An international journal published by K. N. Toosi University of Technology

Document Type : Research Article

Authors

Department of Nuclear Engineering, Faculty of Physics, University of Isfahan‎, ‎Isfahan‎, ‎Iran

Abstract

Probabilistic uncertainty and sensitivity analysis is frequently recommended for safety and reliability assessment of computer simulations. For this purpose, SUAP has been developed, and its latest version is capable of working on analysis results obtained using five well-known nuclear codes (i.e. FRAPCON, FRAPTRAN, FEMAXI, MCNP, and COBRA). SUAP provides support to properly quantify input uncertainties as to probability distributions and appropriate dependency functions. Using the Monte-Carlo sampling method, random combinations of different uncertain input parameters are generated and used to make input files for the corresponding code applied for the modeling. To quantify uncertainties, SUAP determines the variation range for each specific output parameter at any chosen time and/or location. Moreover, sensitivity analysis is accomplished based on the Spearman correlation. In this study, in order to evaluate SUAP applicability, UQ&SA for fuel performance modeling of VVER-1000 fuel rods using FRAPCON code has been accomplished. Acquired results exhibit the possible range of uncertainties in fuel centerline temperature, as well as the importance of different uncertain input parameters on that.

Highlights

  • Probabilistic uncertainty quantification and sensitivity analysis is  facilitated using SUAP toolkit.
  • SUAP is capable of coupling with some frequently used nuclear codes (FRAPCON, FRAPTRAN, ...).
  • Monte-Carlo sampling method is used.
  • Sensitivity (or importance) analysis using Spearman rank ordered coefficient is studied.
  • There are no limitations neither on the numbers of uncertain input parameters nor on the number of the code runs.

Keywords

Main Subjects

Adams, B. (2020). DAKOTA, Multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis: Version 6.13 user’s manual. Technical Report SAND2020-12495, Available online from http://dakota.sandia.gov/documentation.html. Sandia National Laboratories, Albuquerque, NM 87185, Updated Nov. 2020.
D’Auria, F. S., Dusic, M., Dutton LM, C., et al. (2009). Deterministic Safety Analysis for Nuclear Power Plants. IAEA Specific Safety Guide. IAEA.
Helton, J. C., Johnson, J. D., Sallaberry, C. J., et al. (2006). Survey of sampling-based methods for uncertainty and sensitivity analysis. Reliability Engineering & System Safety, 91(10-11):1175–1209.
Hofer, E. (1999). Sensitivity analysis in the context of uncertainty analysis for computationally intensive models. Computer Physics Communications, 117(1-2):21–34.
Khodadadi, H. and Ayoobian, N. (2020). Conceptual design and uncertainty analysis of a typical 50 kW aqueous homogeneous reactor aimed for medical isotope production. Progress in Nuclear Energy, 121:103233.
LabVIEW, F. (2010). National Instruments (NI), 373427G-01. Austin, Texas, pages 78730–5039.
Rusanov, V., Petkov, P., and Kamenov, K. (2018). Implementation of Uncertainty Analysis for Evaluation of Nuclear Reactors VVER-1000 Fuel Safety Margins during Normal Operation by FEMAXI-6 Computer Code Calculations. Journal of Physics and Technology, 2:19–36.
Saltelli, A., Chan, K., and Scott, E. (2000). Wiley series in probability and statistics. Sensitivity analysis.
Strydom, G. (2010). Use of SUSA in uncertainty and sensitivity analysis for INL VHTR coupled codes. Technical report, Idaho National Lab.(INL), Idaho Falls, ID (United States).
Veshchunov, M., Stuckert, J., Van Uffelen, P., et al. (2018). FUMAC: IAEAs Coordinated Research Project on Fuel Modelling in Accident Conditions. Trans. TopFuel.
Wilks, S. S. (1941). Determination of sample sizes for setting tolerance limits. The Annals of Mathematical Statistics, 12(1):91–96.
Wilks, S. S. (1942). Statistical prediction with special reference to the problem of tolerance limits. The Annals of Mathematical Statistics, 13(4):400–409.
Zhang, J., Umidova, Z., and Dethioux, A. (2015). Simulation of fuel behaviours under LOCA and RIA using FRAPTRAN and uncertainty analysis with DAKOTA. In Modelling of Water Cooled Fuel Including Design Basis and Severe Accidents, Proceedings of a Technical Meeting Held in Chengdu, China, 28 October–1 November 2013, pages 115–142.