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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Data for Characterization of the Ghd8 Flowering Time Gene in a Mini-Core Collection of Miscanthus sinensis

The optimal flowering time for bioenergy crop miscanthus is essential for environmental adaptability and biomass accumulation. However, little is known about how genes controlling flowering in other grasses contribute to flowering regulation in miscanthus. Here, we report on the sequence characterization and gene expression of Miscanthus sinensisGhd8 , a transcription factor encoding a HAP3/NF-YB DNA-binding domain, which has been identified as a major quantitative trait locus in rice, with pleiotropic effects on grain yield, heading date and plant height. In M. sinensis , we identified two homoeologous loci, MsiGhd8A located on chromosome 13 and MsiGhd8B on chromosome 7, with one on each of this paleo-allotetraploid species’ subgenomes. A total of 46 alleles and 28 predicted protein sequence types were identified in 12 wild-collected accessions. Several variants of MsiGhd8 showed a geographic and latitudinal distribution. Quantitative real-time PCR revealed that MsiGhd8 expressed under both long days and short days, and MsiGhd8B showed a significantly higher expression than MsiGhd8A . The comparison between flowering time and gene expression indicated that MsiGhd8B affected flowering time in response to day length for some accessions. This study provides insight into the conserved function of Ghd8 in the Poaceae, and is an important initial step in elucidating the flowering regulatory network of Miscanthus.

Feedstock Production↗
DOE OSTI2024

Deep learning-based temporal deconvolution for photon time-of-flight distribution retrieval

The acquisition of the time of flight (ToF) of photons has found numerous applications in the biomedical field. Over the last decades, a few strategies have been proposed to deconvolve the temporal instrument response function (IRF) that distorts the experimental time-resolved data. However, these methods require burdensome computational strategies and regularization terms to mitigate noise contributions. Herein, we propose a deep learning model specifically to perform the deconvolution task in fluorescence lifetime imaging (FLI). The model is trained and validated with representative simulated FLI data with the goal of retrieving the true photon ToF distribution. Its performance and robustness are validated with well-controlled in vitro experiments using three time-resolved imaging modalities with markedly different temporal IRFs. The model aptitude is further established with in vivo preclinical investigation. Overall, these in vitro and in vivo validations demonstrate the flexibility and accuracy of deep learning model-based deconvolution in time-resolved FLI and diffuse optical imaging.

Pandey, Vikas (ORCID:0000000154771095)↗
DOE OSTI2025

Fast quantum ghost imaging with a single-photon-sensitive time-stamping camera

Quantum ghost imaging (QGI) leverages correlations between entangled photon pairs to reconstruct an image using light that has never physically interacted with an object. Despite extensive research interest, this technique has long been hindered by slow acquisition speeds, due to the use of raster-scanned detectors or the slow response of intensified cameras. Here, we utilize a single-photon-sensitive time-stamping camera to perform QGI at ultra-low-light levels with rapid data acquisition and processing times, achieving high-resolution and high-contrast images in under 1 min. Our work addresses the trade-off between image quality, optical power, data acquisition time, and data processing time in QGI, paving the way for practical applications in biomedical and quantum-secured imaging.

Mavian, Alex (ORCID:0000000279448830)↗
DOE OSTI2025

Time-refraction near the critical angle and angular streaking with attosecond resolution [Invited]

We study the time-refraction of optical waves as they propagate through a time-varying slab and find the frequency shift associated with the abrupt change in the refractive index. We discover a geometrical mechanism responsible for an ultrafast blueshift followed by a redshift, occurring while the refractive index is increased monotonically. Furthermore, we find high sensitivity to incident angles near the traditional critical angle and unravel a new critical angle that varies in time. Finally, we introduce an innovative concept of a streak camera design that utilizes ultrafast nonlinearities in time-varying media and offers attosecond resolution capabilities.

Fruhling, Colton (ORCID:0000000282968802)↗
DOE OSTI2020

Automated pipeline framework for processing of large-scale building energy time series data

Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for “virtual” energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings’ time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building’s daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England

Genetic drift in infectious disease transmission results from randomness of transmission and host recovery or death. The strength of genetic drift for SARS-CoV-2 transmission is expected to be high due to high levels of superspreading, and this is expected to substantially impact disease epidemiology and evolution. However, we don’t yet have an understanding of how genetic drift changes over time or across locations. Furthermore, noise that results from data collection can potentially confound estimates of genetic drift. To address this challenge, we develop and validate a method to jointly infer genetic drift and measurement noise from time-series lineage frequency data. Our method is highly scalable to increasingly large genomic datasets, which overcomes a limitation in commonly used phylogenetic methods. We apply this method to over 490,000 SARS-CoV-2 genomic sequences from England collected between March 2020 and December 2021 by the COVID-19 Genomics UK (COG-UK) consortium and separately infer the strength of genetic drift for pre-B.1.177, B.1.177, Alpha, and Delta. We find that even after correcting for measurement noise, the strength of genetic drift is consistently, throughout time, higher than that expected from the observed number of COVID-19 positive individuals in England by 1 to 3 orders of magnitude, which cannot be explained by literature values of superspreading. Our estimates of genetic drift suggest low and time-varying establishment probabilities for new mutations, inform the parametrization of SARS-CoV-2 evolutionary models, and motivate future studies of the potential mechanisms for increased stochasticity in this system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

