Development of an object-oriented expert system for PWR core reload Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/zw12z763p

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  • Reactor refueling is a computationally intensive problem solving process that requires automation since significant resources are expended upon the search for an optimal core loading. Without imposing some constraints upon the configurations investigated, an extremely large number of prototype loadings may be generated without discovery of an improved loading. As the result of years of study upon the problem, a fuel management expert understands how to direct and constrain the search for an acceptable minimum peak power loading. This research attempts to automate the expert's knowledge with the ease of representation and maintenance the tools of artificial intelligence (AI) are most specialized. It seeks to make his expertise generally available. The structure, operators, and search methods for representation of the core reload problem are identified and reveal the limitations which many expert system tools have for its solution. An object-oriented representation allows a natural means to define components of the problem and share many dependent attributes of objects in the representation consistently. The expert system prototype, Shuffle, is written in Smalltalk, an object-oriented programming language, and evaluates loadings as it generates them using a two group, two dimensional power calculation compiled in a PC-based FORTRAN. Proven strategies of fuel management experts were incorporated on top of an object oriented representation in a highly interactive environment. Shuffle currently includes three strategies or subgoals, each subgoal progressively positioning less reactive fuel by distinct move instructions. Each subgoal is implemented as a hierarchical subclass of constraints and heuristics that generally remain consistent with each new loading. These constraints include requirements for a modified out-in loading pattern with map regions declared as even, odd, intermediate, and peripheral. Evaluating the intelligence of Shuffle requires an analysis of both its rate of convergence toward an improved pattern and its ability to correct what appears to be a poor pattern. Some experiments with test patterns revealed that additional constraints could improve convergence but may limit exploration by the system.
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