The requirements for the next generation of nuclear reactors call for more radiation tolerant materials. One such material, nanostructured ferritic alloys (NFA) are a candidate material for use in cladding. The radiation tolerance of NFAs comes from the high number density of small oxide nanofeatures composed of Y, Ti, and O. These oxide nanofeatures or nano-oxides act as alternative nucleation sites for bubbles of transmutation He, thus preventing the accumulation of He atoms at the grain boundaries which would embrittle the metal.

To further study the material, a mean-field rate theory model (MF-RTM) was created to simulate the radiation-induced segregation (RIS) of the alloy components Y, Ti, and O to the grain boundaries. Later, a kinetic Monte Carlo model (KMC) was made that replicated the results from the rate theory for the radiation induced segregation. Then the KMC model was modified to study the nano-oxide behavior in a range of different behaviors; the nano-oxide precipitation kinetics during heat treatment, resistance to dissolution under irradiation regimes similar to reactor conditions, and ability to trap He bubbles on the nano-oxide surfaces rather than the grain boundary. This KMC model is more complex than others as it includes 5 different atomic species (Fe, Y, Ti, O, and He) which migrate through three different mechanisms. Findings from the precipitation heat treatments were able to replicate the size, number density, and composition of nano-oxides from experiments and determined vacancy trapping at oxide interfaces was a significant for the NFA's coarsening resistance as opposed to interference from dislocations. In the irradiation simulations, the resistance of the nano-oxides to dissolution was confirmed and found the excess vacancy population plays an important role in healing the nano-oxides. He bubbles formed in the KMC simulations were found to preferentially form at the oxide interfaces, particularly the <111> interface, rather than the grain boundary and the characteristics of the He bubbles match expectations from literature. In the development of the KMC model, new insights into steady-state detection concepts were also found. A new type of steady-state detection (SSD) algorithm is described. Additionally, a method of forecasting the number of data points needed to make an accurate determination of steady-state, a 'predicting the pre-requisite to steady state detection' (ppSSD), is explored.