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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Cluster dynamics models of irradiation damage accumulation in ferritic iron. I. Trap mediated interstitial cluster diffusion

The microstructure that develops under low temperature irradiation in ferritic alloys is dominated by a high density of small (2–5 nm) defects. These defects have been widely observed to move via occasional discrete hops during in situ thin film irradiation experiments. Cluster dynamics models are used to describe the formation of these defects as an aggregation process of smaller clusters created as primary damage. Multiple assumptions regarding the mobility of these damage features are tested in the models, both with and without explicit consideration of such irradiation induced hops. Comparison with experimental data regarding the density of these defects demonstrates the importance of including such motions in a valid model. In particular, discrete hops inform the limited dependence of defect density on irradiation temperature observed in experiments, which the model was otherwise incapable of producing.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Size distribution of possible dust carriers for the extended red emission

Power-law size distributions expected to be applicable to possible carriers of extended red emission (ERE) have been examined using Monte Carlo (MC) simulations. Si nanoparticles and some polycyclic aromatic hydrocarbon complexes such as oligoacene and oligorylenes with energy gaps close to 2 eV have been considered. In the simplest case of unit quantum efficiency, the MC-generated size distributions are used to obtain photoluminescence (PL) spectra that are then corrected for dust extinction and reddening effects for comparison with observed ERE spectra. It is shown that a power-law size distribution with a decay exponent of α = 7/2, which closely agrees with starlight extinction data, fails to produce an ERE-like spectrum. However, size distributions with decay exponents of α = 19/12 and 3/2 are found to lead to acceptable spectra. Results indicate that energetic photon-induced breakup and competing aggregation effects dominate collisional effects in producing the observed steady-state mass distribution. It is shown that the peak wavelength of emission critically depends on the band gap, rather than cluster mass, which for oligoacenes and oligorylenes is widely different. The peak wavelength is also shown to be insensitive to dust attenuation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Behind-the-Meter Solar Generation Disaggregation using Consumer Mixture Models

To facilitate deep penetration of solar energy in smart grids, we need high observability of solar generation at the edges of the grid. Current advanced metering infrastructures (AMI) only monitor the aggregated measurements from net-metered households, but disaggregated consumption and solar generation components are required for grid optimizations. We propose an unsupervised disaggregation model for disaggregating solar generation from AMI measurements without the need of training data. The model requires only AMI measurements from consumers in a region and the solar irradiance as input, and models the consumption of consumers by neighboring households without rooftop photovoltaics (PV) to perform the disaggregation. We evaluate our results on a real life dataset from Austin, Texas. We show that our model is able to disaggregate consumption and solar generation measurements with 42.24% and 31.67% less mean squared error, respectively, in comparison to a baseline technique that uses supervised learning. This shows that our model is capable of disaggregating historical data even if the dataset has no training data and only contains minimal exogenous data.

14 SOLAR ENERGY↗
DOE OSTI2021

RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness - An Introduction to the Open-Source Tool and Use Cases

The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an open-source tool for visualizing variable renewable resource forecasts and ramp alerts for significant up/down ramps in renewable resource and the consequent net-load. The modular dashboard of RAVIS contains configurable panes for viewing- probabilistic time series forecasts, ramp event alerts on the look-ahead timeline, spatially resolved resource sites and forecasts, and system simulation and market clearing data such as transmission lines utilization, nodal prices and available generation flexibility. RAVIS uses a technology suite that is assembled to provide optimum visualization facility while maintaining a wide pool of potential deployment and client environments. The tool is designed to take advantage of web application technologies, open source visualization libraries and tooling. Utilizing this technology will enable deployment in any environment, using any operating system, and is scalable to much higher spatial and temporal scales of visualization. As a prototype of the tool and demonstrating a use case of variable renewable integration, RAVIS currently integrates site-specific solar power forecasts in the California Independent System Operator (CAISO) and Mid-continent ISO (MISO) footprint from the IBM WattSun forecasting platform, and also superimposes market simulation data for CAISO footprint from as in-house NREL market clearing tool. The tool has the ability to alert the viewer for excessive up or down ramps for both individual solar sites as well as regionally aggregated net-load ramps, and alerts can also be qualified with respect to available flexible generation. This report will provide an introduction to the RAVIS tool, and summarize the above mentioned capabilities, typical use cases and possible extensions of the tool. The RAVIS development team believes there are likely to be high economic and reliability benefits of integrating probabilistic forecasts of variable renewables into control center visualizations and improved ramp events situational awareness for system operators and forecasting teams in the ISOs and electric utilities in comparison with their business as usual practices.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Analytics for Investigation of Disease Outbreaks (AIDO)

