Prediction of critical heat flux using different methods: A review from empirical correlations to the cutting-edge machine learning

Nucleate boiling effectively dissipates heat through phase change, where heat is absorbed during the transition from liquid to vapor. However, this heat dissipation is strongly limited by Critical Heat Flux (CHF). When CHF is reached, a small increase in heat flux can lead to a sudden temperature surge, potentially causing the heated surface to burn out. CHF has been extensively studied for almost 100 years, and numerous methods have been proposed to predict CHF under various working conditions. In this paper, we aim to comprehensively review the methods for predicting CHF, from initial models derived from experimental correlations to advanced numerical simulations and state-of-the-art machine learning approaches. We begin by introducing CHF models based on experimental data and discuss prediction methods that utilize CHF databases. Next, we examine wall boiling models developed through numerical simulations at different scales. Furthermore, we explore the potential of machine learning in CHF prediction, highlighting the advantages of this approach. By summarizing these studies, we aim to provide researchers with a comprehensive understanding of CHF prediction methods and offer effective strategies for accurate CHF prediction in the future. •The review summarizes current CHF prediction methods, including their advantages, disadvantages, and applicability.•It introduces CHF mechanism and corresponding models in pool and flow boiling, supported by experimental verification.•Detailed overview of wall boiling simulation from macro-scale to nano scale.•Introduction of innovative AI technology applications in CHF prediction.