This chapter has attempted to illustrate the power of KMC for modeling radiation effects in structural materials and nuclear fuels, following an introduction and review of the Monte Carlo technique. Monte Carlo modeling, first developed by Metropolis and coworkers9,10 during the Manhattan project does pro­vide a physically satisfying technique to simulate the stochastic evolution of defect evolution in materials science and in fact has been used to simulate irradia­tion effects on materials for four decades. There are three main types of KMC modeling used in irradia­tion effects, namely event Monte Carlo, object Monte Carlo, and atomistic Monte Carlo.

This chapter has focused on describing the atom­istic KMC and OKMC methods by providing two examples of successful KMC simulations to predict the coupled evolution of vacancy clusters and copper precipitates during low dose rate neutron irradiation of Fe-Cu alloys and the transport and diffusional release of the fission product, silver, in TRISO nu­clear fuel. These examples clearly demonstrate the power and ability of KMC models to capture the spatial correlations that can be an important compo­nent of microstructural evolution in nuclear materi­als. Yet, the further widespread application of KMC models will require algorithmic developments that can more readily treat the wide range of time scales inherent in microstructural evolution and yet effec­tively incorporate the rare-event dynamics in inte­grating system performance to realistic time and irradiation dose exposures.

Thus, the challenges that must be overcome in future nuclear materials modeling include:

• bridging the inherently multiscale time and length scales which control materials degradation in nu­clear environments;

• dealing with the complexity of multicomponent materials systems, including those in which the chemical composition is continually evolving due to nuclear fission and transmutation;

• discovering the unknown to prevent technical surprises;

• transcending ideal materials systems to engineer materials and components; and

• incorporating error assessments within each mod­eling scale and propagating the error through the scales to determine the appropriate confidence bounds on performance predictions.

Successful overcoming of these challenges will result in nuclear materials performance models that can predict the properties, performance, and lifetime of nuclear fuels, cladding, and components in a vari­ety of nuclear reactor types throughout the full life cycle, and provide the scientific basis for the compu­tational design of advanced new materials. While the current chapter is focused on the KMC modeling methodology, it is important to note the challenges of predictive materials models of irradiation effects. High performance computing at the petascale, exas — cale, and beyond is a necessary and indeed critical tool in resolving these challenges, yet it is important to realize that exascale computing on its own will not be sufficient. This is best recognized from a simple example considering the computational degrees of freedom in a MD simulation. Assuming that reliable, multicomponent interatomic potentials existed for the nuclear fuel rod and cladding in a nuclear power plant and that a constant time-step of 2 x 10~15s could sufficiently capture the physics of high-energy atomic collisions to conserve energy; then to simulate 1 day of evolution of 1 cm tall, 1 cm diameter fuel pellet clad and zirconium clad would require ^6 x 1022 atoms for ^4 x 1019 time — steps. For comparison, the LAMPPS MD code using classical force fields has been benchmarked with 40 billion atoms (4 x 1010) and 100 time-steps on 10 000 processors of the RedStorm at Sandia National Laboratory with a wall clock time of 980 s and on 64 000 processors of the BlueGene Light at Lawrence Livermore National Laboratory with a wall clock time of 585 s.103 Thus, even assuming optimistic scaling and parallelization, brute force atomistic simulation of the first full power day of a nuclear fuel pellet in a reactor by MD will remain well beyond the reach of high performance comput­ing capabilities for the next decade.

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