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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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NASA NTRSDate not supplied

Populating a Graph Database to Run a Usage-Based Discovery Tool

Most dataset discovery tools for Earth Observation data rely on descriptions and other metadata of the datasets, using keyword searches or attribute filtering to determine relevance. However, these descriptions often do not include the potential uses of the data. Thus, a user working on floods will rarely see few if any rainfall datasets show up in such a search. The Usage Based Discovery tool, on the other hand, offers usage instances to the user, either research articles or applications, along with the datasets that those usage instances used. This allows a user, particularly one new to the world of Earth Observation data, to investigate which datasets are used in similar cases. The information that powers Usage-Based Discovery is a graph database of relationships of usage to dataset and usage to topic, allowing the user to narrow their search for similar cases. In order to scale out to a graph database rich enough to provide a satisfactory user experience, we combine manual and automated processes to populate the graph. The initial content of the graph has been seeded primarily via human-aided data curation methods, using sites like Google Scholar. To scale up this effort, we’ve employed crowdsourcing. It is easy for anyone to contribute to our graph using their Open Researcher and Contributor Identifier for authorization. We’re now experimenting with Machine Learning and Natural Language Processing to help automate population of the graph, starting with the classification of research articles by topic. Finding adequate training data in the absence of a comprehensive and open research article API continues to be a significant challenge.

Vincent Inverso↗
DOE OSTI2018

Thermosyphon Cooler Hybrid System for Water Savings in an Energy-Efficient HPC Data Center: Results from 24 Months and the Impact on Water Usage Effectiveness

In August 2016, the National Renewable Energy Laboratory (NREL) installed a thermosyphon hybrid cooling system to reduce water usage in its already extremely energy-efficient High-Performance Computing (HPC) Data Center. In its first year of use, the system saved 4,400 m3 (1.16 million gal) of water, and 7,950 m3 (2.10 million gal) during a 2-year period, cutting the use of water in the data center by about one-half. NREL's 930-m2 (10,000-ft2) HPC Data Center is often called the most energy-efficient data center in the world: it has achieved a trailing 12-month average power usage effectiveness of 1.034, and it features a chiller-less design, component-level warm-water liquid cooling, and waste heat capture and reuse. NREL considered the amount of water used by the cooling towers to be counter to the laboratory's sustainability mission, so a team of researchers from NREL, Sandia National Laboratories (Sandia), and Johnson Controls integrated the BlueStream thermosyphon cooler (TSC) - an advanced dry cooler that uses refrigerant in a passive cycle to dissipate heat - on the roof of NREL's Energy Systems Integration Facility, the building that houses the HPC Data Center. In combination with the existing cooling towers, the TSC forms an extremely water- and cost-efficient cooling system. In its first year of operation, on-site water usage effectiveness (WUE) was 0.70 L/kWh. In comparison, the WUE would be 1.27 L/kWh if NREL had continued using only heat-recovery and cooling towers. This on-site water savings was accomplished without negatively impacting the energy-efficient operation of the HPC Data Center. The TSC system technology has the potential for application in data centers around the world, and it is currently being implemented by Sandia. center by about one-half. NREL's 930-m2 (10,000-ft2) HPC Data Center is often called the most energy-efficient data center in the world: it has achieved a trailing 12-month average power usage effectiveness of 1.034, and it features a chiller-less design, component-level warm-water liquid cooling, and waste heat capture and reuse. NREL considered the amount of water used by the cooling towers to be counter to the laboratory's sustainability mission, so a team of researchers from NREL, Sandia National Laboratories (Sandia), and Johnson Controls integrated the BlueStream thermosyphon cooler (TSC) - an advanced dry cooler that uses refrigerant in a passive cycle to dissipate heat - on the roof of NREL's Energy Systems Integration Facility, the building that houses the HPC Data Center. In combination with the existing cooling towers, the TSC forms an extremely water- and cost-efficient cooling system. In its first year of operation, on-site water usage effectiveness (WUE) was 0.70 L/kWh. In comparison, the WUE would be 1.27 L/kWh if NREL had continued using only heat-recovery and cooling towers. This on-site water savings was accomplished without negatively impacting the energy-efficient operation of the HPC Data Center. The TSC system technology has the potential for application in data centers around the world, and it is currently being implemented by Sandia.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Applying Graph Analytics to Understand Compute Core Usage and Publication Trends in a Petascale Supercomputing Facility

