Design Automation and Optimization of Micro Cross-Junctions for Droplet Generation Using CFD and Machine Learning Approaches

This work numerically studies the water-in-oil (W/O) droplet formation inside a flow focusing on the micro junction formed by rectangular channels with dimensions of 390 × 190 μm2 using OpenFoam. An automatic algorithm was developed to assess the effect of key parameters such as water viscosity, restriction ratio and water mass flow rate ratio on the droplet size. A total of 96 simulations, with different parameter combinations, were conducted to train a Machine Learning (ML) algorithm capable of predicting the droplet dimensions based on the key parameters mentioned. The ML algorithm was also compared to a Newtonian-based optimization method, where the geometry is iteratively adjusted to produce droplets of a fixed size. Results reveal that both methods appear valid in the prediction of droplet dimensions.