Liquid breakup and local droplet distribution in flat-fan sprays

Abstract

Flat-fan sprays are widely used in agricultural spraying and surface-coating applications, where droplet size and velocity distributions strongly influence spray uniformity, deposition efficiency, and drift. These properties are governed by breakup mechanisms within the liquid sheet and emerging ligaments, which leave measurable trends in the downstream droplet distributions. The aim of this study is to investigate these breakup mechanisms in a near two-dimensional, non-evaporating flat-fan spray by characterising local droplet size and velocity distributions at high spatial and temporal resolution. This work also provides the experimental foundation needed to validate mechanistic spray models such as the generalised Fully Lagrangian Approach (gFLA).
To achieve this, a dedicated experimental methodology based on high-speed videography (HSV) was developed, in conjunction with particle image velocimetry algorithm, to analyse spray behaviour at different flow rates under quasi-steady, quiescent conditions and to reconstruct local droplet statistics in the dispersed region. HSV results were compared with point-wise phase Doppler anemometry (PDA), highlighting the complementary strengths of the two techniques. While both methods captured consistent trends, discrepancies in measured velocities and droplet sizes reflected differences inherent to each approach. HSV captured a broader range of droplet sizes, whereas PDA measured velocities with higher temporal resolution and greater dynamic range.
HSV data were then used to define initial conditions and validate gFLA simulations. Kernel regression was applied to reconstruct droplet field quantities on an Eulerian grid. The gFLA reproduced key features of droplet redistribution, including clustering and representative size and velocity distributions, although differences in number density and velocity were observed due to zero gas velocity field and the absence of secondary breakup. Overall, this work provides the first detailed experimental dataset suitable for validating the gFLA and demonstrates its potential as a computationally efficient tool for modelling polydisperse flat-fan sprays.

Date of Award	Dec 2025
Original language	English
Awarding Institution	University of Brighton
Supervisor	Oyuna Rybdylova (Supervisor), Steven Begg (Supervisor) & Guillaume De Sercey (Supervisor)