The Oak Ridge Leadership Computing Facility (OLCF) runs Titan, the No. 4 supercomputer in the world, to deliver over four billion compute core hours every year to several scientific domains, in their pursuit of leadership science. In this paper, we analyze four years worth of heterogeneous log data sources from the OLCF resource fabric, capturing metadata on entities such as users (2,546), scientific project allocations (674), jobs (1,352,402) and publications (1,146), to derive insights into the trends in core hour usage and publications, across 35 science domains. We have constructed a scalable graph to represent the OLCF entities and apply rich graph analytics for our analysis. Based on this, we have analyzed the metadata across five dimensions, namely (1) quantitative analysis of Titan system usage, (2) quantitative analysis of OLCF publications, (3) correlation analysis between system usage and publications, (4) text analysis to derive OLCF research trends, and (5) utilization of graph mining for association analysis. To the best of our knowledge, our work is the first of its kind to apply graph- based big data techniques to provide comprehensive insights on an HPC center's core hour usage and users' publication trends. Our results provide valuable details into an HPC center's core allocation program, measuring the productivity of scientific domains, the interplay between core usage and research output, accelerating collaboration, and in predicting new connections between resource entities.

Lee, Sangkeun (Matt)↗
DOE OSTI2013

Hierarchical Cluster Analysis of Service Usage and Geographic Variation in Medicare Spending

Per capita Medicare spending varies considerably throughout the Unites States, even after taking into account factors such as cost of living and pre-existing conditions of the Medicare populations. Additionally, per capita spending may vary up to four times as much under Medicare as it does under private insurance. Several studies have explored the Medicare-costs differences between regions with respect to how Medicare services are used. The recent public release of a large, relatively fine-grained set of Medicare data over four annual periods (2007–2010) by the Centers for Medicare and Medicaid Services (CMS), allowed us to perform a broad-scale exploratory analysis of Medicare service-usage patterns. The CMS data set contains data on the Health Care Referral Region level, which subsets the United States into 306 distinct regions. To identify potential trends in service usage over these regions, we used hierarchical agglomerative cluster analysis (HACA) to group regions into clusters based on similarity of service-usage patterns. Much previous work on the subject of cost variation starts with analysis of cost. By beginning with service-usage data, HACA effectively uncoupled patterns of service-usage from overall patterns of cost. We found that while some Medicare services were used in a fairly consistent manner over all clusters, other services varied considerably. Variation in how frequently services are used may provide insight into practices that are particularly expensive or particularly cost effective. It also may be possible to develop recommendations for more cost-efficient high-quality medical care by taking such information into account. In addition, the variance among the frequencies with which some services are used may point to inconsistent standards of medical practice that are not necessarily directly reflected in total cost, or to more deeply underlying geographic or demographic factors that are not necessarily reflected directly in total cost.

Vacha, Linnet↗
DOE OSTI2022

Shared Mobility Data Availability and Usage Trends

In this report, we summarize data availability of new shared mobility technologies by mobility type and region and analyze how shared mobility usage varies by time and by demographic factors in the United States. The shared mobility technologies include ridehailing from transportation network companies (TNC), bikeshare, and scooter share. There is a wide range in shared mobility usage per capita across the country, even within urban areas. We observe that there was steady growth of new shared mobility usage from 2015 to early 2020, before COVID-19 reduced overall ridership. An analysis focused on Chicago shows that despite the availability of good public transportation, high usage of new shared mobility modes is centered in high income communities, especially by households who own less vehicles. However, we find that TNC is used for first-mile and last-mile in lower income communities. Analysis on the bikeshare usage also shows that household income is shown to not be a statistically significant factor when accounting for other factors including employment density, population density, percentage of college graduates, and bike-lane proximity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

