Information technology and data mining for spent fuel treatment

Information technology is being used to provide interactive access to data collected from the electro-metallurgical treatment of spent fuel. The data are results from many hundreds of experiments performed to better characterize the processes by which uranium is separated from the waste products. Web-based display and relational database query capabilities facilitate the identification of trends in the data and the relating of these trends to the underlying electrochemistry. The objectives are to ensure that the process behavior is well understood, to make readily accessible the necessary data for development and validation of models, and to identify unexpected trends in the data as indications of phenomena not yet represented in the models

Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data

Many systems are partially stochastic in nature. We have derived data driven approaches for extracting stochastic state machines (Markov models) directly from observed data. This chapter provides an overview of our approach with numerous practical applications. We have used this approach for inferring shipping patterns, exploiting computer system side-channel information, and detecting botnet activities. For contrast, we include a related data-driven statistical inferencing approach that detects and localizes radiation sources.Comment: Accepted by 2017 International Symposium on Sensor Networks, Systems and Securit

Scalability of the natural convection shutdown heat removal test facility (NSTF) data to VHTR/NGNP RCCS designs.

Passive safety in the Very High Temperature Reactor (VHTR) is strongly dependent on the thermal performance of the Reactor Cavity Cooling System (RCCS). Scaled experiments performed in the Natural Shutdown Test Facility (NSTF) are to provide data for assessing and/or improving computer code models for RCCS phenomena. Design studies and safety analyses that are to support licensing of the VHTR will rely on these models to achieve a high degree of certainty in predicted design heat removal rate. To guide in the selection and development of an appropriate set of experiments a scaling analysis has been performed for the air-cooled RCCS option. The goals were to (1) determine the phenomena that dominate the behavior of the RCCS, (2) determine the general conditions that must be met so that these phenomena and their relative importance are preserved in the experiments, (3) identify constraints specific to the NSTF that potentially might prevent exact similitude, and (4) then to indicate how the experiments can be scaled to prevent distortions in the phenomena of interest. The phenomena identified as important to RCCS operation were also the subject of a recent PIRT study. That work and the present work collectively indicate that the main phenomena influencing RCCS heat removal capability are (1) radiation heat transport from the vessel to the air ducts, (2) the integral effects of momentum and heat transfer in the air duct, (3) buoyancy at the wall inside the air duct giving rise to mixed convection, and (4) multidimensional effects inside the air duct caused by non-uniform circumferential heat flux and non-circular geometry

Peptide Cotransmitters as Dynamic, Intrinsic Modulators of Network Activity

Neurons can contain both neuropeptides and “classic” small molecule transmitters. Much progress has been made in studies designed to determine the functional significance of this arrangement in experiments conducted in invertebrates and in the vertebrate autonomic nervous system. In this review article, we describe some of this research. In particular, we review early studies that related peptide release to physiological firing patterns of neurons. Additionally, we discuss more recent experiments informed by this early work that have sought to determine the functional significance of peptide cotransmission in the situation where peptides are released from neurons that are part of (i.e., are intrinsic to) a behavior generating circuit in the CNS. In this situation, peptide release will presumably be tightly coupled to the manner in which a network is activated. For example, data obtained in early studies suggest that peptide release will be potentiated when behavior is executed rapidly and intervals between periods of neural activity are relatively short. Further, early studies demonstrated that when neural activity is maintained, there are progressive changes (e.g., increases) in the amount of peptide that is released (even in the absence of a change in neural activity). This suggests that intrinsic peptidergic modulators in the CNS are likely to exert effects that are manifested dynamically in an activity-dependent manner. This type of modulation is likely to differ markedly from the modulation that occurs when a peptide hormone is present at a relatively fixed concentration in the blood

Initial VHTR Accident Scenario Classification: Models and Data.

Nuclear systems codes are being prepared for use as computational tools for conducting performance/safety analyses of the Very High Temperature Reactor. The thermal-hydraulic codes are RELAP5/ATHENA for one-dimensional systems modeling and FLUENT and/or Star-CD for three-dimensional modeling. We describe a formal qualification framework, the development of Phenomena Identification and Ranking Tables (PIRTs), the initial filtering of the experiment databases, and a preliminary screening of these codes for use in the performance/safety analyses. In the second year of this project we focused on development of PIRTS. Two events that result in maximum fuel and vessel temperatures, the Pressurized Conduction Cooldown (PCC) event and the Depressurized Conduction Cooldown (DCC) event, were selected for PIRT generation. A third event that may result in significant thermal stresses, the Load Change event, is also selected for PIRT generation. Gas reactor design experience and engineering judgment were used to identify the important phenomena in the primary system for these events. Sensitivity calculations performed with the RELAP5 code were used as an aid to rank the phenomena in order of importance with respect to the approach of plant response to safety limits. The overall code qualification methodology was illustrated by focusing on the Reactor Cavity Cooling System (RCCS). The mixed convection mode of heat transfer and pressure drop is identified as an important phenomenon for Reactor Cavity Cooling System (RCCS) operation. Scaling studies showed that the mixed convection mode is likely to occur in the RCCS air duct during normal operation and during conduction cooldown events. The RELAP5/ATHENA code was found to not adequately treat the mixed convection regime. Readying the code will require adding models for the turbulent mixed convection regime while possibly performing new experiments for the laminar mixed convection regime. Candidate correlations for the turbulent mixed convection regime for circular channel geometry were identified in the literature. We describe the use of computational experiments to obtain correction factors for applying these circular channel results to the specialized channel geometry of the RCCS. The intent is to reduce the number of laboratory experiments required. The FLUENT and Star-CD codes contain models that in principle can handle mixed convection but no data were found to indicate that their empirical models for turbulence have been benchmarked for mixed convection conditions. Separate effects experiments were proposed for gathering the needed data. In future work we will use the PIRTs to guide review of other components and phenomena in a similar manner as was done for the mixed convection mode in the RCCS. This is consistent with the project objective of identifying weaknesses or gaps in the code models for representing thermal-hydraulic phenomena expected to occur in the VHTR both during normal operation and upsets, identifying the models that need to be developed, and identifying the experiments that must be performed to support model development

Incorporating background frequency improves entropy-based residue conservation measures

BACKGROUND: Several entropy-based methods have been developed for scoring sequence conservation in protein multiple sequence alignments. High scoring amino acid positions may correlate with structurally or functionally important residues. However, amino acid background frequencies are usually not taken into account in these entropy-based scoring schemes. RESULTS: We demonstrate that using a relative entropy measure that incorporates amino acid background frequency results in improved performance in identifying functional sites from protein multiple sequence alignments. CONCLUSION: Our results suggest that the application of appropriate background frequency information may lead to more biologically relevant results in many areas of bioinformatics