| Summary: | With the ever-increasing demands of the nuclear power community to extend fuel cycles and overall core-lifetimes in a safe and economic manner, it is becoming more necessary to extend the working knowledge of nuclear fuel performance. From the atomistic to the macroscopic level, great morphological changes occur within the fuel over its lifetime. The main initial damaging events produced by fuel recoils from fast neutrons and fission fragment spiking leads to the onset of grain growths and fuel restructuring. Therefore, it is desirable to have a more detailed understanding of the initial events leading to fuel morphology changes at the atomistic level. However, this is difficult to achieve with the fission fragments due to the wide variability of their species (charge, mass, and energy) and the large averaging of their relative yields in the nuclear data files.
This work is our first iteration at developing a general methodology to characterize a procedure, based on Monte Carlo principles, for generating individual fission event result channels and analyzing their specific response in the fuel. We utilized the nuclear reaction simulation tool, TALYS, to generate energy-dependent fission fragment yield distributions for different fissile/fissionable isotopes. These distributions can then be used in conjunction with fuel isotopics and a neutron energy spectrum to generate a fission-reaction-rate-averaged distribution of the fission fragment yields. We then used Monte Carlo sampling to generate the result channels from individual fission events, using the Q-value of the prompt fission system to either accept or reject. The simulation tool: Transport of Ions in Matter (TRIM) was used to characterize the general response of the fission fragment species within Uranium Dioxide (UO2), including the range, energy loss, displacements, recoils, etc. These responses were then correlated which allowed for the quick calculation of the response of the individual fission fragment species generated from the Monte Carlo sampling. As an example of this strategy, we calculated the response on a PWR fuel pin where MCNP was used to generate a high-fidelity neutron energy spectrum.
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