Power Spectral Density of Velocity Fluctuations Estimated from Phase Doppler Data

Abstract

Laser Doppler Anemometry (LDA) and its modifications such as Phase-Doppler Particle Anemometry (P/DPA) is point-wise method for optical non-intrusive measurement of particle velocity with high data rate. Conversion of the LDA velocity data from temporal to frequency domain – calculation of power spectral density (PSD) of velocity fluctuations, is a non trivial task due to non-equidistant data sampling in time. We briefly discuss possibilities for the PSD estimation and specify limitations caused by seeding density and other factors of the flow and LDA setup. Arbitrary results of LDA measurements are compared with corresponding Hot Wire Anemometry (HWA) data in the frequency domain. Slot correlation (SC) method implemented in software program Kern by Nobach (2006) is used for the PSD estimation. Influence of several input parameters on resulting PSDs is described. Optimum setup of the software for our data of particle-laden air flow in realistic human airway model is documented. Typical character of the flow is described using PSD plots of velocity fluctuations with comments on specific properties of the flow. Some recommendations for improvements of future experiments to acquire better PSD results are given.

Laser Doppler Anemometry (LDA) and its modifications such as Phase-Doppler Particle Anemometry (P/DPA) is point-wise method for optical non-intrusive measurement of particle velocity with high data rate. Conversion of the LDA velocity data from temporal to frequency domain – calculation of power spectral density (PSD) of velocity fluctuations, is a non trivial task due to non-equidistant data sampling in time. We briefly discuss possibilities for the PSD estimation and specify limitations caused by seeding density and other factors of the flow and LDA setup. Arbitrary results of LDA measurements are compared with corresponding Hot Wire Anemometry (HWA) data in the frequency domain. Slot correlation (SC) method implemented in software program Kern by Nobach (2006) is used for the PSD estimation. Influence of several input parameters on resulting PSDs is described. Optimum setup of the software for our data of particle-laden air flow in realistic human airway model is documented. Typical character of the flow is described using PSD plots of velocity fluctuations with comments on specific properties of the flow. Some recommendations for improvements of future experiments to acquire better PSD results are given.