Uncertainty quantification and sensitivity analysis of a nuclear thermal propulsion reactor startup sequence

Journal Article · Wed Oct 08 00:00:00 EDT 2025 · Frontiers in Nuclear Engineering

DOI:https://doi.org/10.3389/fnuen.2025.1628866· OSTI ID:3027604

Harter, Jackson R ^[1]; DeHart, Mark D ^[2]

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1. Idaho National Laboratory
2. Abilene Christian University

The research presented in this article describes progress in applying stochastic methods, uncertainty quantification, parametric studies, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated via the radiation transport code \emph{Griffin} to simulate neutronics. Our goal is to develop a reduced-order (surrogate) model that can be rapidly sampled with perturbations to multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums (CDs), which is itself controlled by a hybrid proportional-integral-derivative (PID) controller actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time [$$\Lambda$$] and effective delayed neutron fraction [$$\beta_\text{eff}$$] from a transient \emph{Griffin} simulation executed via \emph{Griffin}'s improved quasi-static solver to provide the kinetic parameters) as inputs to functions that control the CD rotation angle. By investigating numerous stochastic approaches, we developed a dual-purpose surrogate model of the NTP system, using polynomial regression in the Multiphysics Object Oriented Simulation Environment (MOOSE) Stochastic Tools Module (STM). The trained model can be rapidly sampled while simultaneously perturbing various input parameters, such as coefficients on the PID control or temperature (directly affecting the neutron cross section). The surrogate model delivers accurate (within 5\%) results at speeds orders of magnitude faster (minutes, not days of computational time) than the base model. Once the surrogate model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs as well as the effects of these inputs on the model output. For example, coefficients used in the PID control system may vary due to some type of physical interference, or uncertainty may exist in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters, and operational boundaries can be determined. The goal of this work is to support development of an advanced control system for operating CDs in a functioning NTP system. This work is a scoping study of the MOOSE STM.

• Research Organization: Idaho National Laboratory (INL), Idaho Falls, ID (United States)
• Sponsoring Organization: USDOE Office of Nuclear Energy (NE)
• Grant/Contract Number: AC07-05ID14517
• OSTI ID: 3027604
• Report Number(s): INL/JOU-25-88527
• Journal Information: Frontiers in Nuclear Engineering, Journal Name: Frontiers in Nuclear Engineering Journal Issue: 1 Vol. 4
• Country of Publication: United States
• Language: English

Related Subjects

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sensitivity analysis
uncertainty quantification