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Sensitivity Analysis of Fabrication Parameters for Dry Process Fuel Performance Using Monte Carlo Simulations

Journal of the Korean Nuclear Society = 원자력학회지, v.36 no.4, 2004년, pp.338 - 345  

Park Chang Je (Korea Atomic Energy Research Institute) ,  Song Kee Chan (Korea Atomic Energy Research Institute) ,  Yang Myung Seung (Korea Atomic Energy Research Institute)

Abstract AI-Helper 아이콘AI-Helper

This study examines the sensitivity of several fabrication parameters for dry process fuel, using a random sampling technique. The in-pile performance of dry process fuel with irradiation was calculated by a modified ELESTRES code, which is the CANDU fuel performance code system. The performance of ...

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제안 방법

  • Several fuel fabrication parameters are chosen from the CANDU fuel design manual for the sensitivity analysis. A Monte Carlo simulation with a random sampling technique is performed for both dry process fuel and UO2 fuel, and various statistical results are obtained from the simulations. Among the fuel fabrication parameters, the pellet density is the most sensitive parameter for the fuel irradiation performance for both dry process fuel
  • According to modeling experts, nonparametric distributions, such as uniform, triangular, and discrete distributions, are far more reliable and flexible [10]. In this study, a uniform distribution (nonparametric distribution) was chosen that takes into account the fabrication environment of fuel rod. The outlayers of the fabrication specification are withdrawn in the fabrication process.

데이터처리

  • 7. The results are analyzed using a statistical method, and the correlation coefficients are also obtained to find the relationship between the input and output variables.

이론/모형

  • For this study, the ELESTRES code [9, 10] is chosen, considering its ease of application and the original target of the dry process fuel. The ELESTRES is a computer program designed to predict the behavior of CANDU fuel under normal operating conditions.
  • Among several methods of sensitivity analyses and uncertainty analyses, suitable sampling techniques are adopted to find the optimal design parameters of dry process fuel. In this study, a random sampling approach [6] is used to obtain the fabrication parameters, with the design criteria, and is given in Ref. 7. The results are analyzed using a statistical method, and the correlation coefficients are also obtained to find the relationship between the input and output variables.
  • In general, the purpose of a sensitivity analysis is to determine the change of a response to the changes of the model parameters and specifications. In this study, the Spearman rank correlation method is used for the sensitivity analysis.
  • It takes into account the fuel geometry, material properties, and the operating conditions, and predicts the percentage of fission gas release, internal gas pressure, radial temporat나re distribution, and the percentage of the elastic and plastic sheath strains in a given fuel element [9]. To evaluate the performance of dry process fuel, we modified the ELESTRES code with the thermal calculation models. Specifically, the thermal conductivity and thermal expansion models of the dry process fuel are added, and minor parameters are changed to be appropriate for dry process fuel.
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참고문헌 (12)

  1. Generation 4 Roadmap - Report of the Fuel Cycle Crosscut Group, DOE, FCCG Summary Rpt FR02-00, November 1, (2001) 

  2. H.S. Park, et al.,' The DUPIC Fuel Cycle Alternatives: Status & Perspective,' Proceedings of the 10th PBNC, 1996, Kobe, Japan 

  3. M.S. Yang, et al., 'Characteristics of DUPIC Fabrication Technology,' Proc. Int. Conf. Future Nuclear Systems: Challenge towards Second Nuclear Era with Advanced Fuel Cycles. Gobal' 97, p.535, 1997,Yokohama, Japan 

  4. K.C. Song, et al., Irradiation Tests and Performance Evaluation of DUPIC Fuel, KAERI/RR- 2236/2001, MOST, Korea (2001) 

  5. A. Saltelli, et.al., Sensitivity Analysis, John Wiley & Sons Ltd., (2000) 

  6. C.J. Park, et al., 'Development of an Optimization Method for Fuel Design Parameters Using Sampling Techniques,' Proc. Korean Nuclear Society, Kyung-ju, Korea (2003).(CD-Rom) 

  7. I.E. Oldaker and M. Gacesa, Fuel Design Manual for CANDU 6 Reactor, DM-XX-37000-001, AECL, Canada (1989) 

  8. K.H. Kang, et al., 'The Thermal Conductivity of Simulated DUPIC Fuel,' J. Nucl. Sci. Tech., Suppl, 3, 776 (2002) 

  9. H.C. Suk, et.al., ELESTRES.M11K Program Users' Manual and Description, KAERI/TR-320/92, Korea Atomic Energy Research Institute, (1992) 

  10. K.S. Sim, et.al., Modification of ELESTRES Code with New Database of Flux Depression across the Pellet Radius, KAERI/TR-485/94, Korea Atomic Energy Research Institute, (1994) 

  11. D. Vose, Quantitative Risk Analysis: A Guide Monte Carlo Simulation Method, John Wiley & Sons, New York, (1996) 

  12. D.R. Olander, Fundamental Aspects of Nuclear Reactor Fuel Elements, Technical Information Center, U.S. Department Energy. (1976) 

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