A new age of fuel performance code criteria studied through advanced atomistic simulation techniques

A fundamental step in understanding the effects of irradiation on metallic uranium and uranium dioxide ceramic fuels, or any material, must start with the nature of radiation damage on the atomic level. The atomic damage displacement results in a multitude of defects that influence the fuel performance. Nuclear reactions are coupled, in that changing one variable will alter others through feedback. In the field of fuel performance modeling, these difficulties are addressed through the use of empirical models rather than models based on first principles. Empirical models can be used as a predictive code through the careful manipulation of input variables for the limited circumstances that are closely tied to the data used to create the model. While empirical models are efficient and give acceptable results, these results are only applicable within the range of the existing data. This narrow window prevents modeling changes in operating conditions that would invalidate the model as the new operating conditions would not be within the calibration data set. This work is part of a larger effort to correct for this modeling deficiency. Uranium dioxide and metallic uranium fuels are analyzed through a kinetic Monte Carlo code (kMC) as part of an overall effort to generate a stochastic and predictive fuel code. The kMC investigations include sensitivity analysis of point defect concentrations, thermal gradients implemented through a temperature variation mesh-grid, and migration energy values. In this work, fission damage is primarily represented through defects on the oxygen anion sublattice. Results were also compared between the various models. Past studies of kMC point defect migration have not adequately addressed non-standard migration events such as clustering and dissociation of vacancies. As such, the General Utility Lattice Program (GULP) code was utilized to generate new migration energies so that additional non-migration events could be included into kMC code in the future for more comprehensive studies. Defect energies were calculated to generate barrier heights for single vacancy migration, clustering and dissociation of two vacancies, and vacancy migration while under the influence of both an additional oxygen and uranium vacancy.

An examination of the dynamic validities of cognitive ability and personality measures with performance

Prior research shows that both cognitive ability (Schmidt & Hunter, 1998) and personality measures (Poropat, 2009; Hough & Furnham, 2003) are valid predictors of job performance. The dynamic nature of the relationships between cognitive ability and personality measures with performance over time spent on the job is less understood and thus this paper explores their relationships. Although there is much research to suggest that the predictive relationship between cognitive ability and performance decreases over years of tenure (e.g., Hulin, Henry, & Noon, 1990), other research suggests that the relationship between cognitive ability and performance will increase over time (Kolz, McFarland, & Silverman, 1988). In regard to personality, this study provides a critical test of two competing theories. The first position holds that the validity of personality degrades over time. Support for this position comes from the “ubiquitous” nature of the simplex pattern in individual differences (Humphreys, 1985). It follows that personality validities should perform like cognitive ability in this respect, and thus decline over time. In contrast to this viewpoint, the alternative position contends that the predictive relationship between personality variables and performance increases over time, with the correlation becoming larger in magnitude and more positive in direction over years of tenure. The results of this study support the latter position; personality validities predicted long term performance outcomes.