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Hybrid expert system implementation to determine core reload patterns

Conference · · Transactions of the American Nuclear Society; (USA)
OSTI ID:6836752
Determining reactor reload fuel patterns is a computationally intensive problem solving process for which automation can be of significant benefit. Often much effort is expended in the search for an optimal loading. While any modern programming language could be used to automate solution, the specialized tools of artificial intelligence (AI) are the most efficient means of introducing the fuel management expert's knowledge into the search for an optimum reload pattern. Prior research in pressurized water reactor refueling strategies developed FORTRAN programs that automated an expert's basic knowledge to direct a search for an acceptable minimum peak power loading. The dissatisfaction with maintenance of compiled knowledge in FORTRAN programs has served as the motivation for the development of the SHUFFLE expert system. SHUFFLE is written in Smalltalk, an object-oriented programming language, and evaluates loadings as it generates them using a two-group, two-dimensional nodal power calculation compiled in a personal computer-based FORTRAN. This paper reviews the object-oriented representation developed to solve the core reload problem with an expert system tool and its operating prototype, SHUFFLE.
OSTI ID:
6836752
Report Number(s):
CONF-891103--
Conference Information:
Journal Name: Transactions of the American Nuclear Society; (USA) Journal Volume: 60
Country of Publication:
United States
Language:
English