Abstract

A practical fuel management system for the he Pennsylvania State University Breazeale Research Reactor (PSBR) based on the advanced Monte Carlo methodology was developed from the existing fuel management tool in this research. Several modeling improvements were implemented to the old system. The improved fuel management system can now utilize the burnup dependent cross section libraries generated specifically for PSBR fuel and it is also able to update the cross sections of these libraries by the Monte Carlo calculation automatically. Considerations were given to balance the computation time and the accuracy of the cross section update. Thus, certain types of a limited number of isotopes, which are considered “important”, are calculated and updated by the scheme. Moreover, the depletion algorithm of the existing fuel management tool was replaced from the predictor only to the predictor-corrector depletion scheme to account for burnup spectrum changes during the burnup step more accurately. An intermediate verification of the fuel management system was performed to assess the correctness of the newly implemented schemes against HELIOS. It was found that the agreement of both codes is good when the same energy released per fission (Q values) is used. Furthermore, to be able to model the reactor at various temperatures, the fuel management tool is able to utilize automatically the continuous cross sections generated at different temperatures. Other additional useful capabilities were also added to the fuel management tool to make it easy to use and be practical. As part of the development, a hybrid nodal diffusion/Monte Carlo calculation was devised to speed up the Monte Carlo calculation by providing more converged initial source distribution for the Monte Carlo calculation from the nodal diffusion calculation. Finally, the fuel management system was validated against the measured data using several actual PSBR core loadings. The agreement of the predicted core excess reactivities and the measured values is found to be good considering the measurement uncertainties.

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