Sustained and Enhanced Nucleate Boiling Using Hierarchical Architectures at Large Superheats

Area of Science:

• Heat Transfer
• Fluid Dynamics
• Materials Science

Background:

• Droplet boiling is crucial in industrial processes but limited by the Leidenfrost effect, which reduces cooling performance.
• The Leidenfrost point (LP) marks the transition to vapor film formation, hindering efficient heat transfer.

Purpose of the Study:

• To enhance droplet boiling performance by elevating the Leidenfrost point (LP).
• To investigate the impact of a nano-micro hierarchical triple-passage architecture on boiling dynamics and heat transfer.

Main Methods:

• Fabrication of a nano-micro hierarchical triple-passage architecture with a high aspect ratio.
• Experimental analysis of droplet boiling behavior and heat transfer.
• Theoretical modeling using a multi-force competition model.
• Development of a physics-informed deep neural network for boiling prediction.

Main Results:

• The novel architecture elevated the LP to 273°C, a 130°C increase compared to a copper surface (145°C).
• Enhanced vapor and liquid spreading dynamics were observed, boosting heat transfer.
• Lower droplet impact velocities were found to delay the LP by manipulating the impact pattern.

Conclusions:

• The hierarchical architecture effectively suppresses the Leidenfrost effect and enhances heat transfer.
• The study challenges conventional understanding of droplet impact dynamics and LP.
• The developed models provide accurate prediction of droplet boiling behavior, aiding in device design.