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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A synthesis of bacterial and archaeal phenotypic trait data

A synthesis of phenotypic and quantitative genomic traits is provided for bacteria and archaea, in the form of a scripted, reproducible workflow that standardizes and merges 26 sources. The resulting unified dataset covers 14 phenotypic traits, 5 quantitative genomic traits, and 4 environmental characteristics for approximately 170,000 strain-level and 15,000 species-aggregated records. It spans all habitats including soils, marine and fresh waters and sediments, host-associated and thermal. Trait data can find use in clarifying major dimensions of ecological strategy variation across species. They can also be used in conjunction with species and abundance sampling to characterize trait mixtures in communities and responses of traits along environmental gradients.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A Graph-Net with Node Embeddings to Detect False Data Injection Attacks in Photovoltaic Systems

Distributed energy resources (DER) contribute to the operational stability of the larger power grid both at utility-scale as well as commercial and residential scales in aggregated forms. These DER in-turn are susceptible to increasing cyber threats. An adversary can plug into the same local network that a field photovoltaic (PV) system uses to interconnect its data loggers and inverters and manipulate certain measurements collected from the network or trick existing irradiance and inverter readings through false data injection attacks (FDIA). Control routines that rely on these measurements can propagate the false data, impacting critical decisions that result in a suboptimal operation or even cause intentional harm leading to inverter-tripping or unscheduled loads that need to be shed. To detect FDIA in PV systems, the paper introduces an attention-based graph neural network with node embeddings and applied it to a simple prototypical DC-coupled microgrid with PV, energy storage, and load. The algorithm shows a detection accuracy of up to 98.95%. The proposed FDIA detection technique will provide micro-grid operators with an effective method to safeguard their systems, guaranteeing the secure and reliable operation.

Parvez, Imtiaz [Utah Valley University]↗
DOE OSTI2020

ExpoKids: An R-based tool for characterizing aggregate chemical exposure during childhood

Background: Aggregate exposure, the combined exposures to a single chemical from all pathways, is a critical children’s health issue. Objective: The primary objective is to develop a tool to illustrate potential differences in aggregate exposure at various childhood lifestages and the adult lifestage. Methods: We developed ExpoKids (an R-based tool) using oral exposure estimates across lifestages generated by US EPA’s Exposure Factors Interactive Resource for Scenarios Tool (ExpoFIRST). Results: ExpoKids is applied to illustrate aggregate oral exposure, for ten media, as average daily doses (ADD) and lifetime average daily doses (LADD) in five graphs organized across seven postnatal childhood lifestages and the adult lifestage. This data visualization tool conveys ExpoFIRST findings, from available exposure data, to highlight the relative contributions of media and lifestages to chemical exposure. To evaluate the effectiveness of ExpoKids, three chemical case examples (di[2-ethylhexyl] phthalate [DEHP], manganese, and endosulfan) were explored. Data available from the published literature and databases for each case example were used to explore research questions regarding media and lifestage contributions to aggregate exposure. Significance: These illustrative case examples demonstrate ExpoKids’ versatile application to explore a diverse set of children’s health risk assessment and management questions by visually depicting specific media and lifestage contributions to aggregate exposure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

ComStock Measure Scenario Documentation: Chiller Replacement

Building on a 3-year effort to calibrate and validate the U.S. Department of Energy's ResStock (TM) and ComStock (TM) models, this work produces national datasets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual energy consumption (at a subhourly resolution) of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology and results of the baseline model are discussed in the final technical report of the End-Use Load Profiles project. The goal of this work is to develop energy efficiency and demand flexibility end-use load shapes that cover high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to various "what-if" scenarios that can be applied to buildings. An end-use savings shape is the difference in energy consumption between a baseline building (or collection of buildings) and a building with an energy efficiency or demand flexibility measure applied. It results in a time-series profile broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step, as well as annual aggregations. This report describes the modeling methodology for a single end-use savings shape measure - chiller replacement - and briefly introduces key results. The full public dataset can be accessed on the ComStock (TM) data lake or via the Data Viewer at comstock.nrel.gov. The public data set enables users to create custom aggregations of results for their use case (e.g., filter to a specific county).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation

Current spectral clustering algorithms suffer from the sensitivity to existing noise, and parameter scaling, and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real world datasets with heterogeneous densities. This paper aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically\ models the heat diffusion traces along the entire time scale, so it ensures robustness during clustering process, while LDAT probabilistically reveals local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, not only does it provide an advanced noise-resisting and density-aware spectral mapping to the original dataset, but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithms outperform state-of-the-art clustering algorithms for the data with heterogeneous density distributions, and achieve robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Recombinant proteins incorporating short non-native extensions may display increased aggregation propensity as detected by high resolution NMR spectroscopy

