Data inversion algorithms for droplet characterization based on simulated rainbows

In characterizing droplets, the rainbow reveals information about the size of individual drops, their re-fractive index, temperature, and in some instances composition. Previous experience in exploiting rain-bow scattering for this purpose has led to classical rainbow refractometry, but also to the global rainbow technique. To improve on these existing techniques, a highly focused Gaussian illumination beam of-fers some advantages for achieving higher spatial resolution in dense spray situations. This is explored in the present study, using simulations of rainbow scattering from a spherical droplet in the primary and secondary rainbow regions. These simulations are performed using the generalized Lorenz-Mie the-ory (GLMT), allowing for a focused Gaussian beam illumination. For droplet characterization, three inver-sion algorithms are investigated, designated as the peak-trough method, inflection-inflection method and trough-trough method. The effect of the incident position of the beam on the droplet characterization is considered, as are effects of white noise superimposed on the rainbow. For a spherical water droplet in the size range 50-200 mu m, an accuracy of the measured relative refractive index up to 4 decimal places and a relative error in size smaller than 5% can be achieved in most cases. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Rainbow scattering can provide information about the size, refractive index, temperature, and composition of droplets. Using a highly focused Gaussian illumination beam can improve the spatial resolution in dense spray situations. This study investigates rainbow scattering from spherical droplets and explores different inversion algorithms and the effect of beam incident position on droplet characterization. For water droplets in the size range of 50-200 μm, high accuracy in measuring the relative refractive index and small relative error in size can be achieved.