This work explores the combination of direct numerical simulations (DNSs) and experimental approaches for studying technical emulsification processes. Although emulsions have long been used in a variety of industries and many important research papers have been published over the years, quantifying and predicting the dispersion of droplets in another liquid remains challenging because of the complex multiphase nature and microscopic droplet scales. This study focuses on water-in-gasoline emulsions, which have the potential to improve efficiency and reduce emissions in combustion-based power generation. Experimental data from two different emulsion injection systems are complemented with DNS to gain insight into emulsification and the resulting droplet size distribution. In situ shadow imaging is used to acquire the experimental droplet size distributions, whereas DNS is performed via the geometric volume of fluid (VoF) method with the open-source code PARIS. The results indicate consistent agreement between the experimental and simulation results. Additionally, a corresponding trend of increasing droplet size is observed as the volume fraction of the dispersed phase increases. Furthermore, a detailed analysis of various probability density functions for modeling droplet size distributions (DSDs) reveals that the gamma distribution is the most appropriate. Overall, this work demonstrates that DNS can be successfully combined with experiments to increase the understanding of emulsification processes.