Highlights: Black-Right-Pointing-Pointer Bile acid binding proteins from different constructs retain structural integrity. Black-Right-Pointing-Pointer NMR {sup 15}N-T{sub 1} relaxation data of BABPs show differences if LVPR extension is present. Black-Right-Pointing-Pointer Deviations from a {sup 15}N-T{sub 1}/molecular-weight calibration curve indicate aggregation. -- Abstract: The use of a recombinant protein to investigate the function of the native molecule requires that the former be obtained with the same amino acid sequence as the template. However, in many cases few additional residues are artificially introduced for cloning or purification purposes, possibly resulting in altered physico-chemical properties that may escape routine characterization. For example, increased aggregation propensity without visible protein precipitation is hardly detected by most analytical techniques but its investigation may be of great importance for optimizing the yield of recombinant protein production in biotechnological and structural biology applications. In this work we show that bile acid binding proteins incorporating the common C-terminal LeuValProArg extension display different hydrodynamic properties from those of the corresponding molecules without such additional amino acids. The proteins were produced enriched in nitrogen-15 for analysis via heteronuclear NMR spectroscopy. Residue-specific spin relaxation rates were measured and related to rotational tumbling time and molecular size. While the native-like recombinant proteins show spin-relaxation rates in agreement with those expected for monomeric globular proteins of their mass, our data indicate the presence of larger adducts for samples of proteins with very short amino acid extensions. The used approach is proposed as a further screening method for the quality assessment of biotechnological protein products.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

An Elegant Sufficiency: Load-Aware Differentiated Scheduling of Data Transfers

We investigate the file transfer scheduling problem, where transfers among different endpoints must be scheduled to maximize pertinent metrics. We propose two new algorithms that exploit the fact that the aggregate bandwidth obtained over a network or at a storage system tends to increase with the number of concurrent transfers—but only up to a certain limit. The first algorithm, SEAL, uses runtime information and data-driven models to approximate system load and adapt transfer schedules and concurrency so as to maximize performance while avoiding saturation. We implement this algorithm using GridFTP as the transfer protocol and evaluate it using real transfer logs in a production WAN environment. Results show that SEAL can improve average slowdowns and turnaround times by up to 25% and worst-case slowdown and turnaround times by up to 50%, compared with the best-performing baseline scheme. Our second algorithm, STEAL, further leverages user-supplied categorization of transfers as either “interactive” (requiring immediate processing) or “batch” (less time-critical). Results show that STEAL reduces the average slowdown of interactive transfers by 63% compared to the best-performing baseline and by 21% compared to SEAL. For batch transfers, compared to the best-performing baseline, STEAL improves by 18% the utilization of the bandwidth unused by interactive transfers. By elegantly ensuring a sufficient, but not excessive, allocation of concurrency to the right transfers, we significantly improve overall performance despite constraints.

Kettimuthu, Rajkumar↗
DOE OSTI2022

Updated U.S. Low-Temperature Heating and Cooling Demand by County and Sector

This dataset includes U.S. low-temperature heating and cooling demand at the county level in major end-use sectors: residential, commercial, manufacturing, agricultural, and data centers. Census division-level end-use energy consumption, expenditure, and commissioned power database were dis-aggregated to the county level. The county-level database was incorporated with climate zone, numbers of housing units and farms, farm size, and coefficient of performance (COP) for heating and cooling demand analysis. This dataset also includes a paper containing a full explanation of the methodologies used and maps. Residential data were updated from the latest Residential Energy Consumption Survey (RECS) dataset (2015) using 2020 census data. Commercial data were baselined off the latest Commercial Building Energy Consumption Survey (CBECS) dataset (2012). Manufacturing data were baselined off the latest Manufacturing Energy Consumption Survey (MECS) dataset (2021).

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel

Abstract Estimates of plant traits derived from hyperspectral reflectance data have the potential to efficiently substitute for traits, which are time or labor intensive to manually score. Typical workflows for estimating plant traits from hyperspectral reflectance data employ supervised classification models that can require substantial ground truth datasets for training. We explore the potential of an unsupervised approach, autoencoders, to extract meaningful traits from plant hyperspectral reflectance data using measurements of the reflectance of 2151 individual wavelengths of light from the leaves of maize ( Zea mays ) plants harvested from 1658 field plots in a replicated field trial. A subset of autoencoder‐derived variables exhibited significant repeatability, indicating that a substantial proportion of the total variance in these variables was explained by difference between maize genotypes, while other autoencoder variables appear to capture variation resulting from changes in leaf reflectance between different batches of data collection. Several of the repeatable latent variables were significantly correlated with other traits scored from the same maize field experiment, including one autoencoder‐derived latent variable (LV8) that predicted plant chlorophyll content modestly better than a supervised model trained on the same data. In at least one case, genome‐wide association study hits for variation in autoencoder‐derived variables were proximal to genes with known or plausible links to leaf phenotypes expected to alter hyperspectral reflectance. In aggregate, these results suggest that an unsupervised, autoencoder‐based approach can identify meaningful and genetically controlled variation in high‐dimensional, high‐throughput phenotyping data and link identified variables back to known plant traits of interest.

