Planar Interferometric Tracking of droplets in evaporating conditions

An effective Lagrangian Planar Interferometric Tracking (PIT) processor is proposed to track the size and path of multiple droplets, with spray droplet diameters (20-150 mu m) and volumetric concentrations (approximate to 300 drops/cm(3)) consistent with industrial applications, produced by an ultrasonic atomiser in evaporating conditions. A test facility was developed where liquid droplets are exposed to a temperature gradient in a co-axial air flow, where the outer stream is preheated to the desired temperature (288-550 K). The PIT method builds on a TSI Global Sizing Velocimtery measurement technique and allows to detect, size and follow the path of droplets which were otherwise discarded or mis-analysed by the commercial software. The methodology was first tested under non-evaporating conditions, and multiple sources of errors, some common to most planar interferometric techniques, were identified and their order of magnitude and impact on final droplet measurement assessed. The main source of error is related to the out-of-plane motion of the droplets and the time they spend in the measurement volume. For non-evaporating conditions, measured data can be processed to filter out this source of error. In evaporating conditions, a novel method for assessing the impact of measurement error with respect to droplet evaporation and measurement timescales is defined. The PIT method allowed tracking of individual methanol droplets entrained within an airflow heated to 495 K and determining their size reduction under evaporating conditions. Measured droplet evaporation rates were then compared against those predicted by an iterative evaporation model, and a very good agreement was found between the modelled and measured estimates.

Automated summary

This paper proposes an effective Lagrangian Planar Interferometric Tracking (PIT) processor to track the size and path of multiple droplets. It analyzes the sources of errors in droplet measurement and assesses their impact on the final results. The feasibility of the method is validated through experiments and comparison with model predictions.