Modeling of gap conductance for LWR fuel rods applied in the BISON code

References

• Ross AM, Stoute RL. 1962. Heat transfer coefficient between UO₂ and Zircaloy-2. Canada: Atomic Energy of Canada. CRFD-1075: https://inis.iaea.org/collection/NCLCollectionStore/_Public/43/103/43103492.pdf
   Google Scholar
• Hales JD, et al. BISON theory manual the equations behind nuclear fuel analysis. Idaho Falls (ID): Idaho National Laboratory; 2014.
   Google Scholar
• Salko R, et al. CTF 4.0 Theory Manual. Oak Ridge (TN): Oak Ridge National Laboratory; 2019. ORNL/TM-2019/1145. doi:10.2172/1550750.
   Google Scholar
• TRACE V5.0 theory manual field equations, solution methods, and physical models. USA; 2015, DC 20555-0001. [Accessed September 2019] https://www.nrc.gov/docs/ML0710/ML071000097.pdf
   Google Scholar
• Geelhood K et al. FRAPCON-4.0: A Computer Code for the Calculation of Steady-State, Thermal-Mechanical Behavior of Oxide Fuel Rods for High Burnup. USA; 2015. [Accessed September 2019] https://frapcon.labworks.org/Code-documents/FRAPCON_Description_Final.pdf
   Google Scholar
• Toptan A, Salko R, Avramova M, et al. A new fuel modeling capability, CTFFuel, with a case study on the fuel thermal conductivity degradation. Nucl Eng Des. 2019;341:248–258.
   Web of Science ®Google Scholar
• Toptan A, Kropaczek D, Avramova M. Gap conductance modeling II: optimized model for UO2-Zircaloy interfaces. Nucl Eng Des. 2019;355:110289.
   Web of Science ®Google Scholar
• Williamson RL, Gamble KA, Pereza DM, et al. Validating the BISON fuel performance code to integral LWR experiments. Nucl Eng Des. 2016;301:232–244.
   Web of Science ®Google Scholar
• Bird RB, Stewart WE, Lightfoot EN. Transport Phenomena. New York: John Wiley and Sons, Inc.; 1960.
   Google Scholar
• Toptan A. A novel approach to improve transient fuel performance modeling in multi-physics calculations. Raleigh (NC): North Carolina State University, Nuclear Engineering; 2019. https://repository.lib.ncsu.edu/handle/1840.20/36352
   Google Scholar
• Garnier JE, Begej S. Ex-reactor determination of thermal gap conductance between uranium dioxide and Zircaloy-4 – stage II: high gas pressure. USA: Pacific Northwest Laboratory; 1980. NUREG/CR-0330; PNL-3232. doi:10.2172/1076471.
   Google Scholar
• Toptan A, Kropaczek D, Avramova M. Gap conductance modeling I: theoretical considerations for single- and multi-component gases in curvilinear coordinates. Nucl Eng Des. 2019;353:110283.
   Web of Science ®Google Scholar
• Hagrman DL, Reymann GA, Manson RE. MATPRO-version 11 (rev. 1): a handbook of materials properties for use in the analysis of light water reactor fuel rod behavior. Idaho Falls (ID): Idaho National Engineering Laboratory; 1980. NUREG/CR-0497, TREE-1290. doi:10.2172/6442256.
   Google Scholar
• Lanning DD, Hann CR. Review of review of methods applicable to the calculation of gap conductance in Zircaloy-Clad UO₂ fuel rods. USA: Pacific Northwest Laboratory; 1975. BNWL-1894. doi:10.2172/4209005.
   Google Scholar
• Jacobs G, Todreas N. Thermal contact conductance in reactor fuel elements. Nucl Sci Eng. 1973;50(3):283–306.
   Web of Science ®Google Scholar
• Oberkampf WL, Roy CJ. Verification and validation in scientific computing. Cambridge, UK: Cambridge University Press; 2010.
   Google Scholar
• Hann C, Lanning D, Bradley E, et al. Data report for the NRC/PNL Halden assembly IFA-432. PNL, 1978, NUREG/CR-0560; PNL-2673. doi:10.2172/6401826.
   Google Scholar
• Sartori E, Killeen J, Turnbull J International fuel performance experiments (IFPE) database. OECD-NEA, 2010. [Accessed September 2019] Available at www.oecd-nea.org/science/fuel/ifpelst.html.
