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atw Vol. 62 (2017) | Issue 6 ı June RESEARCH AND INNOVATION 412 | | Fig. 5. Correlation degree in different resolution. As seen from Fig. 5, using different resolutions, the factors have different effect on the maximum outlet temperature of the coolant. There is a little difference between the effects of different factors, when the resolution is 0.5. It shows that the above parameters are not well distributed. The individual characteristics of the above parameters are gradually distinguished, once the resolution is reduced from 0.5 to 0.1. The order of these parameters is established, based on their degree of the importance, X 6 , X 1 , X 4 , X 5 , X 2 , X 3 . The initial power has the strongest influence on the decay heat after shutdown. This is followed by temperature of IRWST, which is the cooling source for the core. As the height of the ascending pipe is | | Fig. 6. Error analysis. increased, there is a greater density difference between cold and hot sections, further increasing the mass flow rate of PRHRS. Other parameters X5, X2 and X3 have a less pronounced effect on the maximum outlet temperature of the coolant. 4.3 GM(1,6) model The Grey correlation has been used to determine the influence of each parameter on the maximum coolant temperature at the reactor’s output. Considering the relationship among the parameters, the coolant fluid temperature (X 0 ) is considered to represent the main behavior of the system. The temperature of IRWST (X 1 ), the diameter of PRHR HX(X 2 ), resistance coefficient (X 3 ), height of ascending pipe(X 4 ), initial pressure(X 5 ) and the initial power level(X 6 ) are considered to represent the correlated behavior factors. According to Eq. (9)-(18), an in-house code has been used to build GM (1,6) model. The code randomly selects 90 groups and the other 10 groups are used to validate. The corresponding differential equation is shown as Eq. (19). (19) Figure 6 shows the errors in the coolant temperature as determined by the results from GM(1,6) and RELAP5. As seen in Fig. 6, the results of GM(1,6) agree well with RELAP5, and the errors fall within 15%. The Grey model can adequately predict maximum coolant temperature at the outlet of the reactor core using a small amount of data, making up for the deficiency of artificial neural network (ANN), which becomes unstable with a small amount of data. This is a new way to replace thermal-hydraulic model. 5 Conclusion By taking the loss of normal feedwater in AP1000 as an example, the behavior of PRHRS has been analyzed with the help of RELAP5, and the Grey system method has been applied for calculating the maximum coolant temperature at the outlet of the reactor core. The following conclusions are drawn. (1) The degree of Grey correlation is used to analyze the importance of influencing factors. Smaller the resolution, more obvious is the difference among these factors. The behavior of the factors can be distinguished very easily, when the resolution is 0.1. (2) The initial reactor power has the greatest influence on the maximum coolant temperature at the reactor outlet. And the sequences is followed by the temperature of IRWST, height of ascending pipe, initial pressure. Correspondingly, the diameter and resistance coefficient of PRHRS-HX have a lesser effect. (3) The GM(1,6) model is built to predict maximum coolant temperature at the reactor outlet. All errors fall within 15 % range. 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 Acknowledgments The research has been funded by Science and Technology on Reactor System Design Technology Laboratory Funds (2015BJ0151). References [1] Zhou Tao, Li Jingjing, Ru Xiaolong, et al. Application and development of passive technology in nuclear power units [J]. Proceedings of the CSEE, 2013, 33(8):81-89. [2] Zhou Tao. Passive concept and technology [M]. Beijing, Tsinghua university press, 2016. [3] Burgazzi L. Evaluation of uncertainties related to passive systems performance [J]. Nuclear Engineering and Design, 2004, 230(1):93-106. [4] Zhang Shunxiang, Liang Guoxing. Application of status uncertainty analysis methods for AP1000 LBLOCA calculation [J]. Atomic Energy Science and Technology, 2012,S1:330-334. [5] Zio E, Cantarella M, Cammi A. The analytic hierarchy process as a systematic approach to the identification of important parameters for the reliability assessment of passive systems [J]. Nuclear Engineering and Design, 2003, 