No abstract

This paper studies the e ective use of information retrieval and machine learning techniques in a new task, event detection and tracking. The objective is to automatically detect novel events from chronologically-ordered streams of news stories, and track events of interest over time. We extended existing supervised learning and unsupervised clustering algorithms to allow document classi cation based on both information content and temporal aspects of events. A task-oriented evaluation was conducted using Reuters and CNN news stories. We found agglomerative document clustering highly e ective (82% in the F 1 measure) for retrospective event detection, and single-pass clustering with time windowing a better choice for on-line alerting of novel events. We also observed robust learning behavior for k-nearest neighbor (kNN) classi cation and a decision-tree approach in event tracking, under the di cult condition when the number of positive training examples is extremely small.

No abstract

Accurately predicting changes in the thermal conductivity of light water reactor UO 2 fuel throughout its lifetime in reactor is an essential part of fuel performance modeling. However, typical thermal conductivity models from the literature are empirical. In this work, we begin to develop a mechanistic thermal conductivity model by focusing on the impact of gaseous fission products, which is coupled to swelling and fission gas release. The impact of additional defects and fission products will be added in future work. The model is developed using a combination of atomistic and mesoscale simulation, as well as analytical models. The impact of dispersed fission gas atoms is quantified using molecular dynamics simulations corrected to account for phonon-spin scattering. The impact of intragranular bubbles is accounted for using an analytical model that considers phonon scattering. The impact of grain boundary bubbles is determined using a simple model with five thermal resistors that are parameterized by comparing to 3D mesoscale heat conduction results. However, when used in the BISON fuel performance code to model four reactor experiments, it produces reasonable predictions without having been fit to fuel thermocouple data.

The stabilities of selected fission products-Xe, Cs, and Sr-are investigated as a function of non-stoichiometry x in UO(2 ± x). In particular, density functional theory (DFT) is used to calculate the incorporation and solution energies of these fission products at the anion and cation vacancy sites, at the divacancy, and at the bound Schottky defect. In order to reproduce the correct insulating state of UO(2), the DFT calculations are performed using spin polarization and with the Hubbard U term. In general, higher charge defects are more soluble in the fuel matrix and the solubility of fission products increases as the hyperstoichiometry increases. The solubility of fission product oxides is also explored. Cs(2)O is observed as a second stable phase and SrO is found to be soluble in the UO(2) matrix for all stoichiometries. These observations mirror experimentally observed phenomena.