   Google Scholar
• Tournier J-MP, El-Genk MS. Properties of noble gases and binary mixtures for closed brayton cycle applications. Energy Convers Manag. 2008;49(3):469–492.
   Web of Science ®Google Scholar
• Toptan A, Kropaczek D, Avramova M. On the validity of the dilute gas assumption for gap conductance calculations in nuclear fuel performance codes. Nucl Eng Des. 2019;350:1–8.
   Web of Science ®Google Scholar
• BISON Team. Assessment of BISON: a nuclear fuel performance analysis code. USA: Idaho National Laboratory, Fuels Modeling and Simulation Department; 2017. INL/MIS-13-30314.
   Google Scholar
• Tverberg T, Amaya M. Study of thermal behaviour of UO₂ and (U,Gd)O₂ to high burnup (IFA-515). Norway: OECD Halden Reactor Project; 2001. ( HWR-671).
   Google Scholar
• Lösönen P. Early-in-life Irradiation of IFA-562.2 (the ultra high burn-up experiment). Norway: OECD Halden Reactor Project; 1989. ( HWR-247).
   Google Scholar
• Fuel modelling at extended burnup (FUMEX-II): report of a coordinated research project. International Atomic Energy Agency, 2002-2007, IAEA-TECDOC-1687.
   Google Scholar
• The third Risø fission gas project: bump test AN2 (CB6). Risø, 1990, Risø-FGP3-AN2, Risø.
   Google Scholar
• The third Risø fission gas project: bump test AN4 (CB7-2R). Risø, 1990, Risø-FGP3-AN4.
   Google Scholar
• Onnes HK. Expression of the equation of state of gases and liquids by means of series, in Koninklijke Nederlandse Akademie van Wetenschappen. Proc Ser B Phys Sci. 1901;4:125–147.
   Google Scholar
• Mason EA, Spurling TH. The virial equation of state. Toronto: Pergamon Press; 1969.
   Google Scholar
• Weisstein EW “Cubic Formula.” from mathworld–a wolfram web resource. [ Online; cited 2019 Aug 8]. Available at http://mathworld.wolfram.com/cubicformula.html
   Google Scholar
• Wolfram—alpha widgets: cubic equation solver. [ Online; cited 2019 Dec 5]. Available at https://www.wolframalpha.com/widgets/view.jsp?id=1beb192fcbfba3b6afa17b00ae68605a
   Google Scholar
• Sobol’ I. Sensitivity estimates for nonlinear mathematical models. Math Modell Comput Exp. 1993(1):404–414.
   Google Scholar
• Homma T, Saltelli A. Importance measures in global sensitivity analysis of non linear models. Reliab Eng Syst Saf. 1996;52(1):1–17.
   Web of Science ®Google Scholar
• Iooss B, Lemaître P. A review on global sensitivity analysis methods. Boston, MA: Springer; 2015. DOI:10.1007/978-1-4899-7547-8_5.
   Google Scholar
• Herman J, Usher W. Salib: an open-source python library for sensitivity analysis. The Journal of Open Source Software. 2017;2(9):97.
   Google Scholar
• Smith R Uncertainly Quantification: theory, Implementation, and Applications. SIAM in the Computational Science and Engineering Series, CS12; 2014.
   Google Scholar
• Saltelli A. Making best use of model evaluations to compute sensitivity indices. Comput Phys Commun. 2002;145(2):280–297.
   Web of Science ®Google Scholar
• Kolmogorov AN. Sulla Determinazione Empirica di una Legge di Distribuzione. Giornale dell’Istituto Italiano degli Attuari. 1933;4:83–91.
   Google Scholar
• Smirnov NV. On the estimation of the discrepancy between empirical curves of distribution for two independent samples. Bull Math l’Universite Moscou. 1939;2:3–16.
   Google Scholar
• Smirnov NV Table for estimating the goodness of fit of empirical distributions. Ann Math Stat. 1948;19(2):279–281. https://doi.org/10.1214/aoms/1177730256
   Google Scholar
• Jones E, Oliphant T, Peterson P, et al. SciPy: open source scientific tools for Python. 2001. [ Online; cited 2019 Dec 5]. Available at http://www.scipy.org
   Google Scholar