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.

We present a method for particle characterization by analyzing the light scattering from a single particle using a shaped light beam, whereby the polarization of the light beam is modulated in time. As a result, light with oscillating polarization is scattered by the particle, which offers enhanced signal evaluation benefits. First, the frequency of the high-frequency component provides an improved differentiation of the signal within a continuous flow. Second, the low-frequency component of the oscillating signal can be exploited to determine the optical refractive index. Finally, the inherent complexity of these oscillating signals offers a unique opportunity for the precise categorization of particles using machine learning.

We present here a new instrument, to our knowledge, in combination with a machine learning approach to achieve a more cost-effective and compact measurement instrument for particle characterization in a flow based on the established measurement technique known as the time-shift-time-of-flight (TSTOF) technique. A commercial device based on TSTOF was introduced and has since been recognized under the brand name SpraySpy. In this study, we propose a machine learning model capable of using only a single signal in this device to determine the same information about particles such as particle size and particle velocity, traditionally obtained from the classical measurement device based on the TSTOF technique, where four signals have been used. To achieve this, we train a machine learning model using the four signals, but connect only a single signal to the model in the final step. The initial experimental results have been conducted, and preliminary calculations demonstrate high potential for this method. By applying this method, one light source and three detectors, along with the corresponding electronics and optics, can be eliminated from a TSTOF measurement instrument. This not only reduces hardware costs but also enables the production of a smaller measurement probe and the use of a single signal acquisition system without the need for synchronization.

In this study, we present a basic idea for a robust optical measurement method for the practical monitoring of slurry sprays with high mass concentrations, as typically used in combustion processes. The proposed approach is based on analyzing the transparency properties of individual dynamic droplets within the slurry spray. These so-called dynamic slurry droplets consist of a base fluid and suspended solid particles. Our method enables the determination of the mass concentration of solid particles and provides an estimate of their average size. The resolution of this technique allows for the detection of errors in the slurry-mixing process and even in the milling process of the solid particles prior to mixing. To atomize slurries with high mass concentration, an experimental setup with a gas-assisted coaxial nozzle was employed. Slurries with three different mass concentrations and three particle size distributions were tested, resulting in five combinations. The theoretical expectations and experimental results show a strong correlation, highlighting the potential of the proposed method for slurry-spray monitoring.

The TSTOF stands for the time-shift time-of-flight measurement method, which is used to characterize individual dynamic particles—such as droplets in droplet clouds or particles in flows—in various industrial applications. Over the past 15 years, this method has become increasingly important and is valued in particular for its ease of use and compact design. So far, however, there is no technical report that deals exclusively and systematically with TSTOF technology. In the scientific literature, the method is usually only treated in connection with specific applications—theoretical or experimental—but is rarely presented in its entirety. Often the citations are incomplete or do not refer to scientifically validated sources. Current technical developments are usually not adequately considered, while non-scientific publications often take up technical aspects uncritically without referring to verifiable data. This report describes the technical foundations of the TSTOF measurement method, presents its historical development, and systematically compares it with established laser-based methods.