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Application of information technologies (genetic algorithms, neural networks, parallel calculations) in safety analysis of Nuclear Power Plants
KTH, School of Engineering Sciences (SCI), Physics, Nuclear Power Safety.ORCID iD: 0000-0002-0683-9136
KTH, School of Engineering Sciences (SCI), Physics, Nuclear Power Safety.ORCID iD: 0000-0001-5653-9206
KTH, School of Engineering Sciences (SCI), Physics, Nuclear Power Safety.
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2014 (English)In: Proceedings of the Institute for System Programming, ISSN 2220-6426, Vol. 26, no 2, 137-158 p.Article in journal (Refereed) Published
Abstract [en]

This paper investigates important issues in three types of safety assessment methodologies commonly applied for Nuclear Power Plants (NPP). These methodologies are i) dynamic probabilistic safety assessment (DPSA) where application of genetic algorithm (GA) is shown to improve the efficiency of the analysis, ii) deterministic safety assessment (DSA) with meta model representation of the system using pre-performed computational fluid dynamics (CFD) code and iii) vulnerability search (e.g. identification of accident scenarios in an NPP) with application of neural network (NN). The use of advanced computational tools and methods such as genetic algorithms, neural networks and parallel computations improve the efficiency of safety analysis. To achieve the best effect, these advanced technologies are to be integrated with existing classical methods of safety analysis of the NPP.

Place, publisher, year, edition, pages
2014. Vol. 26, no 2, 137-158 p.
Keyword [en]
information technologies, safety analysis, meta-modelling
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:kth:diva-163877DOI: 10.15514/ISPRAS-2014-26(2)-6OAI: oai:DiVA.org:kth-163877DiVA: diva2:802573
Note

QC 20150414

Available from: 2015-04-13 Created: 2015-04-13 Last updated: 2015-10-20Bibliographically approved

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Kudinov, PavelJeltsov, Marti

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