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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Shifted μ -hybrid inflation, gravitino dark matter, and observable gravity waves

We investigate supersymmetric hybrid inflation in a realistic model based on the gauge symmetry SU(4) c × SU(2) L × SU(2) R . The minimal supersymmetric standard model (MSSM) μ term arises, following Dvali, Lazarides, and Shafi, from the coupling of the MSSM electroweak doublets to a gauge singlet superfield which plays an essential role in inflation. The primordial monopoles are inflated away by arranging that the SU(4) c × SU(2) L × SU(2) R symmetry is broken along the inflationary trajectory. The interplay between the (above) μ coupling, the gravitino mass, and the reheating following inflation is discussed in detail. We explore regions of the parameter space that yield gravitino dark matter and observable gravity waves with the tensor-to-scalar ratio r ~ 10 –4 –10 –3 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

CRDIAC: Coupled Reactor Depletion Instrument with Automated Control

When modeling the behavior of a nuclear reactor over time, it is important to understand how the isotopes in the reactor will change, or transmute, over that time. This is especially important in the reactor fuel itself. Many nuclear physics modeling codes model how particles interact in the system, but do not model this over time.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1983

Nuclear winter - Global consequences of multiple nuclear explosions

The results of a computerized simulation of the potential global environmental effects of dust and smoke clouds that would be generated by a nuclear war are presented. Short term effects of blast, fire, and radiation are neglected in the series of physical models that include a nuclear war scenario, a particle microphysics model, and a radiative convective model. Account is taken of the altitude-dependent dust, smoke, radioactivity, and NO(x) injections, the temporal evolution of dust and smoke clouds, land and ocean environments, and temperature contrasts. A nuclear exchange would produce thousands of individual smoke and dust clouds rising up to 30 km altitude in the midlatitudes. The smoke, dust, and radioactive debris would cover the entire midlatitudes within 1-2 weeks. The smoke would arise from conflagrations of forests, suburbs, and urban areas. Obscuration of sunlight would induce subfreezing temperatures for several months, disruption of the global circulation patterns, and the arrival of a nuclear winter, followed and accompanied by radioactive fallout, pyrogenic air pollution, and UV-B flux enhancements. It is estimated that a total of only 100 Mtons would be sufficient to plunge the Northern Hemisphere summer to subfreezing temperatures lasting months. Since the probable exchange in a nuclear war would exceed 5000 Mtons, it is expected that many species, including humans, may not survive the war.

Turco, R. P.↗
NASA NTRS2012

Particle Filtering for Model-Based Anomaly Detection in Sensor Networks

A novel technique has been developed for anomaly detection of rocket engine test stand (RETS) data. The objective was to develop a system that postprocesses a csv file containing the sensor readings and activities (time-series) from a rocket engine test, and detects any anomalies that might have occurred during the test. The output consists of the names of the sensors that show anomalous behavior, and the start and end time of each anomaly. In order to reduce the involvement of domain experts significantly, several data-driven approaches have been proposed where models are automatically acquired from the data, thus bypassing the cost and effort of building system models. Many supervised learning methods can efficiently learn operational and fault models, given large amounts of both nominal and fault data. However, for domains such as RETS data, the amount of anomalous data that is actually available is relatively small, making most supervised learning methods rather ineffective, and in general met with limited success in anomaly detection. The fundamental problem with existing approaches is that they assume that the data are iid, i.e., independent and identically distributed, which is violated in typical RETS data. None of these techniques naturally exploit the temporal information inherent in time series data from the sensor networks. There are correlations among the sensor readings, not only at the same time, but also across time. However, these approaches have not explicitly identified and exploited such correlations. Given these limitations of model-free methods, there has been renewed interest in model-based methods, specifically graphical methods that explicitly reason temporally. The Gaussian Mixture Model (GMM) in a Linear Dynamic System approach assumes that the multi-dimensional test data is a mixture of multi-variate Gaussians, and fits a given number of Gaussian clusters with the help of the wellknown Expectation Maximization (EM) algorithm. The parameters thus learned are used for calculating the joint distribution of the observations. However, this GMM assumption is essentially an approximation and signals the potential viability of non-parametric density estimators. This is the key idea underlying the new approach.

Solano, Wanda↗