Objective: Analytics for the Investigation of Disease Outbreaks (AIDO) is a web-based tool designed to enhance a user’s understanding of unfolding infectious disease events. A representative library of over 650 outbreaks across a wide selection of diseases allows similar outbreaks to be matched to the conditions entered by the user. These historic outbreaks contain detailed information on how the disease progressed as well as what measures were implemented to control its spread, allowing for a better understanding within the context of other outbreaks.Introduction: Situational awareness, or the understanding of elemental components of an event with respect to both time and space, is critical for public health decision-makers during an infectious disease outbreak. AIDO is a web-based tool designed to contextualize incoming infectious disease information during an unfolding event for decision-making purposes.Methods: Public health analysts of the Biology Division at Los Alamos National Laboratory curated a diverse library of historic disease outbreaks from publicly available official reports and peer reviewed literature to serve as a representation of the range of potential outbreak scenarios for a given disease. Available outbreak metadata are used to identify properties that relate to the magnitude and/or duration of the outbreak. Properties vary by disease, as they are related to disease-specific characteristics like transmission, disease manifestation, risk factors related to disease severity, and environmental factors specific to the given location. These properties are then incorporated into a similarity algorithm (s in Figure 1) to identify outbreaks that are similar to user inputs.Results: AIDO currently includes libraries for 39 diseases that are diverse across pathogen type (viral, bacterial and parasitic) as well as transmission type (vectorborne (e.g., Dengue, Malaria), foodborne (e.g., Salmonella, Campylobacteriosis), waterborne (e.g., Cholera), and person-to-person transmitted (e.g., Measles)). In addition to providing a similarity score to the user’s outbreak, we provide aggregated comparisons to multiple historical outbreaks, descriptive statistics to show the distribution of property values for each disease, and extensive contextual information about each outbreak.Conclusions: The analytics provided by AIDO allow users to interact with a unique data set of historic outbreaks and the associated metadata to contextualize incoming information and generate hypotheses about appropriate decisions. The tool is continually updated with new functionalities and additional data.

Daughton, Ashlynn↗
DOE OSTI2014

Field Trial of a Low-Cost, Distributed Plug Load Monitoring System

Researchers have struggled to inventory and characterize the energy use profiles of the ever-growing category of so-called miscellaneous electric loads (MELs) because plug-load monitoring is cost-prohibitive to the researcher and intrusive to the homeowner. However, these data represent a crucial missing link to our understanding of how homes use energy, and we cannot control what we do not understand. Detailed energy use profiles would enable the nascent automated home energy management (AHEM) industry to develop effective control algorithms that target consumer electronics and other plug loads. If utility and other efficiency programs are to incent AHEM devices, they need large-scale datasets that provide statistically meaningful justification of their investments by quantifying the aggregate energy savings achievable. To address this need, we have investigated a variety of plug-load measuring devices available commercially and tested them in the laboratory to identify the most promising candidates for field applications. The scope of this report centers around the lessons learned from a field validation of one proof-of-concept system, called Smartenit (formerly SimpleHomeNet). The system was evaluated based on the rate of successful data queries, reliability over a period of days to weeks, and accuracy. This system offers good overall performance when deployed with up to ten end nodes in a residential environment, although deployment with more nodes and in a commercial environment is much less robust. We conclude that the current system is useful in selected field research projects, with the recommendation that system behavior is observed over time.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Minimizing development costs for efficient many-core visualization using MCD3

Scientific visualization software increasingly needs to support many-core architectures. However, development time is a significant challenge due to the breadth and diversity of both visualization algorithms and architectures. With this work, we introduce a development environment for visualization algorithms on many-core devices that extends the traditional data-parallel primitive (DPP) approach with several existing constructs and an important new construct: meta-DPPs. We refer to our approach as MCD 3 — Meta-DPPs, Convenience routines, Data management, DPPs, and Devices. The twin goals of MCD 3 are to reduce developer time and to deliver efficient performance on many-core architectures, and our evaluation considers both of these goals. For development time, we study 57 algorithms implemented in the VTK-m software library and determine that MCD 3 leads to significant savings. For efficient performance, we survey ten studies looking at individual algorithms and determine that the MCD 3 hardware-agnostic approach leads to performance comparable to hardware-specific approaches: sometimes better, sometimes worse, and better in the aggregate. In total, we find that MCD 3 is an effective approach for scientific visualization libraries to support many-core architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Grandmaster: Interactive text-based analytics of social media

