Review: Artificial Intelligence for Liquid-Vapor Phase-Change Heat Transfer

TL;DR

This review discusses how artificial intelligence and machine learning are transforming research on phase-change heat transfer, enabling new insights, data analysis, and physical modeling in boiling and condensation phenomena.

Contribution

It provides a comprehensive overview of AI applications in thermal energy science, highlighting recent innovations, challenges, and future perspectives for physics-centered machine learning in phase-change heat transfer.

Findings

AI enables new feature extraction for phase-change systems.

AI techniques facilitate meta-analysis and data stream analysis.

Future directions include physics-informed machine learning and sustainable cyberinfrastructures.

Abstract

Artificial intelligence (AI) is shifting the paradigm of two-phase heat transfer research. Recent innovations in AI and machine learning uniquely offer the potential for collecting new types of physically meaningful features that have not been addressed in the past, for making their insights available to other domains, and for solving for physical quantities based on first principles for phase-change thermofluidic systems. This review outlines core ideas of current AI technologies connected to thermal energy science to illustrate how they can be used to push the limit of our knowledge boundaries about boiling and condensation phenomena. AI technologies for meta-analysis, data extraction, and data stream analysis are described with their potential challenges, opportunities, and alternative approaches. Finally, we offer outlooks and perspectives regarding physics-centered machine learning, sustainable cyberinfrastructures, and multidisciplinary efforts that will help foster the growing trend of AI for phase-change heat and mass transfer.