Usage-Based Discovery of Earth Observations

Most providers of Earth Observation data enable search via dataset characteristics, (e.g., quantity being measured, instrument, location, and time). However, the Earth Science Information Partners Federation (ESIP) is attempting to improve dataset discovery by capturing information on how datasets are used. At a usage-based discovery hackfest at the July 2020 ESIP Summer Meeting, participants pooled their collective skills to implement a prototype based on the connections between datasets and data usage, which can reveal unanticipated patterns in dataset connectivity that are not apparent through traditional approaches. A survey of natural hazards websites, focusing on hydrology-related hazards such as floods and hydrology datasets such as rainfall, illustrated the difficulty of gleaning which dataset was actually used and how it was accessed. This difficulty highlights the need for greater transparency on dataset usage in website design. An application wireframe for a usage-based discovery database was conceptualized as a tool to help applications data specialists rapidly assemble a fit-for-purpose website as part of a disaster response, where customer feedback was integral to the manner in which the website would be developed and delivered. Capturing connections among datasets and instances of usage, either for research or applications, could help users identify datasets with a proven track record of utility or those that have been supplanted by more accurate and(or) precise representations. It may also help Earth Observation providers maximize returns on investments in data services. Social discovery, i.e, following discovery paths laid down by previous users of the data, could be a critical tool for improving the timeliness and applicability of dataset delivery at times and in circumstances where practitioners don’t have the capacity to evaluate the deluge of datasets that are returned by today’s search tools.

search↗
DOE OSTI2021

Evaluating MPI resource usage summary statistics

The Message Passing Interface (MPI) remains the dominant programming model for scientific applications running on today’s high-performance computing (HPC) systems. This dominance stems from MPI’s powerful semantics for inter-process communication that has enabled scientists to write applications for simulating important physical phenomena. MPI does not, however, specify how messages and synchronization should be carried out. Those details are typically dependent on low-level architecture details and the message characteristics of the application. Therefore, analyzing an application’s MPI resource usage is critical to tuning MPI’s performance on a particular platform. The result of this analysis is typically a discussion of the mean message sizes, queue search lengths and message arrival times for a workload or set of workloads. While a discussion of the arithmetic mean in MPI resource usage might be the most intuitive summary statistic, it is not always the most accurate in terms of representing the underlying data. In this paper, we analyze MPI resource usage for a number of key MPI workloads using an existing MPI trace collector and discrete-event simulator. Our analysis demonstrates that the average, while easy and efficient to calculate, is a useful metric for characterizing latency and bandwidth measurements, but may not be a good representation of application message sizes, match list search depths, or MPI inter-operation times. Additionally, we show that the median and mode are superior choices in many cases. We also observe that the arithmetic mean is not the best representation of central tendency for data that are drawn from distributions that are multi-modal or have heavy tails. Furthermore, the results and analysis of our work provide valuable guidance on how we, as a community, should discuss and analyze MPI resource usage data for scientific applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Phosphate Limitation Responses in Marine Green Algae Are Linked to Reprogramming of the tRNA Epitranscriptome and Codon Usage Bias

Marine algae are central to global carbon fixation, and their productivity is dictated largely by resource availability. Reduced nutrient availability is predicted for vast oceanic regions as an outcome of climate change; however, there is much to learn regarding response mechanisms of the tiny picoplankton that thrive in these environments, especially eukaryotic phytoplankton. Here, we investigate responses of the picoeukaryote Micromonas commoda, a green alga found throughout subtropical and tropical oceans. Under shifting phosphate availability scenarios, transcriptomic analyses revealed altered expression of transfer RNA modification enzymes and biased codon usage of transcripts more abundant during phosphate-limiting versus phosphate-replete conditions, consistent with the role of transfer RNA modifications in regulating codon recognition. To associate the observed shift in the expression of the transfer RNA modification enzyme complement with the transfer RNAs encoded by M. commoda, we also determined the transfer RNA repertoire of this alga revealing potential targets of the modification enzymes. Codon usage bias was particularly pronounced in transcripts encoding proteins with direct roles in managing phosphate limitation and photosystem-associated proteins that have ill-characterized putative functions in “light stress.” The observed codon usage bias corresponds to a proposed stress response mechanism in which the interplay between stress-induced changes in transfer RNA modifications and skewed codon usage in certain essential response genes drives preferential translation of the encoded proteins. Collectively, we expose a potential underlying mechanism for achieving growth under enhanced nutrient limitation that extends beyond the catalog of up- or downregulated protein-encoding genes to the cell biological controls that underpin acclimation to changing environmental conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Effect of Ladle Usage on Cleanliness of Bearing Steel

