Sensitivity analysis, surrogate modeling, and optimization of pebble-bed reactors considering normal and accident conditions

• Published in: Nuclear engineering and design 2024-11, Vol.428 (-), p.113466, Article 113466
• Main Authors: Prince, Zachary M., Balestra, Paolo, Ortensi, Javier, Schunert, Sebastian, Calvin, Olin, Hanophy, Joshua T., Mo, Kun, Strydom, Gerhard
• Format: Article
• Language: English
• Subjects: Depressurized loss of forced cooling; Equilibrium core; MATHEMATICS AND COMPUTING; MOOSE; Optimization; Pebble bed reactor; Sensitivity analysis; Surrogate modeling
• ISSN: 0029-5493
• DOI: 10.1016/j.nucengdes.2024.113466

This research provides a valuable tool that streamlines the reactor design optimization process while significantly increasing its accuracy. This study creates a robust framework by incorporating multiphysics modeling using the Comprehensive Reactor Analysis Bundle, or BlueCRAB, based on the Multiphysics Object-Oriented Simulation Environment (MOOSE). BlueCRAB is the United States Nuclear Regulatory Commission’s code suite for non-light water reactor analysis and includes the Griffin, Pronghorn, and Bison applications. This not only improves the efficiency of the optimization process but also enhances the reliability of the results. Such a tool is essential for advancing the state-of-the-art in pebble-bed reactor technology and is critical for achieving the goals of Generation IV reactors, which aim for safe, sustainable, and economically viable nuclear energy solutions. This work presents and applies this workflow on pebble-bed reactors while considering both normal and off-normal conditions. A representative gas-cooled pebble-bed reactor at equilibrium core conditions serves as the nominal design specification for normal operation and is based on previous research. The depressurized loss-of-forced-cooling accident is deployed for off-normal conditions in this work. After defining design-related parameters and quantities of interest regarding reactor safety and performance, this multiphysics model is sampled using the MOOSE stochastic tools module. The result is a comprehensive dataset of configurations, enabling sensitivity analysis and the generation of surrogate models. Subsequently, the dataset and surrogate models are employed in two optimization studies aimed at maximizing fuel utilization and economic profit while adhering to safety and operational constraints. Performing the optimization process with fuel utilization as the metric leads to an improvement of approximately 10%, compared to engineering-judgment-based nominal conditions. The optimization on economic profit leads to an estimated increase of ∼300 million USD over the lifetime of the reactor. •Multiphysics tool developed for streamlining pebble-bed reactor design optimization.•Sensitivity analysis correlates design parameters and reactor safety and economics.•Surrogate modeling techniques compared to accelerate optimization process.•Optimized model improves reactor economics, while abiding by safety constraints.