An Integrated Framework for Uncertainty Quantification in High Temperature Gas Cooled Reactors using the HCP Time-dependent Multiphysics code and Dakota toolkit

TL;DR

This paper develops an integrated framework coupling HCP with DAKOTA to perform uncertainty quantification and sensitivity analysis on high temperature gas cooled reactors, enhancing understanding of performance variability under uncertain conditions.

Contribution

It introduces a novel coupling of HCP with DAKOTA for comprehensive UQ workflows in HTGRs, enabling detailed analysis of uncertainty impacts on reactor performance.

Findings

Reactor design shows robustness to key input uncertainties.

Maximum fuel temperature remains within safety limits despite uncertainties.

The framework facilitates future expansion to include more physics and data uncertainties.

Abstract

The High Temperature Reactor Code Package provides sophisticated modeling and simulation capabilities for high temperature gas cooled reactors like the HTR-200 Modul. However, HCP currently lacks integrated methods for uncertainty quantification and sensitivity analysis. This work aims to couple HCP with the DAKOTA toolkit to enable UQ workflows for quantifying how different uncertainties impact HTGR system performance. DAKOTA offers state of the art sampling and analysis methods that will be linked to the HCP time-dependent multiphysics environment. Key input parameters related to manufacturing variability, boundary and initial conditions, and material properties will be defined as uncertain in this study. Both steady state and time-dependent multiphysics simulations will be analyzed to understand the relative importance of uncertainties across different physics phenomena. Output metrics of interest can include anything measured by the HCP code. Results show the HTR-200 Modul design's robustness to input uncertainties related to inlet gas temperature, U-235 enrichment, graphite density, inlet mass flow rate, and reactor power. A pressurized loss of forced cooling transient was simulated against uncertainty in the inlet gas temperature. Results show the maximum fuel temperature is within design limits with a relatively big safety margin even when considering uncertainty in the boundary conditions of the reactor. Future work will expand the analysis to more HCP physics modules and nuclear data uncertainties. By leveraging UQ and sensitivity analysis with the linked HCP/DAKOTA environment, this work will provide valuable new insights into the expected performance variability of HTR systems during normal and off-normal conditions. The framework developed here will aid uncertainty management activities by gas-cooled reactor developers, researchers, and regulators.