To investigate the effects of ladle usage on the inclusions and total oxygen contents of bearing steel, MgO refractory rods with different glazes were used to simulate different ladle usages. The results show that the effects of different ladle usages on the cleanliness of the steel differ from each other. The total oxygen content of steel increases with the decreasing glaze basicity. Ladle glaze having a lower basicity has a more negative impact on the cleanliness of steel in the subsequent production. Inclusions can be generated by the flush-off of ladle glaze, and the initial glaze is important in the evolution of inclusions in the subsequent heats. To avoid the negative effect of ladle usage and to improve the steel cleanliness as much as possible, specialized ladles were suggested for producing high-quality steel grades.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Video game console usage and US national energy consumption: Results from a field-metering study

There has been an increased in attention placed on the energy consumption of miscellaneous electronic loads in buildings by energy analysts and policymakers in recent years. The share of electricity consumed by consumer electronics in US households has increased in the last decade. Many devices, however, lack robust energy use data, making energy consumption estimates difficult and uncertain. Video game consoles are high-performance machines present in approximately half of all households and can consume a considerable amount of power. The precise usage of game consoles has significant uncertainty, however, leading to a wide range of recent national energy consumption estimates. We present here an analysis based on field-metered usage data, collected as part of a larger field metering study in the USA. This larger study collected data from 880 households in 2012 on a variety of devices, including 113 game consoles (the majority of which are Generation 7 consoles). From our metering, we find that although some consoles are left on nearly 24 h/day, the overall average usage is lower than many other studies have assumed, leading to a US national energy consumption estimate of 7.1 TWh in 2012. Nevertheless, there is an opportunity to reduce energy use with proper game console power management, as a substantial amount of game console usage occurs with the television turned off. The emergence of Generation 8 consoles may increase national energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Cooking methods and kitchen ventilation availability, usage, perceived performance and potential in Canadian homes

Cooking is a substantial contributor to air pollutant exposures in many residences. Effective use of kitchen ventilation can mitigate exposure; however, information on its availability, usage, and potential to increase its use across the population has been limited. This study aimed to obtain nationally representative information on cooking methods, kitchen ventilation availability and usage, and the potential for education to increase effective usage. An online survey was sent to a representative sample of Canadian homes to collect data on cooking methods, the presence and use of mechanical kitchen ventilation devices, perceived device performance, and willingness to implement mitigation strategies. Responses were weighted to match key demographic factors and analyzed using non-parametric statistics. Among the 4500 respondents, 90% had mechanical ventilation devices over the cooktop (66% of which were vented to the outside), and 30% reported regularly using their devices. Devices were used most often for deep-frying, followed by stir-frying, sautéing or pan-frying, indoor grilling, boiling or steaming. Almost half reported rarely or never using their ventilation devices during baking or oven self-cleaning. Only 10% were fully satisfied with their devices. More frequent use was associated with the device being vented to the outdoors, having more than two speed settings, quiet operation if only one speed, covering over half of the cooktop, and higher perceived effectiveness. After being informed of the benefits of kitchen ventilation, 64% indicated they would consider using their devices more often, preferentially using back burners with ventilation, and/or using higher ventilation device settings when needed. This study provides population-representative data on the most used cooking methods, kitchen ventilation availability and usage, and influencing factors in Canadian homes. Such data are needed for exposure assessments and evaluating the potential to mitigate cooking-related pollutant exposures via more effective use of kitchen ventilation. The data can be reasonably extrapolated to the United States, given the similarities in residential construction practices and cultural norms between the two countries.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Genome-wide role of codon usage on transcription and identification of potential regulators

Significance Codon usage bias, the preference for certain synonymous codons, is a feature of all genomes and plays an important role in determining gene expression levels. Surprisingly, in addition to its role in mRNA translation, codon usage was recently shown to regulate gene expression at the transcriptional level. In this study, our results suggest that codon usage influences transcription genome-wide independently of its effect on translation. By carrying out a large-scale genetic screen, we identified 18 factors participating in multiple transcriptional regulatory mechanisms involved in the codon usage-mediated transcriptional effects. Together, our study establishes a foundation for future mechanistic understanding of this gene regulatory phenomenon.

