Abstract
The time-shift technique is a well-documented technique for the size and velocity measurement of individual drops passing through one or two tightly focused laser beams. It is a counting technique, nominally applicable for pure liquid drops, but with potential to also characterize drops with embedded particles or drops containing a second dispersed phase. In the present study a novel approach to signal processing is introduced in which the signal detection and validation phase is eliminated. This extends the capabilities of the time-shift technique in two manners. For one, size and velocity estimates are made possible for drops exhibiting very poor signal structure or signal-to-noise ratio. Such signals are commonly expected when measuring complex drops, either drops with embedded nano/micro-particles (dispersions) or emulsions. Second, the size and velocity distributions are estimated not by processing of signals from individual drops (single realization counting technique), but from a large ensemble of drop signals, improving both computational speed and reducing the influence of outliers in final statistics. These capabilities are achieved without sacrificing accuracy of mean and variance estimates of size and velocity of drop ensembles. To demonstrate the advantages of this new approach, measurements of a paint spray are presented, processed using both standard processing routines and the new approach. Limitations concerning the application of this new approach are discussed in detail.
Highlights
The time-shift technique is a counting technique for the measurement of drop size and velocity in a spray
While this approach no longer offers measurement data corresponding to each individual drop, it yields robust and estimators of size and velocity distributions prevailing in the spray
This study has introduced a novel signal processing approach for estimating size and velocity from time-shift signals
Summary
The time-shift technique is a counting technique for the measurement of drop size and velocity in a spray. The present study addresses this problem by eliminating the signal detection step and abandoning the counting approach, i.e. the evaluation of the signals from each individual drop, and reverting to the processing of a large ensemble of drop signals in an integral manner While this approach no longer offers measurement data corresponding to each individual drop, it yields robust and estimators of size and velocity distributions prevailing in the spray. A brief introduction to the principles and implementation of the time-shift technique is first given to establish the signal form expected under ideal conditions This is followed by a description of conventional signal processing algorithms, since results obtained using these algorithms will be used as a reference and comparison to results obtained using the new integrating approach. Sample measurement results will be presented to demonstrate the robustness of the new approach
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