Abstract
Different signal processing technique performances are compared to each other with regard to separating the mean and fluctuating velocity components of a simulated one-dimensional unsteady velocity signal comparable to signals observed in internal combustion engines. A simulation signal with known mean and fluctuating components was generated using experimental data and generic turbulence spectral information. The simulation signal was generated based on observations on the measured velocity data obtained using LDV in a motored Briggs-and-Stratton engine at about 600 RPM. Experimental data was used as a guide to shape the simulated signal mean velocity variation; fluctuating velocity variations with specified spectrum and standard deviation was used to mimic the turbulence. Cyclic variations were added both to the mean and the fluctuating velocity signals to simulate prescribed cyclic variations. The simulated signal was introduced as input to the following algorithms: ensemble averaging; high-pass filtering; Proper-Orthogonal Decomposition (POD); Wavelet Decomposition (WD) and Wavelet Decomposition/Principal Component Analysis (WD/PCA). The results were analyzed to determine the best method in correctly separating the mean and the fluctuating velocity information, indicating that the WD/PCA performs better compared to other techniques.
Highlights
Understanding the nature of turbulence in internal combustion engines is important in studying the underlying mechanisms between turbulence and related phenomena, such as noise generation or lean/clean combustion [1]
The simulated signal was introduced as input to the following algorithms: ensemble averaging; high-pass filtering; Proper-Orthogonal Decomposition (POD); Wavelet Decomposition (WD) and Wavelet Decomposition/Principal Component Analysis (WD/PCA)
Figures indicate that the differences between the velocity signals are the smallest using the WD/PCA method, and that the POD method results contain high frequency content
Summary
Understanding the nature of turbulence in internal combustion engines is important in studying the underlying mechanisms between turbulence and related phenomena, such as noise generation or lean/clean combustion [1]. Combustion quality depends on the turbulent mixing of fuel and air; turbulence defines the flame speed, burning temperature and the emission of pollutants from combustion processes. Much research has been undertaken to study the in-cylinder flow fields using many different experimental techniques [2], including hotwire anemometry [3], particle tracking velocimetry (PTV) [4,5,6,7], particle image velocimetry (PIV) [7,8,9] and LDV [10,11,12,13,14,15,16,17,18]. LDV has been widely used since LDV can be adapted to study flow fields within hard to reach geometries such as the valve exit flow or, the flow inside complex bowl-piston configurations [19]
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