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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A General Method for Estimating Zonal Transmission Interface Limits from Nodal Network Data: Preprint

Capacity expansion models for the electric power system often employ zonal (rather than nodal) resolution, necessitating estimates of aggregate power transfer limits across the interfaces between model zones. Interface limits between planning areas are sometimes published, but they are not generalizable to arbitrary zone shapes. There is thus a need for a reproducible method for estimating interface transfer limits (ITLs) between user-defined zones directly from nodal transmission system data. Here, we present a simple method for estimating ITLs using a DC power flow approximation via the power transfer distribution factor (PTDF) matrix. Linear optimization is performed to identify the distribution of power flows that maximizes the total flow on interface-crossing lines, subject to individual line ratings, limits on bus injection/withdrawal, and the relationships among flows, injections, and withdrawals imposed by the PTDF matrix. We demonstrate the application of the method on a 134-zone ~65000-bus system, and we explore the influence of flow direction, contingency level, and zone size on the estimated ITLs. There is significant heterogeneity in the ratio of the ITL to the sum of interface-crossing line ratings, which highlights the importance of accounting for the physical constraints on power flows imposed by Kirchhoff's laws when estimating zonal ITLs.

17 WIND ENERGY↗
DOE OSTI2024

HSW-V v1.0: localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model

Abstract. A new set of standalone parameterizations is presented for simulating the injection, evolution, and radiative forcing by stratospheric volcanic aerosols against an idealized Held–Suarez–Williamson (HSW) atmospheric background in the Energy Exascale Earth System Model version 2 (E3SMv2). In this model configuration (HSW with enabled volcanism, HSW-V), sulfur dioxide (SO2) and ash are injected into the atmosphere with a specified profile in the vertical, and they proceed to follow a simple exponential decay. The SO2 decay is modeled as a perfect conversion to a long-living sulfate aerosol which persists in the stratosphere. All three species are implemented as tracers in the model framework and are transported by the dynamical core's advection algorithm. The aerosols contribute simultaneously to local heating of the stratosphere and cooling of the surface by a simple plane-parallel Beer–Lambert law applied on two zonally symmetric radiation broadbands in the longwave and shortwave ranges. It is shown that the implementation parameters can be tuned to produce realistic temperature anomaly signatures of large volcanic events. In particular, results are shown for an ensemble of runs that mimic the volcanic eruption of Mt. Pinatubo in 1991. The design requires no coupling to microphysical subgrid-scale parameterizations and thus approaches the computational affordability of prescribed aerosol forcing strategies. The idealized simulations contain a single isolated volcanic event against a statistically uniform climate, where no background aerosols or other sources of externally forced variability are present. HSW-V represents a simpler-to-understand tool for the development of climate source-to-impact attribution methods.

Hollowed, Joseph P. (ORCID:0000000286581672)↗
DOE OSTI2024

A Generalized approach to the operationalization of Software Quality Models

Comprehensive measures of quality are a research imperative, yet the development of software quality models is a wicked problem. Definitive solutions do not exist and quality is subjective at its most abstract. Definitional measures of quality are contingent on a domain, and even within a domain, the choice of representative characteristics to decompose quality is subjective. Thus, the operationalization of quality models brings even more challenges. A promising approach to quality modeling is the use of hierarchies to represent characteristics, where lower levels of the hierarchy represent concepts closer to real-world observations. Building upon prior hierarchical modeling approaches, we developed the Platform for Investigative software Quality Understanding and Evaluation (PIQUE). PIQUE surmounts several quality modeling challenges because it allows modelers to instantiate abstract hierarchical models in any domain by leveraging organizational tools tailored to their specific contexts. Here, we introduce PIQUE; exemplify its utility with two practical use cases; address challenges associated with parameterizing a PIQUE model; and describe algorithmic techniques that tackle normalization, aggregation, and interpolation of measurements.

Data aggregation↗
DOE OSTI2021

Hybrid deep learning architecture for general disruption prediction across tokamaks

In this paper, we present a new deep learning disruption prediction algorithm based on important findings from explorative data analysis which effectively allows knowledge transfer from existing devices to new ones, thereby predicting disruptions using very limited disruptive data from the new devices. The explorative data analysis conducted via unsupervised clustering techniques confirms that time-sequence data are much better separators of disruptive and non-disruptive behavior than the instantaneous plasma state data with further advantageous implications for a sequence-based predictor. Based on such important findings, we have designed a new algorithm for multi-machine disruption prediction that achieves high predictive accuracy on the C-Mod (AUC=0.801), DIII-D (AUC=0.947) and EAST (AUC=0.973). tokamaks with limited hyperparameter tuning. Through numerical experiments, we show that boosted accuracy (AUC=0.959) is achieved on EAST predictions by including in the training only 20 disruptive discharges, thousands of non-disruptive discharges from EAST, and combining this with more than a thousand discharges from DIII-D and C-Mod. The improvement of predictive ability obtained by combining disruptive data from other devices is found to be true for all permutations of the three devices. Furthermore, by comparing the predictive performance of each individual numerical experiment, we find that non-disruptive data are machine-specific while disruptive data from multiple devices contain device-independent knowledge that can be used to inform predictions for disruptions occurring on a new device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

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.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

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.

Roehling, Tien↗
DOE OSTI2021

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.

LeGrand, Keith Allen↗