Thermochemical and Continuum Modeling to Understand the Chemical Composition of PWR Fuel CRUD

Computational modeling of Chalk River Undesirable Deposits (CRUD) allows for the prediction of associated phenomena that impact nuclear power plant performance, reliability, and safety. It also provides insight into the physical mechanisms by which CRUD forms and affects plant performance. A major concern in pressurized water reactors (PWRs) is Axial Offset Anomaly (AOA) which is caused by CRUD’s proficiency at trapping boron within the reactor core. The ability to predict AOA and other phenomena requires a detailed explanation of the chemical composition of CRUD. By pairing computational models that can simulate the structure and species trapping with detailed thermochemical models, the compounds that makeup CRUD are determined. Among these thermodynamically predicted compounds is Ni2FeBO5, a mineral named bonaccordite, the formation of which provides a boron retention mechanism. Accordingly, bonaccordite has been found in CRUD samples from fuel linked to very extreme AOA. In this dissertation, thermochemical models are detailed for PWR primary loop chemistry up to the saturation temperature and are implemented using CALPHAD modeling. Likely solid precipitation reactions are identified, and those reactions are incorporated into the multiphysics continuum modeling code MAMBA. An assessment of the kinetic rates of the reactions are determined by Bayesian calibration of the MAMBA model using observational data from CRUD samples. The modeling is able to demonstrate the composition of CRUD scrapes obtained from plant data. This model contributes to the understanding of CRUD formation and composition and allows for the prediction of phenomena such as AOA

Data-driven methods for diffusivity prediction in nuclear fuels

The growth rate of structural defects in nuclear fuels under irradiation is intrinsically related to the diffusion rates of the defects in the fuel lattice. The generation and growth of atomistic structural defects can significantly alter the performance characteristics of the fuel. This alteration of functionality must be accurately captured to qualify a nuclear fuel for use in reactors. Predicting the diffusion coefficients of defects and how they impact macroscale properties such as swelling, gas release, and creep is therefore of significant importance in both the design of new nuclear fuels and the assessment of current fuel types. In this article, we apply data-driven methods focusing on machine learning (ML) to determine various diffusion properties of two nuclear fuels, uranium oxide and uranium nitride. We show that using ML can increase, often significantly, the accuracy of predicting diffusivity in nuclear fuels in comparison to current analytical models. We also illustrate how ML can be used to quickly develop fuel models with parameter dependencies that are more complex and robust than what is currently available in the literature. These results suggest there is potential for ML to accelerate the design, qualification, and implementation of nuclear fuels

Extreme Ultraviolet Reflective Grating Characterization and Simulationsfor the Aspera SmallSat Mission

The Aspera SmallSat mission is designed to detect and map the warm-hot gaseous component of the halos of nearby galaxies through long-slit spectroscopy of the ionized O VI emission line (103.2 nm) for the first time. The Aspera Rowland circle type spectrograph uses a toroidal grating coated with a multilayer film consisting of aluminum, lithium fluoride, and magnesium fluoride capping to optimize reflectivity in the extreme ultraviolet (EUV) waveband from 103 to 104nm. We discuss the grating characterization test setup at the University of Arizona (UA), which will validate the multilayer coating and grating efficiency in a UV vacuum chamber. We also simulate the reflectivity of the multilayer thin film coating using IMD IDL software to compare simulated results with measured reflectivity. Additionally, non-sequential ray trace simulations and 3D CAD modeling are used for verification of the test setup. Finally, the implications of the differences between the measured and simulated reflectivity and grating efficiencies are considered, including impact to the mission

Dynamic Modelling under Uncertainty: The Case of Trypanosoma brucei Energy Metabolism

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies

OSIRIS-REx Encounters Bennu: Initial Assessment from the Approach Phase

The OSIRIS-REx spacecraft launched on September 8, 2016, on a seven-year journey to return samples from asteroid (101955) Bennu. This presentation summarizes the scientific results from the Approach and Preliminary Survey phases. Bennu observations are set to begin on August 17, 2018,when the asteroid is bright enough for detection by the PolyCam. PolyCam and MapCam collect data to survey the asteroid environment for any hazards and characterize the asteroid point-source photometric properties. Resolved images acquired during final approach, starting in late October 2018, allow the creation of a shape model using stereophotoclinometry (SPC), needed by both the navigation team and science planners. The OVIRS and OTES spectrometers characterize the point- source spectral properties over a full rotation period, providing a first look at any features and thermophysical properties. TAGSAM is released from the launch container and deployed into the sampling configuration then returned to the stow position.Preliminary Survey follows the Approach Phase in early December 2018. This phase consists of a series of hyperbolic trajectories that cross over the North and South poles and the equator of Bennu at a close-approach distance of 7 km. Images from these Preliminary Survey passes provide data to complete the 75-cm resolution SPC global shape model and solve for the rotation state. Once the shape model is complete, the asteroid coordinate system is defined for co-registration of all data products. These higher-resolution images also constrain the photometric properties and allow for an initial assessment of the geology. In Preliminary Survey the team also obtains the first OLA data, providing a measure of the surface topography. OVIRS and OTES collect data as "ride-along" instruments, with the spacecraft pointing driven by imaging constraints. These data provide a first look at the spectral variation across the surface of Bennu. Radio science measurements, combined with altimetry and imagery, determine Bennu's mass, a prerequisite to placing the spacecraft into orbit in late December 2018. Together, data from the Approach and Preliminary Survey phases set the stage for the extensive mapping planned for 2019. These dates are the baseline plan. Any contingency or unexpected discovery may change this mission profile

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe