An experimental research of the influence on critical heat flux of a rod bundle under certain inlet temperatures

Abstract

Critical heat flux (CHF) is one of the most concerned thermal hydraulic phenomena in reactor safety analysis. It involves complex two-phase flow heat transfer mechanism, and has not been fully understood, so the prediction of critical heat flux mainly depends on CHF correlations obtained under limited experimental conditions. At present, CHF correlations are generally developed with pressure, mass flux and quality as key independent variables. And correspondingly, the test matrix of a CHF test consists of the above parameters. However, it is impossible to perform CHF tests accurately according to the predetermined quality. In CIAE, a CHF experimental research of a 5 × 5 uniformly heated rod bundle has been carried out. In the experiment, the inlet temperature of the test section was directly taken as a parameter in the test matrix. The CHF data were achieved by stepwise increasing the heating power. The test conditions covered the pressure of 2.8–15.5 MPa, the mass flux of 845–3533 kg/(m2·s), and the inlet temperature of 100°C–300°C. The test data have been analyzed to obtain the thermal-hydraulic parameter influences on CHF by taking the inlet temperature as a variable. The results indicated that, within the test condition range, under the same inlet temperatures, CHF was hardly affected by pressure, and linearly increased with the increasing mass flux. With the increase of inlet temperature, the enhancement of CHF with the increasing mass flux gradually weakens. And CHF was linearly decreased with the increasing inlet temperature under the same mass flux. By contrast, the parameter influences on CHF were more complex by taking the local quality as a variable. According to the research, it can be concluded that, it has an advantage of simplifying the CHF correlation form to take the inlet temperature of the test section as a variable parameter. The research can provide new ideas for CHF experiment, data analysis and correlation development.