Nuclear Security Assessment Using Loss Function with Modified Random Numbers

The energy production in nuclear power plants (NPPs) is investigated for the safeguard risk management using economic factors. The economic loss function is used for the life quality in the social and natural objects. For the basic event elements, the game theory is applied for the basic elements of the incidents in non-secure situations. The Safeguard Factor (SF) is introduced for the quantifications of simulation. The results are shown by the standard productivity comparisons with the designed power operations, which is obtained as the range of secure life extension in 2,000 MWe is between 0.0000 and 9.1985 and the range in 600 MWe is between 0.0000 and 2.7600. So, the highest value in the range of secure power operation increases about 3.33 times higher than that of the interested power operation in this study, which means the safeguard assessment is quantified by the power rate in the life extension of the NPPs. The Nuclear Safeguard Protocol (NSP) is constructed for the safe operation successfully.

[1]  Paul H. Selden,et al.  Sales Process Engineering: A Personal Workshop , 1996 .

[2]  R. D. Jones,et al.  Calculating the benefit to workers of averting a radiation exposure lasting longer than the working lifetime , 2009 .

[3]  David W. Stupples,et al.  Numerical Techniques for Speeding up the Calculation of the Life Extension Brought About by Removing a Prolonged Radiation Exposure , 2007 .

[4]  M. Hazewinkel.  Encyclopaedia of mathematics , 1987 .

[5]  Messias Borges Silva,et al.  Electrodeposition of copper on titanium wires: Taguchi experimental design approach , 2009 .

[6]  Henry P. Wynn,et al.  Quality through design : experimental design, off-line quality control and Taguchi's contributions , 1991 .

[7]  Pablo Martin de Holan,et al.  It’s All in Your Head , 2014 .

[8]  Samuel Bowles,et al.  Microeconomics: Behavior, Institutions, and Evolution , 2003 .

[9]  J. M. Bilbao,et al.  Cooperative Games on Combinatorial Structures , 2000 .

[10]  Svein J. Knapskog,et al.  On Stochastic Modeling for Integrated Security and Dependability Evaluation , 2006, J. Networks.

[11]  J. Berger.  Statistical Decision Theory and Bayesian Analysis , 1988 .

[12]  Phil J Hobbs,et al.  The Taguchi methodology as a statistical tool for biotechnological applications: A critical appraisal , 2008, Biotechnology journal.

[13]  O. Brafman,et al.  The starfish and the spider : the unstoppable power of leaderless organizations , 2006 .

[14]  K. K. Prasad,et al.  Xylitol production by Candida sp.: parameter optimization using Taguchi approach , 2004 .