147 research outputs found

    Reactor hot spot analysis

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    The principle methods for performing reactor hot spot analysis are reviewed and examined for potential use in the Applied Physics Division. The semistatistical horizontal method is recommended for future work and is now available as an option in the SE2-ANL core thermal hydraulic code. The semistatistical horizontal method is applied to a small LMR to illustrate the calculation of cladding midwall and fuel centerline hot spot temperatures. The example includes a listing of uncertainties, estimates for their magnitudes, computation of hot spot subfactor values and calculation of two sigma temperatures. A review of the uncertainties that affect liquid metal fast reactors is also presented. It was found that hot spot subfactor magnitudes are strongly dependent on the reactor design and therefore reactor specific details must be carefully studied. 13 refs., 1 fig., 5 tabs

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

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    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

    Peptide Cotransmitters as Dynamic, Intrinsic Modulators of Network Activity

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    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
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