Benchmarking FFTF LOFWOS Test# 13 using SAM code: Baseline model development and uncertainty quantification

Liu, Yang; Mui, Travis; Xie, Ziyu; Hu, Rui

doi:10.1016/j.anucene.2023.110010

Benchmarking FFTF LOFWOS Test# 13 using SAM code: Baseline model development and uncertainty quantification

Journal Article · Thu Jul 13 00:00:00 EDT 2023 · Annals of Nuclear Energy

DOI:https://doi.org/10.1016/j.anucene.2023.110010· OSTI ID:2478797

Liu, Yang ^[1]; Mui, Travis ^[1]; Xie, Ziyu ^[2]; Hu, Rui ^[1]

Argonne National Laboratory (ANL), Argonne, IL (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States); North Carolina State University, Raleigh, NC (United States)

The development and deployment of advanced reactors, such as the sodium-cooled fast reactor (SFR), relies on sophisticated modeling tools to ensure the safety of the design under various transients. The predictive capability of these advanced modeling tools requires validation to garner trust in supporting the licensing of the advanced reactors. For this reason, the International Atomic Energy Agency (IAEA) initiated a coordinated research project (CRP) in 2018 for the analysis of the Fast Flux Test Facility (FFTF) Loss of Flow Without Scram (LOFWOS) Test #13.In this study, we present and discuss the benchmarking efforts of the modern system code SAM on the FFTF LOFWOS Test #13. Further, the SAM baseline model was developed according to the benchmark specification, which included a detailed core model with reactivity feedback. Generally, good agreement was observed between the baseline results and benchmark measurements; however, discrepancies persisted, particularly in predicted fuel assembly coolant outlet temperatures. Utilizing the baseline model, uncertainty quantification (UQ) and sensitivity analysis (SA) were conducted with the assistance of various statistical learning and machine learning methods, including kernel density estimation, Gaussian processes, and Sobol indices. Following the baseline model prediction and UQ and SA results, we discuss the reasons for the simulation discrepancies and propose further improvements to the model. This benchmarking effort adheres to the best-estimate plus uncertainty approach and can serve as a valuable example for supporting risk-informed licensing of advanced reactors.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 2478797

Journal Information:: Annals of Nuclear Energy, Journal Name: Annals of Nuclear Energy Vol. 192; ISSN 0306-4549

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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