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atw Vol. 62 (2017) | Issue 6 ı June 408 RESEARCH AND INNOVATION [9] Expert Group on Assay Data of Spent Nuclear Fuel. Spent Nuclear Fuel Assay Data for Isotopic Validation - State-ofthe-art Report, NEA/NSC/WPNCS/ DOC(2011)5, Organisation for Economic Co-operation and Development/Nuclear Energy Agency (OECD/ NEA), June 2011. [10] Kenya Suyama, Minoru Murazaki, Kiyoshi Ohkubo, Yoshinori Nakahara, Gunzo Uchiyama. Re-evaluation of Assay Data of Spent Nuclear Fuel obtained at Japan Atomic Energy Research Institute for validation of burnup calculation code systems, Annals of Nuclear Energy, vol.38, pp.930-941, 2011. [11] M.C. Brady Raap, B.A. Collins, J.A. Lyons, J.V. Livingston. FY13 Summary Report on the Augmentation of the Spent Fuel Composition Dataset for Nuclear Forensics: SFCOMPO/NF, PNNL-23225, Pacific Northwest National Laboratory, Richland, Washington, March 2014. [12] Ian C. Gauld, Georgeta Radulescu, Germina Ilas. SCALE Validation Experience Using an Expanded Isotopic Assay Database for Spent Nuclear Fuel, Proceedings of the International Burnup Credit (BUC) Workshop, Cordoba, Spain, October 2009. [13] Keisuke Okumura, Shiho Asai, Yukiko Hanzawa, Hideya Suzuki, Masaaki Toshimitsu, Jun Inagawa, Tsutomu Okamoto, Nobuo Shinohara, Satoru Kaneko, Kensuke Suzuki. Analyses of Assay Data of LWR Spent Nuclear Fuels with a Continuous-Energy Monte Carlo Code MVP and JENDL-4.0 for Inventory Estimation of 79Se, 99Tc, 126Sn and 135Cs, Progress in NUCLEAR SCIENCE and TECHNOLOGY, Vol. 2, pp.369-374, 2011. [14] Philippe Bienvenu, Philippe Cassette, Gilbert Andreoletti, Marie-Martine Bé, Jérôme Comte, Marie-Christine Lépy. A new determination of 79 Se half-life, Applied Radiation and Isotopes, vol.65, 355-364, 2007. [15] O. W. Hermann, M. D. DeHart, and B. D. Murphy. Evaluation of measured LWR spent fuel composition data for use in code validation, ORNL/M-6121, Oak Ridge National Laboratory, Oak Ridge, Tennessee, February 1998. [16] O. W. Hermann, S. M. Bowman, M. C. Brady, C. V. Parks. Validation of the SCALE System for PWR Spent Fuel Nuclide composition Analyses, ORNL/ TM-12667, Oak Ridge National Laboratory, Oak Ridge, TN, March 1995. [17] M. D. DeHart, O. W. Hermann. An Extension of the Validation of SCALE (SAS2H) Isotopic Predictions for PWR Spent Fuel, ORNL/TM-13317, Oak Ridge National Laboratory, Oak Ridge, TN, September 1996. [18] Jeong-nam Jang, Hyung-moon Kwon, Jung-suk Kim, Yong-bum Chun. Validation of SCALE SAS2H Isotopic Predictions for high burnup PWR spent fuels, Transactions of the 2009 Korean Nuclear Society Spring Meeting, Jeju, Korea, May 22, 2009. [19] M. J. Bell. ORIGEN – the ORNL isotope generation and depletion code, ORNL-4628, Oak Ridge National Laboratory, Oak Ride, Tennessee, May 1973. [20] http://scale.ornl.gov/origen-arp.shtml [21] C. E. Sanders, L C. Gauld, R. Y. Lee. Isotopic Analysis of High-Burnup PWR Spent Fuel Samples From the Takahama-3 Reactor, NUREG/CR-6798, ORNL/TM-2001/259, United States Nuclear Regulatory Commission, Washington, DC, January 2003. [22] Christine Chabert, Alain Santamarina, Robin Dorel, Didier Biron, Christine Poinot-Salanon. Qualification of the APOLLO 2 assembly code using PWR- UO2 isotopic assays – the importance of irradiation history and thermomechanics onfuel inventory prediction, Proceedings of the American Nuclear Society International Topical Meeting on Advances in Reactor Physics, and Mathematics and Computation Into the Next Millennium (PHYSOR-2000), Pittsburgh, Pennsylvania, May 7-11, 2000. [23] Yoshinori Nakahara, Kenya Suyama, and Takenori Suzaki. Technical Development on Burn-up Credit for Spent LWR Fuels, (Eds.), JAERI-Tech 2000-071, Japan Atomic Energy Research Institute (JAERI), 2000 (in Japanese). Translation published as ORNL/TR-2001/01, Oak Ridge National Laboratory, 2002. [24] P.Barbero et.al. Post Irradiation Analysis of The Obrigheim PWR Spent Fuel. Nuclear Science and Technology, 1980. Figure captions Author Man Cheol Kim School of Energy Systems Engineering Chung-Ang University 84 Heukseok-ro Dongjak-gu, Seoul 06974, Korea Reliability Analysis on Passive Residual Heat Removal of AP1000 Based on Grey Model Qi Shi, Zhou Tao, Muhammad Ali Shahzad, Li Yu and Jiang Guangming 1 Introduction It is common to base the design of passive systems [1, 2] on the natural laws of physics, such as gravity, heat conduction, inertia. For AP1000, a generation-III reactor, such systems have an inherent safety associated with them due to the simplicity of their structures. However, there is a fairly large amount of uncertainty in the operating conditions of these passive safety systems. In some cases, a small deviation in the design or operating conditions can affect the function of the system, and the failure to achieve its desired aim is termed as function failure [3]. In the reliability analysis of the passive systems, the main sources of the uncertainty [4] are the numerical errors in the calculation program such as RELAP5 and the reactor parameters. However, a lot of experience is required to analyze the error propagation in such system codes. The difficult is increased by the fact that AP1000 has not been connected to the grid yet. In this paper, more focus has been placed on the uncertainties of design and operation parameters of the reactor. The analytic hierarchy process (AHP) [5, 6] and artificial neural network (ANN) [7] have been applied, in order to perform a sensitivity analysis on different parameters of the passive safety systems. However, there are large subjective qualitative considerations in the AHP. On the other hand, ANN has a large amount of randomness, thus requiring a large amount of data for its training. Hence, these methods have many limitations. The grey correlation method [8]-[9], which has been applied in many fields, can make up for Research and Innovation Reliability Analysis on Passive Residual Heat Removal of AP1000 Based on Grey Model ı Qi Shi, Zhou Tao, Muhammad Ali Shahzad, Li Yu and Jiang Guangming