Tross, Michael C.↗
DOE OSTI2017

The Distributed Wind Cost Taxonomy

To date, there has been no standard method or tool to analyze the installed and operational costs for distributed wind turbine systems. This report describes the development of a classification system, or taxonomy, for distributed wind turbine project costs. The taxonomy establishes a framework to help collect, sort, and compare distributed wind cost data that mirrors how the industry categorizes information. The taxonomy organizes costs so they can be aggregated from installers, developers, vendors, and other sources without losing cost details. Developing a peer-reviewed taxonomy is valuable to industry stakeholders because a common understanding the details of distributed wind turbine costs and balance of station costs is a first step to identifying potential high-value cost reduction opportunities. Addressing cost reduction potential can help increase distributed wind's competitiveness and propel the U.S. distributed wind industry forward. The taxonomy can also be used to perform cost comparisons between technologies and track trends for distributed wind industry costs in the future. As an initial application and piloting of the taxonomy, preliminary cost data were collected for projects of different sizes and from different regions across the contiguous United States. Following the methods described in this report, these data are placed into the established cost categories.

17 WIND ENERGY↗
DOE OSTI2024

CMPLE: Correlation Modeling to Decode Photosynthesis Using the Minorize–Maximize Algorithm

In plant genomic experiments, correlations among various biological traits (phenotypes) give new insights into how genetic diversity may have tuned biological processes to enhance fitness under diverse conditions. Consequently, knowing how the correlations are affected by genetic (G) and environmental (E) factors helps develop climate-resilient plants. However, the current literature lacks any method for assessing the effect of predictors on pairwise correlations among multiple phenotypes together with easily interpretable model parameters. To address this need, we propose to model pairwise correlations directly in terms of G and E and develop a computationally efficient inference procedure. Two major novelties in our methodology are (1) the use of a composite pairwise likelihood method to avoid the positive definiteness restriction on the correlation matrix and (2) the use of a novel Minorize–Maximize (MM) algorithm for the efficient estimation of a large number of parameters. The proposed method shows excellent numerical performance on synthetic datasets. Here, the analysis of the motivating data on cowpea reveals that the rates of solar energy storage by photosynthesis (the aggregate trait) are differentially affected by different genetic loci through two distinct processes: “photoinhibition” which results from photodamage caused by excess light, and “photoprotection” which protects plants from photodamage but also results in energy loss.

Correlation modeling↗
DOE OSTI2017

Combinatorial appraisal of transition states for in situ pair distribution function analysis

In situ total scattering measurements are increasingly utilized to follow atomic and nanoscale structural details of phase transitions and other transient processes in materials. This contribution presents an automated method and associated tool set to analyze series of diffraction and pair distribution function data with a linear combination of end-member states. It is demonstrated that the combinatorial appraisal of transition states (CATS) software tracks phase changes, relative phase fractions and length scales of interest in experimental data series. Here it is further demonstrated, using a series of local structure data simulations, that the misfit of such a model can reveal details of phase aggregation and growth related to the pair distribution function's sensitivity to interphase correlations. CATS may be applied to quantitative evaluation of many transient processes, including amorphous-to-crystalline phase transitions, the evolution of solid-solution behaviors, the precipitation and growth of aggregates, and other atomic to nanoscale details of crystallization and phase transformation phenomena.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Localization of polyhydroxybutyrate in sugarcane using Fourier-transform infrared microspectroscopy and multivariate imaging

Background: Slow-degrading, fossil fuel-derived plastics can have deleterious effects on the environment, especially marine ecosystems. The production of bio-based, biodegradable plastics from or in plants can assist in supplanting those manufactured using fossil fuels. Polyhydroxybutyrate (PHB) is one such biodegradable polyester that has been evaluated as a possible candidate for relinquishing the use of environmentally harmful plastics. Results: PHB, possessing similar properties to polyesters produced from non-renewable sources, has been previously engineered in sugarcane, thereby creating a high-value co-product in addition to the high biomass yield. This manuscript illustrates the coupling of a Fourier-transform infrared microspectrometer, equipped with a focal plane array (FPA) detector, with multivariate imaging to successfully identify and localize PHB aggregates. Principal component analysis imaging facilitated the mining of the abundant quantity of spectral data acquired using the FPA for distinct PHB vibrational modes. PHB was measured in the chloroplasts of mesophyll and bundle sheath cells, acquiescent with previously evaluated plant samples. Conclusion: This study demonstrates the power of IR microspectroscopy to rapidly image plant sections to provide a snapshot of the chemical composition of the cell. While PHB was localized in sugarcane, this method is readily transferable to other value-added co-products in different plants.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Status and Trends in the U.S. Voluntary Green Power Market (2013 Data)

