefficients) as well as the analysis of the probability density function of the velocity field show that the POD extracts an incoherent random velocity field having Gaussian distribution properties. These small amplitude background fluctuations are also homogeneous. We then demonstrate the ability of the POD procedure, based on an energy criterion, in separating the coherent part and random Gaussian fluctuations. Based on this POD flow partitioning, providing a new triple decomposition of the instantaneous turbulent flow, the flow engine cycle-to-cycle variations associated with each velocity field contribution are then accessed. Thus, one of the first full field quantitative analyses of the cyclic variabilities associated with each part of the in-cylinder flow field is provided from the analysis of the corresponding POD temporal coefficients.

Quadratic stochastic estimation of far‐field acoustic pressure with coherent structure events in a 2D compressible plane mixing layer

“…The origin of this Gaussian contribution is still an open issue and may be interpreted in different ways. It is important to note that similar results have been obtained using experimental data in very different flow configurations (Hot Wire Anemometry database [29] or Particle Image Velocimetry database [30]). First, it might be linked to residual eigenmodes related to low-energy numerical or measurement errors.…”

“…In the same way, such structures are related to broadband spectra on non-local bases like the Fourier basis. Similar POD decompositions have been already used to extract the mean part, coherent part and background fluctuations of turbulent flows [29,30]. In these previous works, the background fluctuations were supposed to be QG.…”

Proper orthogonal decomposition analysis for cycle-to-cycle variations of engine flow. Effect of a control device in an inlet pipe

“…The essential idea of the POD is finding among a set of realizations of the flow fields the one that maximizes the mean square energy. The method has been intensively investigated for real-time flow control and physical process simulation because of its ability to yield a basis for low-order dynamic systems (Berkooz 1991;Ly and Tran 2001;Utturkar et al 2005;Rowley et al 2004;Ravindran 2002;Epureanu 2003;Perret et al 2006), and especially for the cyclic variability evaluation via statistical properties of the POD temporal coefficients (Druault et al 2005;Druault and Chaillou 2007;Roudnitzky et al 2006;Cosadia et al 2006;Bizon et al 2010;Fogleman et al 2004;Graftieaux et al 2001). The paper is organized as follows.…”

“…We aim essentially to recommend which flap position among S1, S2 and S3 gives the less cycle-to-cycle variations. In order to distinguish the cyclic variability levels of the tumble motion influenced by the insertion depths, the POD triple decomposition (Roudnitzky et al 2006;Druault et al 2005) would be useful because it allows us to divide each instantaneous flow into three parts representing distinctive features. The mean part is associated with the most energetic vortex and therefore the average velocity field.…”

A Combined Numerical and Experimental Study of the 3D Tumble Structure and Piston Boundary Layer Development During the Intake Stroke of a Gasoline Engine

“…Much research in recent years has focused on the large-scale tumble structure, and several investigations [1][2][3][4][5][6] have aimed to quantify its cycle-to-cycle variation (CCV). Borée et al [7] analyzed the generation of the tumbling motion using particle image velocimetry (PIV).…”