Scalable modular dynamic molten salt reactor system model with decay heat

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Abstract

Nodal dynamic models allow rapid reduced-order simulations of complex systems. This paper presents a publicly available system dynamic model of a molten salt reactor system with a topology of the Molten Salt Reactor Experiment (MSRE), in MATLAB/Simulink, building upon a model previously published and validated with MSRE data. It adds scaling of nominal power, dynamic representation of decay heat generation, a generic decay heat removal system, improved documentation, modularity, and test cases. One hour of system transient runs in less than 10 min on a laptop. This model is intended for first order engineering calculations, developing insights into system transient behavior, equipment sizing, parameter sensitivity studies. The model contains an easy to use toolkit which can be customized and expanded to describe any molten salt reactor system. Scenarios involving off-normal transients are presented. Relation between shutdown reactivity insertions, decay heat removal, core recriticality, and safe shutdown conditions are discussed.

Introduction

This paper presents a Scalable Modular Dynamic model of a Molten Salt Reactor (SMD-MSR), an enhanced nonlinear system dynamic simulation methodology and publicly available implementation of a simple single fluid molten salt reactor system developed in MATLAB Simulink. The model is developed from a previous Molten Salt Reactor Experiment (MSRE) dynamic model (Singh et al., 2018), which was in turn inspired by the original dynamic nodal analysis developed by ORNL in the 1960s (Ball and Kerlin, 1965, Kerlin et al., 1971). Number of nodes are set to satisfy the requirements of the modeling approach (Ball, 1964). Other publicly available dynamic models using analogous methodology describe a two-fluid Molten Salt Breeder Reactor (Singh et al., 2018) and a Molten Salt Demonstration Reactor (Singh et al., 2020).

Key new features are a novel dynamic decay heat production module, and a decay heat removal system model flexible enough to establish appropriate functional requirements for any specific system that deals with decay heat removal. The model has additional functionality by utilizing a modular architecture and full model scaling to any given nominal power.

This model can be used to simulate fundamental dynamic characteristics of an MSR. Specifically this is useful for characterization of system dynamics during normal and off-normal transients, investigating sensitivity of parameter uncertainty on transient progression, equipment sizing, exploring foundation regulatory standards, and planning MSR operational procedures. The SMD-MSR model comes with some limitations in addition to its 1D nodal nature: it assumes homogeneous MSRE fuel salt, and linear temperature reactivity feedbacks.

There are high fidelity simulation tools and approaches available for MSR modeling with dynamic decay heat. These high fidelity approaches require a significant compute power and simulation time to model transients, and are specific to a design concept (Gentry et al., 2017, Heuer et al., 2012). Additionally, these tools are not openly available. The model presented herein is easily tailored to many design concepts, is openly available, and runs fast. One hour of a system transient executes within 10 min on a laptop.

The SMD-MSR model is available at Github (Github Repository for SDM-MSR, 2020). The repository contains the Simulink model and commented input parameter files to execute simulations presented in this paper.

Section snippets

Scalable modular dynamic MSR model

SMD-MSR model is a dynamic MATLAB/Simulink nodal model for a thermal spectrum MSR system with the MSRE topology. This model builds upon previously published MSRE dynamic model (Singh et al., 2018). This original model was built specifically to simulate the MSRE system. It is composed of three major blocks: the nuclear core, the primary heat exchanger (PHX), and the secondary heat exchanger (SHX), which was the MSRE salt to air radiator. The model presented herein is more generic and separates

Decay heat in MSRs

Understanding decay heat production and removal is imperative for understanding reactor transients as well as accident scenarios. Decay heat is specially important for molten salt reactors to keep its fuel salt in a molten state for a period of time after shutdown. MSR temperature reactivity feedbacks are significant due to the large difference between their operational temperature and the liquidus temperature of the fuel salt, typically in the range of 200–300 K. Because of this, decay heat

Results

The presented model allows simulation of MSRs system’s dynamic behavior during a large variety of transients. This includes reactivity insertions, load following, loss of ultimate heat sink, pump trips for both primary and secondary loops, accidental opening of DHRS, cold slug insertions, etc. The user can use these simulations to develop and fine-tune functional requirements of each component. Previously published studies (Singh et al., 2018, Singh et al., 2020) demonstrated some of these

Conclusions

This paper presents a new and updated generic dynamic MSR system model developed in Simulink, which implements modular architecture, dynamic decay heat production, and a configurable decay heat removal system. This model can be used for investigating transient behavior of MSR systems, sensitivity of transient behavior to system parameter changes, equipment sizing, and developing operational procedures, for thermal spectrum molten salt reactor systems.

Post-shutdown recriticality is undesirable

CRediT authorship contribution statement

Visura Pathirana: Software, Validation, Methodology, Writing - original draft, Writing - review & editing. Ondrej Chvala: Conceptualization, Methodology, Supervision, Writing - original draft, Writing - review & editing. Alexander M. Wheeler: Software, Validation, Methodology, Writing - original draft, Writing - review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This research and development at the University of Tennessee is being funded by a grant from Oak Ridge National Laboratory (subcontract 4000159472). We would like to thank M. Scott Greenwood for his collaboration in MSR dynamic simulation research. Partial support for this work is from US DOE award DE-NE0008793. The authors are grateful for this generous support. The authors would also like to thank the reviews whose suggestions and comments greatly improved the manuscript.

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