Predicting void fraction for boiling steam-water two-phase flow in narrow rectangular channels with enhanced modeling of net vapor generation and drift-flux parameters
Abstract
The investigation of boiling two-phase flow in vertical narrow rectangular channels is important due to their widespread use in advanced thermal systems, such as compact heat exchangers and plate-type fuel reactors, where accurate void fraction prediction is critical for thermal-hydraulic performance and safety. Although the drift-flux model is a fundamental prediction tool, its reliable application in such confined geometries requires accurate determination of the thermodynamic equilibrium quality at the net vapor generation point ( x e,NVG ), and accurate correlations for the distribution parameter and drift velocity. However, most existing correlations were developed from conventional geometries (e.g., circular tubes or rod bundles) and lack rigorous validation against dedicated data from narrow rectangular channels. To bridge this gap, this work assesses 17 existing x e,NVG correlations and 20 drift-flux correlations using comprehensive databases for boiling steam-water two-phase flow in vertical narrow rectangular channels. The evaluation reveals that, although a few correlations perform better, none consistently achieve acceptable accuracy across the dataset. Consequently, new dedicated correlations for x e,NVG and drift-flux parameters are developed based on physical considerations to address the unique geometric and thermal-hydraulic characteristics of narrow rectangular channels. The proposed x e,NVG correlation yields a fitting residual of 0.294, while five-fold cross-validation provides a more conservative generalization estimate of 0.355 for 120 data points. Combined with the new drift-flux correlation, the framework achieves a fitting residual of 0.182 for 347 data points (bubbly to annular flow), with a cross-validated MRE of 0.318.
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Authors: Xu Han, Shixun He, Hongyu Yang, Hanyu Wang, Wenhai Qu, Meng Wang, Jianjun Wang
Institutions: Shanghai Jiao Tong University, Harbin Engineering University