QA/QC of the East River, Colorado, discharge and geochemical time series datasets (Almont, BCC, and Pump House) to be used for modeling of hydrogeochemical balance

The following datasets were QA/QC-ed (Quality Assurance/Quality Control): 1. Brush Creek Confluence (BCC) discharge data (from Helen Malenda, USGS, Colorado School of Mines), which were calculated using the pressure transducer data and rating curves. The original 15 min time series data were presented as mean daily discharge. 2. Almont discharge data from United States Geological Survey (USGS). The original data were in 15 min time intervals, and were averaged to mean daily discharge time series. 3. Pump House discharge data as mean daily discharge (downloaded from the SFA portal). 4. BCC and Pump House chemistry data from SFA data portal and/or original spreadsheets provided by Roelof Versteeg. The following challenging QA/QC problems of the datasets were resolved: Missing data with the duration of gaps up to >1 month; Duplicated dates; Anomalies and outliers of discharge and concentrations; Time stamps of measurements of the discharge and concentrations are not aligned (hydrogeochemical balance calculations require the timestamps to be aligned). All QA/QC-ed datasets are given as csv files. The csv files were prepared using the xts files with multiple worksheets, which are also included in the data packages. Figures of the QA/QC-ed datasets are given in the jpeg and pdf formats. The QA/QC-ed datasets have been used to quantify discharge and chemical concentrations in river water in order to understand riverine exports of water and dissolved constituents in the East River watershed. These datasets served as a basis in the presentation given by P. Fox et al. at the 2021 Goldschmidt Conference.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Organic carbon content and turnover time in relation to pedogenic iron and aluminum in four temperate forest soils sampled in 2007

Soil organic carbon (SOC) can be stabilized via association with iron (Fe) and aluminum (Al) minerals. Fe and Al can be strong predictors of SOC storage and turnover in soils with relatively high extractable metals content and moderately acidic to circumneutral pH. Here we test whether pedogenic Fe and Al influence SOC content and turnover in soils with low Fe and Al content and acidic pH. In soils from four sites spanning three soil orders, we quantified the amount of Fe and Al in operationally-defined poorly crystalline and organically-complexed phases using selective chemical dissolution applied to the soil fraction containing mineral-associated carbon. We evaluated the correlations of Fe and Al concentrations, mean annual precipitation (MAP), mean annual temperature (MAT), and pH with SOC content and 14C-based turnover times. We found that poorly crystalline Fe and Al content predicted SOC turnover times (p < 0.0001) consistent with findings of previous studies, while organically-complexed Fe and Al content was a better predictor of SOC concentration (p < 0.0001). Greater site-level MAP (p < 0.0001) and colder site-level MAT (p < 0.0001) were correlated with longer SOC turnover times but were not correlated with SOC content. Our results suggest that poorly crystalline Fe and Al effectively slow the turnover of SOC in these acidic soils, even when their combined content in the soil is less than 2% by mass. However, in the strongly acidic Spodosol, organo-metal complexes tended to be less stable resulting in a more actively cycling mineral-associated SOC pool. We characterized mineralogy and carbon content in soils from four forested sites in the eastern deciduous forest zone that span three soil orders. Using selective chemical dissolution applied to the soil fraction containing mineral-associated carbon, we quantified the amount of Fe and Al in operationally-defined poorly crystalline and organically-complexed phases, and correlated these with previously published SOC content and 14C-based turnover times (McFarlane et al. 2013). This dataset comprises site characterization undertaken in 2007 (.png) and subsequent soil analyses (.csv containing C, Fe, Al, 14C-derived TurnoverTime) concluded in 2010.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Year 2024

A new version of this dataset is available at doi:10.15485/3001338 and is the first citation in the 'Related References' section. It is expands on this dataset by appending another water year of data collection and additional logger sites.This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across the East River basin from 3 October 2023 to 12 August 2024. Instruments were deployed at 14 sites as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. Time-lapse cameras were deployed at three sites. Imagery from sites AP BONUS and AP5 provides insight into large-scale seasonal snow cover variability. Imagery from site EL2 shows smaller-scale snow patterns across a nearby meadow.Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into short videos stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.Dataset update 2025-03-03: Resolved header and datetime formatting inconsistencies within LoggerData.zip files KP1_Air, KP3_Air, KP3_Ground, KP6_Ground, AP3_Ground, AP4_Air, and AP6_Air.Dataset update 2025-11-18: Modified abstract and related references sections to include new version of dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Temperature, Humidity, and Time-Lapse Video Data from Yosemite National Park, Water Year 2024

