Mechanistic CHF Models Under High-Pressure Flow Boiling Conditions: Comprehensive Literature Review and a Comparative Assessment of Bubble Crowding and Sublayer Dryout Models

Critical heat flux (CHF) is a limiting thermal phenomenon in high-pressure boiling systems, and mechanistic models are employed to predict CHF by capturing the underlying physical triggers of the boiling crisis. In this study, two mechanistic CHF models, the bubble crowding model (BCM) and the vapor sublayer dryout model (SDM) are evaluated against an extensive experimental CHF database at pressures from 10 to 20 MPa. A comprehensive literature review is first presented, covering historical and recent developments in mechanistic CHF modeling and validation. The physical bases and governing equations of the BCM and SDM are summarized, and a methodology is described for applying these models to predict CHF with appropriate input parameters and closure correlations. The experimental CHF data (264 data points in a uniformly heated 8-mm tube) are described in detail, including the test facility, procedures, and CHF detection criteria. Model predictions are then compared quantitatively. Overall, the BCM achieves a mean absolute prediction error of ~14.5% and captures ~74.6% of data within ±20% error, whereas the SDM shows ~24.8% mean error with ~69% within ±20%. After excluding two extreme outliers at near critical conditions, the SDM performance improves to ~17.6% mean error (70% within ±20%). The BCM outperforms the SDM on average, especially at low vapor qualities [departure from nucleate boiling (DNB) regime], while both models degrade in accuracy as conditions approach annular flow (quality > 0.3). In the highest quality bin (up to x ~ 0.43), the SDM markedly overpredicts CHF for certain outlying cases, underscoring the limits of its mechanistic assumptions outside its intended regime. While BCM appears more robust under high-pressure DNB conditions, neither model alone can universally predict CHF across all flow regimes. For both models, the accuracy deteriorates significantly as the flow nears annular conditions, highlighting the need for regime-specific mechanistic modeling (or hybrid approaches) to cover all regimes.