Development of a drift-flux model based core thermal-hydraulics code for efficient high-fidelity multiphysics calculation

Abstract

The methods and performance of a pin-level nuclear reactor core thermal-hydraulics (T/H) code ESCOT employing the drift-flux model are presented. This code aims at providing an accurate yet fast core thermal-hydraulics solution capability to high-fidelity multiphysics core analysis systems targeting massively parallel computing platforms. The four equation drift-flux model is adopted for two-phase calculations, and numerical solutions are obtained by applying the Finite Volume Method (FVM) and the Semi-Implicit Method for Pressure-Linked Equation (SIMPLE)-like algorithm in a staggered grid system. Constitutive models involving turbulent mixing, pressure drop, and vapor generation are employed to simulate key phenomena in subchannel-scale analyses. ESCOT is parallelized by a domain decomposition scheme that involves both radial and axial decomposition to enable highly parallelized execution. The ESCOT solutions are validated through the applications to various experiments which include CNEN 4 × 4, Weiss et al. two assemblies, PNNL 2 × 6, RPI 2 × 2 air-water, and PSBT covering single/two-phase and unheated/heated conditions. The parameters of interest for validation include various flow characteristics such as turbulent mixing, spacer grid pressure drop, cross-flow, reverse flow, buoyancy effect, void drift, and bubble generation. For all the validation tests, ESCOT shows good agreements with measured data in the extent comparable to those of other subchannel-scale codes: COBRA-TF, MATRA and/or CUPID. The execution performance is examined with a mini-sized whole core consisting of 89 fuel assemblies and for an OPR1000 core. It turns out that it is about 1.5 times faster than a subchannel code based on the two-fluid three field model and the axial domain decomposition scheme works as well as the radial one yielding a steady-state solution for the OPR1000 core within 30 s with 104 processors.