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Results for “general”
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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The Generalization-Stability Tradeoff In Neural Network Pruning
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Geophysical Monitoring System: Generalized F-detector Prototyping.
Abstract not provided.
Smooth Particle Hydrodynamics for Neutron Star Oscillation with General Relativistic effects
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A Predictive Model for Offgas Composition in Waste Glass Melters - Spring 2021 SULI General Audience Abstract
Approximately 56 million gallons of nuclear waste are currently stored in 177 underground storage tanks at the Hanford site in Washington state. A portion of the tank waste is targeted for immobilization by converting the nuclear waste to borosilicate glass through a process called vitrification. During vitrification, emissions are produced in addition to the intended glass product. These emissions, contained in a volume above the molten glass called the plenum, are composed of water vapor, air components, aerosols, particulates, and other gaseous species that result from evaporation, reactions, and air influx. The formation of these gasses causes the surface of the glass to foam. To reduce foaming, sucrose is added to the waste stream before the conversion to glass. Sucrose also reduces the formation of hazardous gasses in the plenum including nitrous oxides, NOx, and carboxides, COx. However, if excess sucrose is present, the reactions that produce the gasses in the plenum will be incomplete, and increased levels of NOx and COx can be observed. The Waste Treatment and Immobilization Plant at the Hanford site is equipped with the technology to treat the plenum emissions. Though many tests have been run to determine the presence of particulates in the plenum emissions, there is does not exist an established method to confidently predict the composition of the gaseous species in the plenum prior to treatment without physical testing. A model was created in the process modeling software Aspen Plus to predict the composition of NOx and COx in the plenum. The model employs a constant stirred tank reactor and user-defined reaction chemistry to determine the plenum composition. To verify the model, the offgas predicted by the model were compared to available offgas data for ten different feed compositions. It is demonstrated that the developed Aspen Plus model is capable of predicting the composition of NOx and COx in the plenum.
System and method for grain refinement and general control of grain morphology in laser additive manufacturing
The present disclosure relates to a system for forming a material layer that may make use of an optical light source for generating an optical beam, and a beam shaping subsystem configured to shape the optical beam to generate a complex beam intensity profile. The complex shaped beam may be used to selectively melt at least portions of a bed of powder particles residing on a substrate during formation of the material layer, as the optical light source is moved. A computer may be used to control the optical light source. The complex beam intensity profile enables control over the microstructure of grains formed during melting of the powder particles as the material layer is formed.
Data-driven delta-generalized labeled multi-bernoulli tracker
A system and method for tracking a plurality of objects. Unlabeled measurement data identifying a plurality of targets corresponding to the plurality of objects is received. A multi-target likelihood function is generated using a persistent target density, a birth target density, and a clutter density. The multi-target likelihood function is used to associate persistent targets with the unlabeled measurement data to update persistent tracks and to initiate a new track for each target in the plurality of targets in the measurement data that is not associated with a persistent target.
Modeling Neutron Star Oscillations in a Fixed General Relativistic Background Including Solid Crust Dynamics
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Plug and play: a general approach for the fusion of sensor and machine learning models
Regularized or Bayesian inversion has revolutionized our ability to reconstruct images from incomplete data.
A general-mass scheme for prompt charm production at hadron colliders
In these proceedings, we apply the recently developed S-ACOT-MPS factorization scheme at the next-to-leading order to prompt charm production at hadron colliders. It provides a good agreement with experimental data on charm meson production measured by LHCb at 7 and 13 TeV. The low-$p_T$ data are on the margins of the theoretical error bands, emphasizing the importance of including contributions beyond the next-to-leading order.
Generalized Regularization of Constrained Optimal Control Problems.
Abstract not provided.
Prove-It: A Proof Assistant for Organizing and Verifying General Mathematical Knowledge.
Abstract not provided.
Generalized Lapse Rate Formulas for Use in Entraining CAPE Calculations
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Domain Generalization with Machine Learning in the NOvA Experiment
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Loss Estimators Improve Model Generalization
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RE-LABELING DOMAINS IMPROVES MULTI-DOMAIN GENERALIZATION
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Multi-Domain Ensembles for Domain Generalization
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