CFD Modeling Development for DNB Prediction of Rod Bundle with Mixing Vanes Under PWR Conditions

摘要

Critical heat flux (CHF) is a primary parameter for nuclear fuel design and plant operation safety. CHF values are normally obtained from fuel bundle integral departure from nucleate boiling (DNB) or dryout experiments. These experiments are expensive, and detailed measurements (bubble dynamics, void fraction distribution, etc.) are difficult to obtain, particularly under typical pressurized water reactor (PWR) conditions of high pressure and temperature. Therefore, it is highly desirable that computational tools such as computational fluid dynamics (CFD) provide detailed flow and heat transfer information that will efficiently facilitate design improvements of PWR fuel designs.For the CFD studies described in this paper, an Eulerian-Eulerian two-phase modeling approach was adopted to predict DNB in a fuel assembly with mixing vane grids. Subcooled flow boiling was simulated using heat flux partition modeling and phase interactions. Direct addition of heat to the vapor was activated when the local vapor volume fraction reached a specified critical value. Emphases were placed on bubble departure diameter, phase interactions, and pressure drop for two-phase modeling development. Simulations were conducted in steady state. Solution convergence was closely monitored for physical variables in terms of local and global scales. A multi-indicator approach was used to judge DNB occurrence, and a new integrated DNB indicator is proposed.For validation, this CFD-based DNB modeling methodology was applied to two 5 x 5 rod bundle tests equipped with mixing vane grids and uniform axial power shape. The tests were performed under PWR conditions (16.5 MPa) and produced an exit quality of - 4% and 11%. The CFD results show the validity of the new DNB indicator and the improved reliability with the multi-indicator approach. Correctly predicted DNB occurrence locations show the promise of the current modeling approach. Utilization of measured pressure drop and fluid temperature data may permit some bottom-up validations, and this effort may prompt further improvements for experimental measurements, particularly under high-pressure conditions.