Zhao, Fangzhou↗
DOE OSTI2018

Structural Comparison of Human Anti-HIV-1 gp120 V3 Monoclonal Antibodies of the Same Gene Usage Induced by Vaccination and Chronic Infection

ABSTRACT Elucidating the structural basis of antibody (Ab) gene usage and affinity maturation of vaccine-induced Abs can inform the design of immunogens for inducing desired Ab responses in HIV vaccine development. Analyses of monoclonal Abs (MAbs) encoded by the same immunoglobulin genes at different stages of maturation can help to elucidate the maturation process. We have analyzed four human anti-V3 MAbs with the same VH1-3*01 and VL3-10*01 gene usage. Two MAbs, TA6 and TA7, were developed from a vaccinee in the HIV vaccine phase I trial DP6-001 with a polyvalent DNA prime/protein boost regimen, and two others, 311-11D and 1334, were developed from HIV-infected patients. The somatic hypermutation (SHM) rates in VH of vaccine-induced MAbs are lower than in chronic HIV infection-induced MAbs, while those in VL are comparable. Crystal structures of the antigen-binding fragments (Fabs) in complex with V3 peptides show that these MAbs bind the V3 epitope with a new cradle-binding mode and that the V3 β-hairpin lies along the antigen-binding groove, which consists of residues from both heavy and light chains. Residues conserved from the germ line sequences form specific binding pockets accommodating conserved structural elements of the V3 crown hairpin, predetermining the Ab gene selection, while somatically mutated residues create additional hydrogen bonds, electrostatic interactions, and van der Waals contacts, correlating with an increased binding affinity. Our data provide a unique example of germ line sequences determining the primordial antigen-binding sites and SHMs correlating with affinity maturation of Abs induced by vaccine and natural HIV infection. IMPORTANCE Understanding the structural basis of gene usage and affinity maturation for anti-HIV-1 antibodies may help vaccine design and development. Antibodies targeting the highly immunogenic third variable loop (V3) of HIV-1 gp120 provide a unique opportunity for detailed structural investigations. By comparing the sequences and structures of four anti-V3 MAbs at different stages of affinity maturation but of the same V gene usage, two induced by vaccination and another two by chronic infection, we provide a fine example of how germ line sequence determines the essential elements for epitope recognition and how affinity maturation improves the antibody's recognition of its epitope.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Data-driven analysis to understand GPU hardware resource usage of optimizations

With heterogeneous systems, the number of GPUs per chip increases to provide computational capabilities for solving science at a nanoscopic scale. However, low utilization for single GPUs defies the need to invest more money in expensive accelerators. Although related work develops optimizations to improve application performance, none studies how these optimizations impact hardware resource usage or average GPU utilization. Here, this paper takes a data-driven analysis approach in addressing this gap by (1) characterizing how hardware resource usage affects device utilization, execution time, or both, (2) presenting a multiobjective metric to identify important application-device interactions that can be optimized to improve device utilization and application performance jointly, (3) studying hardware resource usage behaviors of several optimizations for a benchmark application, and finally (4) identifying optimization opportunities for several scientific proxy applications based on their hardware resource usage behaviors. Furthermore, we demonstrate the applicability of our methodology by applying the identified optimizations to a proxy application, which improves the execution time, device utilization, and power consumption by up to 29.6%, 5.3% and 26.5% respectively.

