Presentation by Walter Schäfer on “Refractive Index Determination of Dynamic Droplets in a Flow by Analyzing Light Scattering Signals with a Machine Learning Approach” at the 11th International Symposium on Turbulence, Heat and Mass Transfer (THMT-25), held in Tokyo, Japan, on July 27, 2025.
Abstract
In this study, we present a new method for determining the refractive index of dynamic droplets in a spray using a machine learning approach, where the four light scattering signals from individual droplets were analyzed. These light scattering signals were generated by a Time-Shift-Time-of-Flight (TSTOF) system, known by some brand name SpraySpy. The spray was produced using a flat fan nozzle, and to vary the refractive index, a water-glycerin mixture with different mixing ratios was used. First, a machine learning model was developed to analyze the four light scattering signals. In addition, we explored whether the number of signals could be reduced while maintaining calculation precision.
W. Schaefer, “Refractive index determination of dynamic droplets in a flow by analyzing light scattering signals with a machine learning approach,” in Proceeding of THMT-25 Turbulence, Heat and Mass Transfer 11, 21-25 July 2025, Tokyo, Japan, Connecticut: Begellhouse, 2025, p. 8. doi: 10.1615/THMT-25.10.