Bivariate 2D empirical mode decomposition for analyzing instantaneous turbulent velocity field in unsteady flows

ECFM-LES modeling with AMR for the CCV prediction and analysis in lean-burn engines

“…Another improvement was made in Adaptive-projection intrinsically transformed MEMD (APIT-MEMD) [36] in which the imbalance between different components of the multivariate signal was taken into account. The 2D multivariate EMD of the present study is a combination of the former two methods that was proposed by Sadeghi et al [37] and noted as Ensemble NA-APIT-MEMD. It was applied to a velocity field constructed with a large-scale organised synthetic vortex and relatively small-scale isotropic homogeneous turbulence.…”

Development and Application of Bivariate 2D-EMD for the Analysis of Instantaneous Flow Structures and Cycle-to-Cycle Variations of In-cylinder Flow

“…Huang et al [24] applied EMD on homogeneous turbulence time series to investigate turbulent scaling intermittency, however such applications are restricted to the temporal analysis of the velocity fields at one point. Recently, Sadeghi et al [41] proposed Bivariate Two dimensional EMD (Bivariate 2D-EMD) in order to separate spatial large-scale organized motion from homogeneous and isotropic turbulent flow. Although EMD analysis has never been applied to confined, unsteady turbulent flows, as those encountered in piston engines, it appears to be promising for engine flow analysis.…”

“…2. The Bivariate 2D-EMD approach decomposes a two-components (bivariate) signal in 2D spatial field [41]. This method is based on Adaptive-Projection Intrinsically Transformed Multivariate EMD (APIT-MEMD) that processes temporal multi-components signals [20].…”