Prediction of MIT Pressurizer Data Using RELAP5 and TRAC-M

Abstract

Tests simulating Pressurized Water Reactor pressurizers under inflow and outflow conditions have been performed at MIT. Prediction of pressurizer pressure requires accurate models of wall heat transfer as well as interfacial liquid-steam heat transfer. The US NRC has two computer programs used for predicting thermal hydraulic behavior in reactors; RELAP5 and TRAC-M. TRAC-M is the Consolidated Thermal-hydraulics Code developed by combining models from TRAC-B and RELAP5 into a modernized version of TRAC-P. The component models from RELAP5 and TRAC-B have been ported to TRAC-M but not the constitutive models. A suite of assessment cases are being developed to guide the constitutive model improvement process. Assessment against data will determine which constitutive relations need to be ported to TRAC-M. This paper compares the RELAP5 and TRAC-M codes against MIT pressurizer data. As water is injected into the bottom of the pressurizer the steam pressure in the top of the pressurizer rises. The pressure increase rate is controlled by wall and interface condensation rates. Both codes predict this complex compression process reasonably well. The effect of time step size and code options are explored in this paper. The benefits of using two codes to analyze thermal-hydraulic processes are evident from the results.

Investigation on RELAP5-3D© capability to predict thermal stratification in liquid metal pool-type system and comparison with experimental data

“…This approach allows a good evaluation of the thermal stratification when a relatively coarse nodalization is applicable, such as the pressurizer modelling case. Based on the experimental campaign carried out by Saedi (1983) and Kim (1984), simulation activities performed by Shumway et al (2002) and Bayless et al (2015) assessed the capability of RELAP5 to reproduce thermal stratification phenomenon in the pressurizer, modelling the component with a single vertical pipe and adopting the stratification model. The computational activity has been repeated by Ahn et al (2018) investigating the influence of the number of nodes on the evaluation of the thermal stratification.…”