226(3):311-336. [6] Ma G, Yu Y, Huang X, et al. Screening key parameters related to passive system performance based on Analytic Hierarchy Process [J]. Annals of Nuclear Energy, 2015, 85:1141-1151. [7] Zio E, Apostolakis G E, Pedroni N. Quantitative functional failure analysis of a thermal–hydraulic passive system by means of bootstrapped Artificial Neural Networks [J]. Annals of Nuclear Energy, 2010, 37(37):639-649. [8] Liu Sifeng. Grey system theory and application [M]. Beijing, Science Press, 2008 [9] Liu Ping, Zhou Tao, Zhang Ming et al. Study on grey correlation degree of influence factors on ONB in narrow channel under natural circulation [J]. Nuclear Power Engineering, 2011, 32(4):29-32. [10] Zhou Tao, Yang Ruichang, Qin Shiwei et al. Study on grey model in ONB of nature circulation [J]. Nuclear Power Engineering, 2005, 26(2):121-124. [11] Sun Hanhong. Third generation nuclear power technology AP1000 [M]. Beijing, China Power Press, 2010. [12] Li Yankai, Lin Meng, Hou Dong et al. Qualitative accident analysis on loss of normal feedwater for AP1000 [J]. Atomic Energy Science and Technology, 2012, S1:295-300. [13] IAEA. Natural Circulation in Water Cooled Nuclear Power Plants Phenomena Models, and Methodology for System Reliability Assessment. IAEA (TEC-DOC-1474). [14] Baosheng Wang, Dongqing Wang et al. Efficient estimation of the functional reliability of a passive system by means of an improved Line Sampling method [J]. Annals of Nuclear Energy, 2013,55: 9-17. [15] Liu Qiang. Reliability analysis of AP1000 passive system based on artificial neural networks [D]. Beijing: Tsinghua University, 2014. [16] Jiang Shuihua, Li Dianqing, Zhou Chuangbing. Non-instrusive stochastic finite element method for slope reliability analysis based on Latin hypercube sampling [J]. Chinese journal of Geotechnical enginerring, 2013, 35(S2):70-76. [17] Wu Guojun, Chen Weizhong, Tan Xiaojun, et al. Program development of finite element reliability method and its application based on Latin Hypercube sampling [J]. Rock and Soil Mechanics, 2015(2):550-554. [18] Zhang Xiaolian, Hao Sipeng, Li Jun, et al. Grey correlation based analysis on impacting factors of maximum power point tracking control of wind power generating unit [J]. Power system Technology, 2015, 39(2):445-449. [19] W Mendenhall. Statistics for engineers and the sciences [M]. Beijing, China Machine Press, 2009. Authors Qi Shi Zhou Tao Muhammad Ali Shahzad Li Yu School of Nuclear science and Engineering North China Electric Power University Beijing, 102206, China Beijing Key Laboratory of Passive Safety Technology for Nuclear Energy Beijing, 102206,China Jiang Guangming Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu, 610041, China RESEARCH AND INNOVATION 413 Experimental Investigation of a Two- Phase Closed Thermosyphon Assembly for Passive Containment Cooling System Kyung Ho Nam and Sang Nyung Kim 1 Introduction After the Fukushima accident, increasing interest has been raised in passive safety systems that maintain the integrity of the containment building. The conventional containment building is a thick, airtight reinforced concrete structure the design of which is highly unfavorable for removing heat from the containment atmosphere to the environment following an accident. Therefore, the sprays and/or fan coolers are installed to control the containment pressure and temperature for maintaining the integrity of the containment. However, either sprays or fan coolers are dependent on the power supply, which is unreliable if Design Basis Accidents (DBAs) are coupled with a station blackout (SBO) or Extended Loss of AC Power (ELAP). Therefore, to improve the reliability and safety of Nuclear Power Plants (NPPs), long-term passive cooling concepts have been developed for advanced reactors. In a previous study, The proposed design was based on an ordinary cylindrical Two-Phase Closed Thermosyphon (TPCT).[1] The exact assembly size and number of TPCTs should be elaborated upon through accurate calculations based on experiments. While the ultimate goal is to propose an effective MPHP design for the PCCS and experimentally verify its performance, a TPCT assembly that was manufactured based on the conceptual design in this paper was tested. Figure 1. Research and Innovation Experimental Investigation of a Two-Phase Closed Thermosyphon Assembly for Passive Containment Cooling System ı Kyung Ho Nam and Sang Nyung Kim
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