atw Vol. 62 (2017) | Issue 6 ı June | | Fig. 1. Passive Residual Heat Removal System. the limitations of above statistical methods. It does not require a large amount of data and its results are consistent with the qualitative implications. For sensitivity analysis on PRHRS, it is rare to find the application of Grey correlation method in literature. The grey derivative and differential equations are defined in the grey system model [10] to establish the dynamic prediction, based on concepts of space relevance and smooth discrete function. It is used to forecast the passive system parameters. For AP1000, the loss of normal feedwater accident has been taken as an example in this paper, which involves the drop of control rod, operation of PRHRS, coolant pump outage and so on. Grey correlation is used to analyze the importance of influencing factors on PRHRS, and the Grey model plays an important role for predicting the data. This provides a new viewpoint for studying the PRHRS of AP1000. 2 Research object 2.1 System description As an important part of AP1000 passive core cooling system, PRHRS [11] is used to remove the decay heat of the core for ensuring the safety of reactor during the accident operating conditions. It consists of a ‘C’ type heat exchanger, an in-containment refueling water storage tank (IRWST) and the corresponding pipes and valves. Figure 1 shows the layout of PRHRS in AP1000. The PRHRS heat exchanger is located at a higher elevation than the reactor core. Its upper head is connected to the hot leg of reactor coolant system (RCS) and its lower head is connected to the lower head of the steam generator. There is an electro valve normally in the open state located on the inlet line. Two parallel pneumatic valves normally in the close state are located on the outlet line. Once the accident takes place, the pneumatic valves are opened by pneumatic signal, and the electro valve also receives a signal to confirm its open state. Corresponding, a completely natural circulation loop is established and the decay heat is removed by the IRWST using density difference. It is complex to gage the factors, affecting the heat transfer capacity of PRHRS, and its interactions with RCS. Hence, the parametric uncertainties have different effects on PRHRS under different accident conditions. Loss of normal feedwater accident belongs to the second type accidents, meaning medium frequency accident. When the accident takes place, the decay heat needs to be adequately removed from the reactor core. Otherwise, the reactor core may be damaged. In this paper, the influence of parametric uncertainties on PRHRS has been studied under the loss of normal feedwater accident. X 1 X 2 | | Tab. 1. Parameters and corresponding distribution. 2.2 Identification and quantification of uncertainties PRHRS relies on natural circulation, which has a much weaker driving force than the active systems. A small deviation from the design and operating conditions can lead to function failure. According to the criterion of International Atomic Energy Agency (IAEA) [13], the PRHRS is considered to fail in providing its safety function, once the maximum coolant temperature at the reactor outlet exceeds beyond 350 °C, in order to avoid fuel cladding damage. Only the epistemic uncertainties have been considered in the present analysis. In accordance with the previous studies [14, 15], some design and operating parameters have been selected for analysis together with the corresponding distribution as shown in Table. 1. 2.3 Thermal hydraulic model and verification According to the current research results [12], RELAP5 program can be used to analyze the loss of normal feedwater accident in AP1000. It has been developed by Idaho National Engineering and Enviromental Laboratory for transient thermal hydraulic analysis in light water reactors. In this paper, RELAP5/MOD3.4 has been used for the calculation. The nodalization scheme of AP1000 is shown in Figure 2. As depicted in Fig. 2, the model consists of reactor core, two steam generators, pressurizer, reactor coolant pump, PRHRS core makeup tank (CMT) and so on. Some preliminary calculations are performed to determine the steady-state parameters of AP1000 using RELAP5, in order to ensure their consistency with the reference standards. According to the sequence events in transient con ditions, some factors have been Variable Description Average value Standard deviation Distribution T (K) D (mm) Temperature of IRWST Diameter of PRHR HX X 3 Kin Resistance coefficient of inlet X 4 X 5 X 6 H (m) P (MPa) Q (MW) Height of ascending Pipeline 300 10 Normal 0.0162 0.002 Normal 50 17 Normal 9.0 0.33 Normal Initial pressure 15.5 0.5 Normal Initial power level 3415 11.6 Normal RESEARCH AND INNOVATION 409 Research and Innovation Reliability Analysis on Passive Residual Heat Removal of AP1000 Based on Grey Model ı Qi Shi, Zhou Tao, Muhammad Ali Shahzad, Li Yu and Jiang Guangming
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