Abstract
The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper, we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employ a recently introduced machine learning technique, support vector regression (SVR), which is a data driven, kernel based, nonlinear modeling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modeling. We illustrate the performance of the solution and discuss its applicability, that is, complexity, speed, and accuracy.
Highlights
Decrease of the fuel cycle costs is an important factor in nuclear power plant management
We employ a recently introduced machine learning technique, support vector regression (SVR), which is a data driven, kernel based, nonlinear modeling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem
SVR is a supervised learning method in which model parameters are automatically determined by solving a quadratic optimization problem
Summary
Decrease of the fuel cycle costs is an important factor in nuclear power plant management. The economics of the fuel cycle can strongly benefit from the optimization of the reactor core loading pattern, that is, minimization of the amount of enriched uranium and burnable absorbers placed in the core, while maintaining nuclear power plant operational and safety characteristics. The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a reactor physics computer code used for evaluating proposed loading patterns. Since the loading pattern optimization problem is of combinatorial nature and involves heuristics requiring large numbers of core modeling calculations (e.g., genetic algorithms or simulated annealing algorithms), the time needed for one full optimization run is essentially determined by the complexity of the code that evaluates the core loading pattern. SVR is a supervised learning method in which model parameters are automatically determined by solving a quadratic optimization problem
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