Harnessing Machine Learning for Analyzing Droplet Evaporation in Phase-Change Heat Transfer

Document Type

Article

Publication Date

1-1-2026

Abstract

Use of machine learning (ML) is in its early stages within the domain of phase-change heat and mass transfers, yet ML holds transformative potential for both research and practical applications. Droplet evaporation, a fundamental phenomenon in phase-change processes, is pivotal in diverse industries such as inkjet printing, microelectronics cooling, and chemical processing. Conventional experimental and numerical methods, while informative, often fall short in fully capturing the complex multiscale, multivariate dependencies, and nonlinear interactions that govern evaporation dynamics, including vapor shielding and selective evaporation. ML offers a robust set of techniques for advanced highdimensional data analysis, predictive modeling, and real-time monitoring, effectively addressing these limitations. This review explores the integration of ML into droplet evaporation studies, highlighting key challenges and opportunities in optimizing experimental setups, enhancing spatiotemporal analysis, improving model accuracy, and leveraging image processing to interpret complex, nonlinear features embedded in visual data. By concentrating on droplet evaporation, this work aims to establish a foundation for broader advancements in phase-change heat transfer. Furthermore, the methodologies and insights presented herein are expected to extend naturally to related areas such as condensation and other evaporation-driven processes, fostering new directions in thermal management and fluid dynamics research.

Publication Title

Journal of Flow Visualization and Image Processing

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