Simplified matching pursuits applied to 3D nuclear reactor temperature distribution construction

Abstract

Autonomous control systems can enhance the economic viability of complex systems such as nuclear microreactors. Advanced forms of these control systems benefit from techniques for high-resolution field data reconstruction from sensor data. Basis projection methods present a useful set of methodologies for nonparametric reconstruction of these field data. In this work, high fidelity multiphysics simulations are used to generate microreactor temperature distributions that are then used to evaluate the viability of a basis projection method which employs a generalized set of basis functions, or dictionaries. Additionally, metrics for evaluating the accuracy of the temperature distribution are presented that are motivated both from the fields of traditional signal processing and statistics. The performance of a method based on matching pursuits is compared to a method that uses the sequential ordering of basis functions. The matching pursuits-based method gives improved performance for highly sparse representations of temperature distributions. Additionally, accuracy is analyzed for multiple temperature distributions which represent different potential reactor operation configurations. Overall, the results from this work should be considered as an upper bound on the performance of basis projection methods which employ generalized basis functions for real time reactor control.