Submit

Login Login

Register Register

Home / Journals / FDMP / Online First / doi:10.32604/fdmp.2026.079861
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

Open Access

REVIEW

A Review of Artificial Intelligence in Boiling Heat Transfer: Predictive Modeling, Dynamic Characterization, and Methodological Advances

Wei-Chen Tang, Xin Chen, Fei Dong*
Jiangsu University, School of Automotive and Traffic Engineering, 301 Xuefu Road, Zhenjiang, China
* Corresponding Author: Fei Dong. Email: email
(This article belongs to the Special Issue: High-Order Computing and Deep Reinforcement Learning for Multiphase Interfacial Flows)

Fluid Dynamics & Materials Processing https://doi.org/10.32604/fdmp.2026.079861

Received 29 January 2026; Accepted 26 March 2026; Published online 22 April 2026

Abstract

Boiling heat transfer remains a cornerstone of efficient thermal management, with far-reaching implications for energy systems and industrial processes. Advances in this field not only deepen fundamental scientific understanding but also enable transformative improvements in energy efficiency, equipment performance, and operational safety. Contemporary research in this area focuses on accurate parameter prediction, intelligent image analysis, and quantitative characterization of bubble dynamics, collectively advancing both mechanistic insight and engineering optimization. In this context, artificial intelligence (AI), encompassing machine learning and deep learning techniques, has emerged as a powerful paradigm, offering significant advantages in predictive accuracy, data-driven analysis, and experimental efficiency. This paper provides a systematic review of AI applications in boiling heat transfer research. First, conventional approaches are critically assessed, highlighting the growing relevance and advantages of AI-based methodologies. Next, key machine learning algorithms are introduced and classified according to their roles and capabilities. Subsequently, recent advances in AI-driven prediction of heat transfer parameters, automated analysis of bubble dynamics, and the development of novel research methodologies are comprehensively examined. Finally, current achievements are synthesized, and future research directions are outlined, with particular emphasis on the integration of AI into real-time control and edge-computing frameworks for industrial thermal management.

Keywords

Artificial intelligence; boiling heat transfer; bubble dynamics

  • 78

    View

  • 15

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link