Computer science↗
DOE OSTI2021

Signatures of optimal codon usage in metabolic genes inform budding yeast ecology

Reverse ecology is the inference of ecological information from patterns of genomic variation. One rich, heretofore underutilized, source of ecologically relevant genomic information is codon optimality or adaptation. Bias toward codons that match the tRNA pool is robustly associated with high gene expression in diverse organisms, suggesting that codon optimization could be used in a reverse ecology framework to identify highly expressed, ecologically relevant genes. To test this hypothesis, we examined the relationship between optimal codon usage in the classic galactose metabolism ( GAL ) pathway and known ecological niches for 329 species of budding yeasts, a diverse subphylum of fungi. We find that optimal codon usage in the GAL pathway is positively correlated with quantitative growth on galactose, suggesting that GAL codon optimization reflects increased capacity to grow on galactose. Optimal codon usage in the GAL pathway is also positively correlated with human-associated ecological niches in yeasts of the CUG-Ser1 clade and with dairy-associated ecological niches in the family Saccharomycetaceae. For example, optimal codon usage of GAL genes is greater than 85% of all genes in the genome of the major human pathogen Candida albicans (CUG-Ser1 clade) and greater than 75% of genes in the genome of the dairy yeast Kluyveromyces lactis (family Saccharomycetaceae). We further find a correlation between optimization in the GAL actose pathway genes and several genes associated with nutrient sensing and metabolism. This work suggests that codon optimization harbors information about the metabolic ecology of microbial eukaryotes. This information may be particularly useful for studying fungal dark matter—species that have yet to be cultured in the lab or have only been identified by genomic material.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Hourly natural gas usage surrounding NIST NEB and NWB GHG monitoring stations in 2023

This dataset includes hourly natural gas usage surrounding two National Institute of Standards and Technology (NIST) greenhouse gas (GHG) monitoring stations, Northwest Baltimore (NWB; 39.3445°N, 76.6851°W) and Northeast Baltimore (NEB; 39.3154°N, 76.5830°W), for the year 2023 in units of therms. The gas usage was provided by the local gas distribution company and includes hourly data averaged across groups of 15 or more addresses to maintain anonymity. The hourly data was averaged spatially within a radius of 1 km from each monitoring station, including all groups containing data from any address within 1km of the monitoring station. This dataset only includes gas usage from service points equipped with advanced metering infrastructure (AMI). Between the two monitoring sites, this dataset includes usage from a total of 3649 service points, which we estimate is at least 44% of the service points in the domain.

Kenion, Helen C. R. [School of Environment and Sus↗
DOE OSTI2017

System and methods of resource usage using an interoperable management framework

Generic rights expression language allowing interoperability across different computing environments including resource usage of different applications. A formal framework for usage management provides scaffolding upon which interoperable usage management systems can be built. Certain features of the framework are standardized, such as the operational semantics, including areas free of standards that necessitate choice and innovation to achieve a balance of flexibility and usability for interoperability in usage management systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Accounting for multi-dimensional dependencies among decision-makers within a generalized model framework: An application to understanding shared mobility service usage levels

Activity-travel choices of individuals are influenced by spatial dependency effects. As individuals interact and exchange information with, or observe the behaviors of, those in close proximity of themselves, they are likely to shape their behavioral choices accordingly. For this reason, econometric choice models that account for spatial dependency effects have been developed and applied in a number of fields, including transportation. However, spatial dependence models to date have largely defined the strength of association across behavioral units based on spatial or geographic proximity. In the current context of social media platforms and ubiquitous internet and mobile connectivity, the strength of associations among individuals is no longer solely dependent on spatial proximity. Rather, the strength of associations among individuals may be based on shared attitudes and preferences as well. In other words, behavioral choice models may benefit from defining dependency effects based on attitudinal constructs in addition to geographical constructs. In this paper, frequency of usage of car-sharing and ride-hailing services is modeled using a generalized heterogeneous data model (GHDM) framework that incorporates multi-dimensional dependencies among decision-makers. The model system is estimated on the 2014-2015 Puget Sound Regional Travel Study survey sample, with proximity in latent attitudinal constructs defined by a number of personality trait variables. Model estimation results show that social dependency effects arising from similarities in attitudes and preferences are significant in explaining shared mobility service usage. Ignoring such effects may lead to erroneous estimates of the adoption and usage of future transportation technologies and mobility services.

33 ADVANCED PROPULSION SYSTEMS↗