This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across Yosemite National Park from October 2023 to September 2024. Instruments were deployed at 10 sites in two cross-valley transects as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are intended to support hydrologic modeling efforts to better resolve the fate of mountain water, and are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. A time-lapse camera at one site captures valley-scale seasonal snow cover variability.Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into a short video stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Years 2024 and 2025

This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across the East River basin from 3 October 2023 to 8 August 2025. Instruments were deployed at 19 sites as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. Time-lapse cameras were deployed at three sites. Imagery from sites AP BONUS and AP5 (Avery Picnic) provides insight into large-scale seasonal snow cover variability. Imagery from site EL2 (Emerald Lake) shows smaller-scale snow patterns across a nearby meadow. Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into short videos stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluation of “Tight Oil” Well Performance and Completion Practices in the Powder River Basin - “Time Slice” Analysis

The objective of this paper is to assess how well completion practices and well performance have evolved over time (“time slices”) in the tight sands and shales of the Powder River Basin (PRB). This information can provide a foundation for helping operators define more effective well completion practices and thus optimize well performance in an emerging “tight oil” basin. To start, the authors assembled a “database” containing well completion practices, production data, and geologic information for more than 800 horizontal (Hz) wells targeting three “tight oil” formations -- Turner, Frontier, and Mowry--placed on production in the past dozen years. To control for the impact of geologic and reservoir properties on well performance, the authors defined 12 geologically distinct areas (“partitions”) for the Turner, Frontier, and Mowry Shale in the Powder River Basin (4 partitions in each formation). This paper discusses the methodology for establishing “time slices” for each partition within each of the three “tight oil” formations, including (1) taking out, to the extent practical, the effects of geology by partitioning the three “tight oil” formations based on their geologic parameters; (2) using type curves and estimated ultimate recoveries (EURs) to establish a reliable measure of well performance; and (3) rigorously inspecting the production and well completion data to assure a quality dataset. Partition #2, a high thermal maturity area of the Frontier Sandstone in the western Powder River Basin, helps illustrate the results of the study. Using a dataset of 55 Hz wells, the study found that well performance has steadily improved, with oil EURs increasing from 180 MBbl in 2012-13 to 290 MBbl in 2018-19. Much of this improvement was due to the use of longer Hz laterals, increasing notably from 3,765 ft in 2012-13 to 10,260 ft in 2018-19. More intensive completion practices contributed, as well. For example, the number of frac stages more than doubled, from 17 to 35, and proppant concentrations increased from 1,060 lbs/ft to 1,320 lbs/ft. Finally, the data show diminishing returns to longer laterals and more intensive completion practices. For example, the key performance measure of oil EUR per 1,000 ft of lateral is decreasing. For 2012-13, Hz wells recovered 50 MBbl of oil per 1,000 ft of lateral while the more recent 2018-19 Hz wells only provide 30 MBbl of oil per 1,000 ft of lateral. Considerable insight can be gained by using “time slices” and geologic partitioning to better understand the relationship between changes in well drilling and completion practices and changes in well performance in emerging “tight oil” plays. The results from this study can also serve as a foundation for subsequent, more intense efforts involving data analytics for defining more optimum well completion practices targeting specified geologic settings and formations.

02 PETROLEUM↗
DOE OSTI2020

Generating Uncertainty Distributions for Seismic Signal Onset Times

Signal arrival-time estimation plays a critical role in a variety of downstream seismic analyses, including location estimation and source characterization. Any arrival-time errors propagate through subsequent data-processing results. In this article, we detail a general framework for refining estimated seismic signal arrival times along with full estimation of their associated uncertainty. Using the standard short-term average/long-term average threshold algorithm to identify a search window, we demonstrate how to refine the pick estimate through two different approaches. In both cases, new waveform realizations are generated through bootstrap algorithms to produce full a posteriori estimates of uncertainty of onset arrival time of the seismic signal. The onset arrival uncertainty estimates provide additional data-derived information from the signal and have the potential to influence seismic analysis along several fronts.

