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دسته بندی:
تشخیص الگو - Pattern recognition
سال انتشار:
2019
عنوان انگلیسی مقاله:
Robust on-line diagnosis tool for the early accident detection in nuclear power plants
ترجمه فارسی عنوان مقاله:
ابزار تشخیص آنلاین قوی برای تشخیص زود هنگام تصادف در نیروگاه های هسته ای
منبع:
Sciencedirect - Elsevier - Reliability Engineering and System Safety, 186 (2019) 110-119: doi:10:1016/j:ress:2019:02:015
نویسنده:
Silvia Toloa, Xiange Tianb, Nils Bauschb, Victor Becerrab, T.V. Santhoshc, G. Vinodc, Edoardo Patelli⁎,a
چکیده انگلیسی:
Any loss of coolant accident mitigation strategy is necessarily bound by the promptness of the break detection as
well as the accuracy of its diagnosis. The availability of on-line monitoring tools is then crucial for enhancing
safety of nuclear facilities. The requirements of robustness and short latency implied by the necessity for fast and
effective actions are undermined by the challenges associated with break prediction during transients.
This study presents a novel approach to tackle the challenges associated with the on-line diagnostics of loss of
coolant accidents and the limitations of the current state of the art. Based on the combination of a set of artificial
neural network architectures through the use of Bayesian statistics, it allows to robustly absorb different sources
of uncertainty without requiring their explicit characterization in input. It provides the quantification of the
output confidence bounds but also enhances of the model response accuracy. The implemented methodology
allows to relax the need for model selection as well as to limit the demand for user-defined analysis parameters.
A numerical case-study entailing a 220 MWe heavy-water reactor is analysed in order to test the efficiency of the
developed computational tool.
Keywords: LOCA | Neural networks | Pattern recognition | Bayesian statistics | Fault diagnostics | On-line condition monitoring
قیمت: رایگان
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