Experimental and numerical modeling of transition matrix from momentum to buoyancy-driven flow in a pressurized water reactor

The influence of density differences on the mixing of the primary loop inventory and the Emergency Core Cooling (ECC) water in the cold leg and downcomer of a Pressurised Water Reactor (PWR) was analyzed at the ROssendorf COolant Mixing (ROCOM) test facility. This paper presents a matrix of ROCOM experiments in which water with the same or higher density was injected into a cold leg of the reactor model with already established natural circulation conditions at different low mass flow rates. Wire-mesh sensors measuring the concentration of a tracer in the injected water were installed in the cold leg, upper and lower part of the downcomer. A transition matrix from momentum to buoyancy-driven flow experiments was selected for validation of the CFD software ANSYS CFX. A mesh with 4 million control volumes was used for the calculations. The turbulence models usually applied in such cases assume that turbulence is isotropic, whilst buoyancy actually induces anisotropy. Thus, in this paper, higher order turbulence models have been developed which take into account for that anisotropy. Buoyancy generated source and dissipation terms were proposed and introduced into the balance equations for the turbulent kinetic energy. The results of the experiments and of the numerical calculations show that mixing strongly depends on buoyancy effects: At higher mass flow rates (close to nominal conditions) the injected slug propagates in the circumferential direction around the core barrel. Buoyancy effects reduce this circumferential propagation with lower mass flow rates and/or higher density differences. The ECC water falls in an almost vertical path and reaches the lower downcomer sensor directly below the inlet nozzle. Therefore, density effects play an important role during natural convection with ECC injection in PWR and should be also considered in Pressurized Thermal Shock (PTS) scenarios. ANSYS CFX was able to predict the observed flow patterns and mixing phenomena quite well.