Voluntary green power markets are those in which consumers and institutions voluntarily purchase renewable energy to match their electricity needs. This report surveys utilities, competitive suppliers, renewable energy certificate (REC) marketers, and, for the first time, the community choice aggregation market. This report finds that the voluntary market totaled 62 million megawatt-hours in 2013. Approximately 5.4 million customers are purchasing green power. This report presents data and analysis on voluntary market sales and customer participation, products and premiums, green pricing marketing, and administrative expenses. The report also details trends in REC tracking systems, REC pricing in voluntary and compliance markets, community and crowd-funded solar, and interest in renewable energy by the information and communication technologies sector.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

A Retrospective Analysis of the Benefits and Impacts of U.S. Renewable Portfolio Standards

This is the second in a series of reports exploring the costs, benefits, and other impacts of state renewable portfolio standards (RPS), both retrospectively and prospectively. This report focuses on the benefits and impacts of all state RPS programs, in aggregate, for the year 2013 (the most-recent year for which the requisite data were available). Relying on a well-vetted set of methods, the study evaluates a number of important benefits and impacts in both physical and monetary terms, where possible, and characterizes key uncertainties. The prior study in this series focused on historical RPS compliance costs, and future work will evaluate costs, benefits, and other impacts of RPS policies prospectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Multi-Angle Snowflake Camera Value-Added Product

The Multi-Angle Snowflake Camera (MASC) addresses a need for high-resolution multi-angle imaging of hydrometeors in freefall with simultaneous measurement of fallspeed. As illustrated in Figure 1, the MASC consists of three cameras, separated by 36°, each pointing at an identical focal point approximately 10 cm away. Located immediately above each camera, a light aims directly at the center of depth of field for its corresponding camera. The focal point at which the cameras are aimed lies within a ring through which hydrometeors fall. The ring houses a system of near-infrared emitter-detector pairs, arranged in two arrays separated vertically by 32 mm. When hydrometeors pass through the lower array, they simultaneously trigger all cameras and lights. Fallspeed is calculated from the time it takes to traverse the distance between the upper and lower triggering arrays. The trigger electronics filter out ambient light fluctuations associated with varying sunlight and shadows. The microprocessor onboard the MASC controls the camera system and communicates with the personal computer (PC). The image data is sent via FireWire 800 line, and fallspeed (and camera control) is sent via a Universal Serial Bus (USB) line that relies on RS232-over-USB serial conversion. See Table 1 for specific details on the MASC located at the Oliktok Point Mobile Facility on the North Slope of Alaska. The value-added product (VAP) detailed in this documentation analyzes the raw data (Section 2.0) using Python: images rely on OpenCV image processing library and derived aggregated statistics rely on some clever averaging. See Sections 4.1 and 4.2 for more details on what variables are computed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Organic Speciation and Interactions in ALSEP. One Step Partitioning Process of MinorActinides, Lanthanides, and Fission Products

Research conducted in this project focused on speciation characterization of the ALSEP extraction system (similar to the TALSPEAK-like application) which addresses recovery of minor actinides and lanthanides from PUREX raffinate. The ALSEP (Actinide-Lanthanide Separation) system proposes a trivalent minor actinide/lanthanide group extraction followed by a selective actinide strip to effectively partition minor actinides, lanthanides, and other fission products. The group extraction is achieved by a mixed organic solvent, combining acidic and neutral extracting ligands. The objective of this work is investigation the interactions of the acidic and neutral ligands with each other in the extraction organic solvent and with desired metals. Four combinations of a cation-exchange ligand (either HDEHP or HEH[EHP]) and a neutral solvating ligand (either TODGA or T2EHDGA) in n-dodecane were studied. Interligand dimers in these combinations were investigated with NMR and FTIR, the strongest interaction was observed for HDEHP+TODGA and weakest for T2EHDGA+HEH[EHP]. Complexes of the trivalent Am and lanthanides Nd, Eu, Ho, Pr in these binary ligand mixtures were probed with spectroscopic (UV–vis, time-resolved fluorescence, XAFS and SAXS-spectroscopies) and extraction (Job’s plot, slope analysis) methods; co-extracted water and nitric acid were analyzed by titration methods. Comparison of the collected spectra showed significant shifting for both DGA ligands with HDEHP, while both combinations of HEH[EHP]+DGA displayed much smaller spectral changes. Data were modeled with HypSpec program, and stability constants for extracted complexes were calculated. Aggregation in these systems were studied by SAXS (small-angle X-ray scattering) spectroscopy and interfacial tension, size of aggregates were measured. Two PhD students graduated (2017, 2018) and one student continues her PhD study.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