People use social media resources like Twitter, Facebook, forums etc. to share and discuss various activities or topics. By aggregating topic trends across many individuals using these services, we seek to construct a richer profile of a person’s activities and interests as well as provide a broader context of those activities. This profile may then be used in a variety of ways to understand groups as a collection of interests and affinities and an individual’s participation in those groups. Our approach considers that much of these data will be unstructured, free-form text. By analyzing free-form text directly, we may be able to gain an implicit grouping of individuals with shared interests based on shared conversation, and not on explicit social software linking them. In this paper, we discuss a proof-of-concept application called Grandmaster built to pull short sections of text, a person’s comments or Twitter posts, together by analysis and visualization to allow a gestalt understanding of the full collection of all individuals: how groups are similar and how they differ, based on their text inputs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model

The Weather Research and Forecast (WRF) model downscaling skill in extreme maximum daily temperature is evaluated by using the generalized extreme value (GEV) distribution. While the GEV distribution has been used extensively in climatology and meteorology for estimating probabilities of extreme events, accurately estimating GEV parameters based on data from a single pixel can be difficult, even with fairly long data records. This work proposes a simple method assuming that the shape parameter, the most difficult of the three parameters to estimate, does not vary over a relatively large region. This approach is applied to evaluate 31-year WRF-downscaled extreme maximum temperature through comparison with North American Regional Reanalysis (NARR) data. Uncertainty in GEV parameter estimates and the statistical significance in the differences of estimates between WRF and NARR are accounted for by conducting bootstrap resampling. Despite certain biases over parts of the United States, overall, WRF shows good agreement with NARR in the spatial pattern and magnitudes of GEV parameter estimates. Both WRF and NARR show a significant increase in extreme maximum temperature over the southern Great Plains and southeastern United States in January and over the western United States in July. The GEV model shows clear benefits from the regionally constant shape parameter assumption, for example, leading to estimates of the location and scale parameters of the model that show coherent spatial patterns.

Wang, Jiali↗
DOE OSTI2014

Precipitation hydrometeor type relative to the mesoscale airflow in mature oceanic deep convection of the Madden-Julian Oscillation

We present that composite analysis of mature near-equatorial oceanic mesoscale convective systems (MCSs) during the active stage of the Madden-Julian Oscillation (MJO) shows where different hydrometeor types occur relative to convective updraft and stratiform midlevel inflow layers. The National Center for Atmospheric Research (NCAR) S-PolKa radar observed these MCSs during the Dynamics of the Madden-Julian Oscillation/Atmospheric Radiation Measurement-MJO Investigation Experiment (DYNAMO/AMIE). NCAR's particle identification algorithm (PID) is applied to S-PolKa's polarimetric data to identify the dominant hydrometeor type in each radar sample volume. Combining S-PolKa's Doppler velocity data with the PID demonstrates that hydrometeors have a systematic relationship to the airflow within mature MCSs. In the convective region, moderate rain occurs within the updraft core; the heaviest rain occurs just downwind of the core; wet aggregates occur immediately below the melting layer; narrow zones containing graupel/rimed aggregates occur just downstream of the updraft core at midlevels; dry aggregates dominate above the melting level; and smaller ice particles occur along the edges of the convective zone. In the stratiform region, rain intensity decreases toward the anvil; melting aggregates occur in horizontally extensive but vertically thin regions at the melting layer; intermittent pockets of graupel/rimed aggregates occur atop the melting layer; dry aggregates and small ice particles occur sequentially above the melting level; and horizontally oriented ice crystals occur between -10°C and -20°C in turbulent air above the descending midlevel inflow, suggesting enhanced depositional growth of dendrites. Finally, the organization of hydrometeors within the midlevel inflow layer is insensitive to the presence or absence of a leading convective line.

58 GEOSCIENCES↗
DOE OSTI2018

ARC algorithm: A novel approach to forecast and manage daily electrical maximum demand

This paper proposes an innovative algorithm for predicting short-term electrical maximum demand by using historical demand data. The ability to recognize in peak demand pattern for commercial or industrial customers would propose numerous direct and indirect benefits to the customers and utility providers in terms of demand reduction, cost control, and system stability. Prior works in electrical maximum demand forecasting have been mainly focused on seasonal effects, which is not a feasible approach for industrial manufacturing facilities in short-term load forecasting. The proposed algorithm, denoted as the Adaptive Rate of Change (ARC), determines the logarithmic rate-of-change in load profile prior to a peak by postulating the demand curve as a stochastic, mean-reverting process. The rationale behind this analysis, is that the energy efficient program requires not only demand estimation but also to warn the user of imminent maximum peak occurrence. This paper analyzes demand trend data and incorporates stochastic model and mean reverting half-life to develop an electrical maximum demand forecasting algorithm, which is statistically evaluated by cross-table and F-score for three different manufacturing facilities. Lastly, the aggregate results show an overall accuracy of 0.91 and a F-score of 0.43, which indicates that the algorithm is effective predicting peak demand in predicting peak demand.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

