Autonomous Optimization of Fluid Systems at Varying Length Scales

Autonomous optimization is a process by which hardware conditions are discovered that generate an optimized experimental product without the guidance of a domain expert. We design an autonomous optimization framework to discover the experimental conditions within fluid systems that generate discrete and uniform droplet patterns. Generating discrete and uniform droplets requires high-precision control over the experimental conditions of a fluid system. Fluid stream instabilities, such as Rayleigh-Plateau instability and capillary instability, drive the separation of a flow into individual droplets. However, because this phenomenon leverages an instability, by nature the hardware must be precisely tuned to achieve uniform, repeatable droplets. Typically this requires a domain expert in the loop and constant re-tuning depending on the hardware configuration and liquid precursor selection. Herein, we propose a computer vision-driven Bayesian optimization framework to discover the precise hardware conditions that generate uniform, reproducible droplets with the desired features, leveraging flow instability without a domain expert in the loop. This framework is validated on two fluid systems, at the micrometer and millimeter length scales, using microfluidic and inkjet systems, respectively, indicating the application breadth of this approach.