Walter Schäfer, E. Goldenberg, M. Al-Naggar and W. Schaufler
Abstract
In this work, we investigate whether light-scattering signals recorded by a Time-Shift–Time-of-Flight (TSTOF) instrument contain sufficient information to recover refractive index and mass concentration and demonstrate how artificial intelligence (AI) can exploit this information. Accurate determination of the refractive index and mass concentration of individual dynamic droplets is essential for the characterization of transparent and suspension droplets in industrial spray processes. Conventional optical diagnostics cannot provide these quantities for individual droplets in motion, and current TSTOF-based instruments are generally limited to size, velocity, and opacity, especially under dense spray conditions. Controlled measurements were performed on suspension droplets with concentrations from 0\% to 100\% and on transparent droplets from water–glycerin mixtures with refractive indices between 1.30 and 1.40. For each droplet, light-scattering signals were recorded as it traversed an elliptical Gaussian beam. AI models were trained on different combinations of these signals. The results demonstrate that AI-enhanced TSTOF diagnostics enable real-time, composition-sensitive characterization of dynamic droplets and significantly extend the capabilities of current optical spray measurement systems.