DFAT: A web-based toolkit for estimating demand flexibility in building-to-grid integration

Demand Flexibility Assessment Tool (DFAT) is an open source web-based tool that estimates the demand flexibility potential of common control strategies in commercial buildings. The toolkit features a demand flexibility estimation tool that contains two calculators, basic and advanced, based on the level of input of customer data. The basic version calculates demand shed metrics for the control strategy “global temperature adjustment” and “cycle on/off compressors” using customer building information, local weather data, and electrical meter data. The advanced version, which uses detailed HVAC equipment data, calculates demand flexibility metrics for control strategies such as static pressure reset, global temperature adjustment, and cycle on/off compressors. In addition to the demand flexibility estimation tool, this toolkit offers a benchmarking tool that helps facility operators, aggregators, and utility resource managers assess demand flexibility opportunities, quantify/verify performance, and compare their performance against that of their peers.

Leong, Michael↗
DOE OSTI2014

Fibroblasts maintained in 3 dimensions show a better differentiation state and higher sensitivity to estrogens

Cell differentiation and response to hormonal signals were studied in a 3D environment on an in-house generated mouse fibroblast cell line expressing a reporter gene under the control of estrogen responsive sequences (EREs). 3D cell culture conditions were obtained in a Rotary Cell Culture System; (RCCS™), a microgravity based bioreactor that promotes the aggregation of cells into multicellular spheroids (MCS). In this bioreactor the cells maintained a better differentiated phenotype and more closely resembled in vivo tissue. The RCCS™ cultured fibroblasts showed higher expression of genes regulating cell assembly, differentiation and hormonal functions. Microarray analysis showed that genes related to cell cycle, proliferation, cytoskeleton, migration, adhesion and motility were all down-regulated in 3D as compared to 2D conditions, as well as oncogene expression and inflammatory cytokines. Controlled remodeling of ECM, which is an essential aspect of cell organization, homeostasis and tissue was affected by the culture method as assessed by immunolocalization of β-tubulin. Markers of cell organization, homeostasis and tissue repair, metalloproteinase 2 (MMP2) and its physiological inhibitor (TIMP4) changed expression in association with the relative formation of cell aggregates. The fibroblasts cultured in the RCCS™ maintain a better responsiveness to estrogens, measured as expression of ERα and regulation of an ERE-dependent reporter and of the endogenous target genes CBP, Rarb, MMP1 and Dbp. Our data highlight the interest of this 3D culture model for its potential application in the field of cell response to hormonal signals and the pharmaco-toxicological analyses of chemicals and natural molecules endowed of estrogenic potential. - Highlights: • We here characterized the first cell line derived from an estrogen reporter mouse. • In the RCCS cells express an immortalized behavior but not a transformed phenotype. • The RCCS provides a system for maintaining cells in more physiological conditions. • RCCS-cultured fibroblasts showed higher hormonal sensitivity to estradiol. • This bioreactor is a novel 3D model to be applied to pharmacotoxicological studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Use of a Stereochemical Strategy To Probe the Mechanism of Phenol-Soluble Modulin $α$3 Toxicity

Phenol-soluble modulin α3 (PSMα3) is a cytotoxic peptide secreted by virulent strains of Staphylococcus aureus . Here, we used a stereochemical strategy to examine the mechanism of PSMα3-mediated toxicity. One hypothesis is that PSMα3 toxicity requires fibril formation; an alternative is that toxicity is caused by soluble forms of PSMα3, possibly oligomeric. We find that the unnatural enantiomer (D residues) displays cytotoxicity comparable to that of L-PSMα3. Racemic PSMα3 is similarly toxic to enantiopure PSMα3 (L or D) under some conditions, but the toxicity is lost under conditions that cause racemic PSMα3 to aggregate. A crystal structure of racemic PSMα3-NH2 displays an α-helical secondary structure and a packing pattern that is reminiscent of the cross-α arrangement recently discovered in crystals of L-PSMα3. Our data suggest that the cytotoxicity of PSMα3 does not depend on stereospecific engagement of a target protein or other chiral macromolecule, an observation that supports a mechanism based on membrane disruption. In addition, our data support the hypothesis that toxicity is exerted by a soluble form rather than an insoluble fibrillar form.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification

