Computational Intelligence in Nuclear Applications: Lessons Learned and Recent Developments

Da Ruan, Wesley Hines, Imre Pazsit, Gert Van den Eynde

    Research outputpeer-review


    The main objective of this special issue is to publish a peer-reviewed collection of high quality papers in the relevant topic areas. The focus is on those papers that provide theoretical/analytical solutions to the problems of real interest in computational intelligence for nuclear systems (e.g., uncertainties in measurements). In addition, most contributions have shown a sound conclusion that constitutes added value and technical limitations by applying computational intelligence in nuclear systems. It is hoped that the special issue will provide a clear picture of some recommendations on the future use of computational intelligence in nuclear systems. The Special Issue considers papers addressing the use of computational intelligence for the design, analysis, optimization, control, learning, data/sensor/information fusion, etc., for nuclear systems. With a rigorous review process, we have selected 16 papers from FLINS 2004 related to computational intelligence in nuclear applications with more or less four groups. The first group (four papers respectively by Hines and Uhrig; Hines and Davis; Roverso; Fantoni) reviewed trends in computational intelligence in nuclear engineering, lessons learned from the U.S. NPP on-line monitoring programs, and the OECD Halden Reactor Project's experiences. The second group (five papers respectively by Zhao and Upadhyaya; Mori et al.; Figedy and Oksa; Lee and Seong; Zio and Baraldi) presented applications of computational intelligence in NPPs. The third group (four papers respectively by Domingos et al.; Benitez et al.; Benitez et al.; Adda et al.) reported recent progress on nuclear reactor control with fuzzy and soft computing techniques. The last group (three papers respectively by Sunde et al.; Garcia et al.; Fiordaliso and Kunsch) showed some potential uses of new combined computational intelligence methods to classification, data analysis and decision support systems in nuclear applications.
    Original languageEnglish
    Pages (from-to)165-387
    JournalProgress in Nuclear Energy
    Issue number3-4
    StatePublished - 2005

    Cite this