Optimizing droplet coalescence dynamics in microchannels: A comprehensive study using response surface methodology...

Area of Science:

• Fluid dynamics
• Microfluidics
• Computational physics

Background:

• Droplet coalescence in microchannels is complex, affected by size, velocity, surface tension, and spacing.
• Understanding these dynamics is crucial for optimizing microfluidic applications.

Purpose of the Study:

• Investigate control parameters impacting droplet coalescence dynamics in a sudden expansion microchannel.
• Optimize droplet coalescence using Response Surface Methodology (RSM) and machine learning.

Main Methods:

• Employed the boundary element method to solve the Brinkman integral equation.
• Integrated Response Surface Methodology (RSM) with machine learning algorithms.
• Validated accuracy using Regression Coefficient and Mean Absolute Error metrics.

Main Results:

• Identified non-dimensional initial distance (D), viscosity ratio, Capillary number (Ca), and width (w) as key parameters.
• Found A_d and D most influential on final droplet-droplet spacing (DD); viscosity had minimal impact.
• Viscosity and channel width most influenced droplet velocity; initial distance and Ca had least influence.

Conclusions:

• Computational techniques effectively enhance experimental efficiency in microfluidic studies.
• The study provides valuable insights into droplet coalescence and a framework for optimizing microfluidic systems.
• Specific machine learning algorithms demonstrated superior prediction capabilities for droplet dynamics.