Abstract To bolster the accuracy of existing methods for automated phase identification from X-ray diffraction (XRD) patterns, we introduce a machine learning approach that uses a dual representation whereby XRD patterns are augmented with simulated pair distribution functions (PDFs). A convolutional neural network is trained directly on XRD patterns calculated using physics-informed data augmentation, which accounts for experimental artifacts such as lattice strain and crystallographic texture. A second network is trained on PDFs generated via Fourier transform of the augmented XRD patterns. At inference, these networks classify unknown samples by aggregating their predictions in a confidence-weighted sum. We show that such an integrated approach to phase identification provides enhanced accuracy by leveraging the benefits of each model’s input representation. Whereas networks trained on XRD patterns provide a reciprocal space representation and can effectively distinguish large diffraction peaks in multi-phase samples, networks trained on PDFs provide a real space representation and perform better when peaks with low intensity become important. These findings underscore the importance of using diverse input representations for machine learning models in materials science and point to new avenues for automating multi-modal characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Atomistic structural ensemble refinement reveals non-native structure stabilizes a sub-millisecond folding intermediate of CheY

The dynamics of globular proteins can be described in terms of transitions between a folded native state and less-populated intermediates, or excited states, which can play critical roles in both protein folding and function. Excited states are by definition transient species, and therefore are difficult to characterize using current experimental techniques. We report an atomistic model of the excited state ensemble of a stabilized mutant of an extensively studied flavodoxin fold protein CheY. We employed a hybrid simulation and experimental approach in which an aggregate 42 milliseconds of all-atom molecular dynamics were used as an informative prior for the structure of the excited state ensemble. The resulting prior was then refined against small-angle X-ray scattering (SAXS) data employing an established method (EROS). The most striking feature of the resulting excited state ensemble was an unstructured N-terminus stabilized by non-native contacts in a conformation that is topologically simpler than the native state. We then predict incisive single molecule FRET experiments, using these results, as a means of model validation. Our study demonstrates the paradigm of uniting simulation and experiment in a statistical model to study the structure of protein excited states and rationally design validating experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Workplace Charge Management with Aggregated Building Loads

This paper describes a workplace charge management system developed to control plug-in electric vehicle charging stations based on aggregated building loads. A system to collect information from drivers was also developed for better charge management performance since the present AC charging station standard does not provide battery state of charge information. First, simulations with uncontrolled charging data were conducted to investigate several scenarios and control methods, and then one method with the most power curtailment during peak load was selected for verification tests. This paper illustrates load reduction test results for 36 charging stations and real-time campus net load data.

25 ENERGY STORAGE↗
DOE OSTI2016

TH-EF-BRB-06: Implementation of a Modulated-Arc Total Body Irradiation (TBI) Technique Using the RayStation Treatment Planning System

Purpose: To develop a clinical workflow for delivering a modulated-arc total body irradiation (TBI) with RayStation scripting. This technique uses arc fields with the patient lying at floor level on a padded table and is validated through measurements taken on a custom-made TBI phantom. Methods: Treatment planning was performed for a retrospective cohort of eight patients with a diverse range of heights and body types. Each was replanned using an open-field dual arc method, with the patient in supine and prone positions on the floor of the vault. All plans were optimized using Raystation Planning 4.7.2.5 (RaySearch Laboratories, Stockholm, Sweden), with 200 cGy prescribed to the 95% of the body contour − 5mm. This results in an open-field beam that sweeps craniocaudally across the length of the patient. The technique is validated with measurements at 10 cm intervals in a custom-milled, 5 cm thick acrylic phantom. A centrally located CC13 ion chamber and a Mobile MOSFET (Best Medical Canada, Ottawa, ON) detector array were used to measure dose. Supine and prone arcs for each patient were consecutively delivered, and the aggregate dose at each point was compared to the planned dose calculated in the phantom. Results: The ion chamber measurements differed from the planned dose by an average of .5%, with a standard deviation of 2.1%. All measured data for the MOSFETS were within 10% of the corresponding planned dose except for two outlying points. The standard deviation of dose differences across the entire cohort was 4.0%. Most significant discrepancies occurred either in inhomogeneous regions with large gradients, or at inferior points where beam angle was steepest. Conclusion: We have confirmed that the planned dose is well matched to our measurements within 10% for this method of planning and delivery. We are currently incorporating this technique into our clinical workflow. This work is supported by RaySearch.

60 APPLIED LIFE SCIENCES↗