58 GEOSCIENCES↗
DOE OSTI2022

rmap: An R package to plot and compare tabular data on customizable maps across scenarios and time

`rmap` is an R package that allows users to easily plot tabular data (CSV or R data frames) on maps without any Geographic Information Systems (GIS) knowledge. Maps produced by `rmap` are `ggplot` objects and thus capitalize on the flexibility and advancements of the `ggplot2` package and all elements of each map are thus fully customizable. Additionally `rmap` automatically detects and produces comparison maps if the data has multiple scenarios or time periods as well as animations for time series data. Advanced users can load their own shapefiles if desired. `rmap` comes with a range of pre-built color palettes but users can also provide any `R` color palette or create their own as needed. Four different legend types are available to highlight different kinds of data distributions. The input spatial data can be both gridded or polygon data. `rmap` is desgined in particular for comparing spatial data across scenarios and time periods and comes preloaded with standard country, state, and basin maps as well as custom maps compatible with the Global Change Analysis Model (GCAM) spatial boundaries. `rmap` has a growing number of users and its products have been used in multiple multisector dynamics publications as well as a required dependency in other R packages such as `rfasst` and `metis`. `rmap's` automatic processing of tabular data using pre-built map selection, difference map calculations, faceting, and animations offers unique functionality which makes it a powerful and yet simple tool for users looking to explore multi-sector, multi-scenario data across space and time.

58 GEOSCIENCES↗
DOE OSTI2023

Looking at supersymmetric black holes for a very long time

We study correlation functions for extremal supersymmetric black holes. It is necessary to take into account the strongly coupled nature of the boundary supergraviton mode. We consider the case with {\cal N}=2 supercharges which is the minimal amount of supersymmetry needed to give a large ground state degeneracy, separated from the continuum. Using the exact solution for this theory we derive formulas for the two point function and we also give integral expressions for any n n -point correlator. These correlators are time independent at large times and approach constant values that depend on the masses and couplings of the bulk theory. We also explain that in the non-supersymmetric case, the correlators develop a universal time dependence at long times. This paper is the longer companion paper of arXiv:2207.00407.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A time-dependent momentum-resolved scattering approach to core-level spectroscopies

While new light sources allow for unprecedented resolution in experiments with X-rays, a theoretical understanding of the scattering cross-section is lacking. In the particular case of strongly correlated electron systems, numerical techniques are quite limited, since conventional approaches rely on calculating a response function (Kramers-Heisenberg formula) that is obtained from a perturbative analysis of scattering processes in the frequency domain. This requires a knowledge of a full set of eigenstates in order to account for all intermediate processes away from equilibrium, limiting the applicability to small tractable systems. In this work, we present an alternative paradigm, recasting the problem in the time domain and explicitly solving the time-dependent Schrödinger equation without the limitations of perturbation theory: a faithful simulation of the scattering processes taking place in actual experiments, including photons and core electrons. We show how this approach can yield the full time and momentum resolved Resonant Inelastic X-Ray Scattering (RIXS) spectrum of strongly interacting many-body systems. We demonstrate the formalism with an application to Mott insulating Hubbard chains using the time-dependent density matrix renormalization group method, which does not require a priory knowledge of the eigenstates and can solve very large systems with dozens of orbitals. This approach can readily be applied to systems out of equilibrium without modification and generalized to other spectroscopies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Predicting Execution Times for Disk-based and In-Situ Parallel Data Analytics (Final Technical Report)

In recent years, there has been a significant amount of interests in in-situ analytics on simulation programs. For a variety of reasons, it is desirable to be able to predict the execution time of an analytics program. At the same time, frameworks such as MapReduce have become popular for scientific data analytics. This paper focuses on developing performance models for predicting execution time of parallel data analytics, with a special emphasis on in-situ analytics. We take two distinct approach towards performance prediction. We first expand SKOPE (a SKeleton framewOrk for Performance Exploration) with performance models for disk data read, cache performance, and page fault penalty. Second, an analytical performance model is also developed. We have evaluated our performance prediction framework as well as the analytical model on three hardware setups with well-known data mining algorithms implemented in three programming paradigms, MapReduce, MATE (a MapReduce-like parallel system with an alternate API for multi-core environments) and Smart (a MapReduce-like framework for in-situ analytics). Results show that our performance prediction framework along with the incorporated performance models are capable of accurately predicting execution times for parallel scientific analytics on different hardware setups.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Multigrid-Reduction-in-Time for the Rotating Shallow Water Equations

We consider multilevel time-parallel methods for the numerical solution of the rotating shallow water equations. In particular, the multigrid-reduction-in-time (MGRIT) algorithm is used for the parallel time integration. An asymptotic model is used at the coarse levels while the full model is employed at the finer levels. The asymptotic model is well-suited for highly oscillatory partial differential equations like the rotating shallow water equations because it can accurately and stably take the required large time-steps on coarse levels. Our work exploits the flexibility of the MGRIT algorithm in terms of the number of levels and relaxation schemes to show some computational benefits, especially with respect to FCF-relaxation and data reuse.

97 MATHEMATICS AND COMPUTING↗