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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

The use of TOMS data for tropospheric ozone studies

Instances were identified where enhancements of tropical ozone are associated with sporadic widespread biomass burning. A strong positive correlation exists between total ozone and the distribution of carbon monoxide in the tropics, indicating that total ozone data can be used for tropospheric ozone studies. Using ozone profiles derived from the Stratospheric Aerosol and Gas Experiment (SAGE), preliminary findings show enhanced tropospheric ozone concentrations over Africa and the eastern Atlantic at tropical latitudes. Other preliminary studies also suggest that Total Ozone Mapping Spectrometer (TOMS) data may provide a means of identifying widespread air pollution episodes over the United States during the summer.

Fishman, Jack↗
DOE OSTI2021

Analysis of Oil and Gas Ethane and Methane Emissions in the Southcentral and Eastern United States Using Four Seasons of Continuous Aircraft Ethane Measurements

In the last decade, much work has been done to better understand methane (CH 4 ) emissions from the oil and gas (O&G) industry in the United States. Ethane (C 2 H 6 ), a gas that is co-emitted with thermogenic sources of CH 4 , is emitted in the US predominantly by the O&G sector. Here, in this study, we perform an inverse analysis on 200 h of atmospheric boundary layer C 2 H 6 measurements to estimate C 2 H 6 emissions from the US O&G sector. Measurements were collected from 2017 to 2019 as part of the Atmospheric Carbon and Transport (ACT) America aircraft campaign and encompass much of the central and eastern United States. We find that for the fall, winter, and spring campaigns, C 2 H 6 data consistently exceeds values that would be expected based on EPA O&G leak rate estimates by more than 50%. C 2 H 6 observations from the summer 2019 data set show significantly lower C 2 H 6 enhancements in the southcentral region that cannot be reconciled with data from the other three seasons, either due to complex meteorological conditions or a temporal shift in the emissions. Combining the fall, winter, and spring C 2 H 6 posterior emissions estimate to an inventory of O&G CH 4 emissions, we estimate that O&G CH 4 emissions are larger than EPA inventory values by 48%–76%. Uncertainties in the gas composition data limit the accuracy of using C 2 H 6 as a proxy for O&G CH 4 emissions. These limits could be resolved retroactively by increasing the availability of industry-collected gas composition data.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

Stereoscopic computer graphics display system

Handbook was published on study which describes relative merits of two general-purpose, steroscopic display systems. Both systems are adaptable to most small data processing facilities and, with minimal hardware development, greatly enhance user ability to interact with computer and to interpret data output. Section also describes digital-to-analog converters designed for use with system.

Plott, H. H., Jr.↗
NASA NTRS1995

ROSAT observations of the Eridanus soft X-ray enhancement

We present maps of the Eridanus soft X-ray enhancement made from the ROSAT all-sky survey data. The maps are in two energy bands: 0.25 keV (0.12-0.284 keV at 10% response, the ROSAT R1 and R2 bands), and 0.75 keV (0.47-1.20 keV at 10% response, the ROSAT R4 and R5 bands). The entire enhancement spans a total of approximately 35 deg in Galactic latitude and approximately 20 deg in Galactic longitude, centered at l approximately 22 deg, b approximately -32 deg. While the higher spatial resolution of the ROSAT data reveals a pattern of detailed anticorrelations with 100 micrometers intensity that was not evident in the HEAO 1 or earlier data, the basic morphology found in previous maps of this enhancement is confirmed. The ROSAT data also clearly show that the X-ray enhancement continues up to the Orion region in the 0.75 keV band, confirming the structure suggested by the Wisconsin sky survey maps. The ROSAT data are consistent with the interpretation of Reynolds & Ogden and Burrows et al. that this enhancement, the higher temperature component of Burrows et al., is generated for the most part by several-million-degree gas filling a huge interstellar cavity. However, we interpret the low-temperature component discussed by Burrows et al. as part of a more distant large scale (greater than or equal to approximately 30 deg) diffuse background enhancement. The part of this softer X-ray enhancement examined here appears to be produced by million degree gas in the galactic halo, and is therfore unlikely to be an isolated bubble as suggested by Burrows et al. on the basis of lower resolution HEAO 1 data.

Snowden, S. L.↗
NASA NTRS2022

Cause of a Lower-Tropospheric High-Ozone Layer in Spring Over Hanoi

The ozonesonde observations in Hanoi, Vietnam, over fourteen years since 2004 have confirmed the enhancement in lower tropospheric ozone concentration at about 3 km altitude in the spring season. We investigated the evolution of the ozone enhancement from analysis of meteorological data, backward trajectories, and model sensitivity experiments. In spring, air masses over Hanoi exhibit strong height dependence. At 3km, the high-ozone air masses originate from the land area to the west of Hanoi, while low-ozone air masses below about 1.5 km are from the oceanic area to the east. Above 4 km, the air masses are mostly traced back to the farther west area. The chemical transport model simulations revealed that precursor emissions from biomass burning in the inland Indochina Peninsula have the largest contribution to the lower tropospheric ozone enhancement, which is transported upward and eastward and overhangs the clean air intrusion from the ocean to the east of Hanoi. At this height level, the polluted air has the horizontal extent of about 20 degrees in longitude and latitude. The polluted air observed in Hanoi is transported further east and widely spread over the northern Pacific Ocean.

tropospheric ozone↗
NASA NTRS2022

Cause of a Lower-Tropospheric High-Ozone Layer in Spring Over Hanoi

The ozonesonde observations in Hanoi, Vietnam, over fourteen years since 2004 have confirmed the enhancement in lower tropospheric ozone concentration at about 3 km altitude in the spring season. We investigated the evolution of the ozone enhancement from analysis of meteorological data, backward trajectories, and model sensitivity experiments. In spring, air masses over Hanoi exhibit strong height dependence. At 3km, the high-ozone air masses originate from the land area to the west of Hanoi, while low-ozone air masses below about 1.5 km are from the oceanic area to the east. Above 4 km, the air masses are mostly traced back to the farther west area. The chemical transport model simulations revealed that precursor emissions from biomass burning in the inland Indochina Peninsula have the largest contribution to the lower tropospheric ozone enhancement, which is transported upward and eastward and overhangs the clean air intrusion from the ocean to the east of Hanoi. At this height level, the polluted air has the horizontal extent of about 20 degrees in longitude and latitude. The polluted air observed in Hanoi is transported further east and widely spread over the northern Pacific Ocean.

Hanoi↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
NASA NTRS1985

Application of satellite data to tropic/subtropic moisture coupling

The objective is to develop analysis tools for use of satellite data to interpret synoptic-scale systems in data-void regions. Interim goals are to: (1) quantify the synoptic information content of satellite data; and (2) utilize these data in the diagnosis of moisture bursts in the eastern tropical Pacific Ocean. Researchers developed and implemented a statistical procedure for using TIROS N microwave data to infer infrared channel data for overcast conditions; they used the same procedure for deducing full TIROS N channel radiance profiles from NOAA 5 VTPR channel data over regions where the TIROS N data are missing. An empirical orthogonal function analysis of twice-daily channel radiance fields over the tropical eastern Pacific was completed. The vertically oriented eigenfunctions were interpreted in terms of typical meteorological events. The horizontal distribution of the eigenfunction amplitudes relates these meteorological signals to moisture bursts. A pair of moisture burst climatologies is complete: one of four years using infrared imagery (including the highly anomalous 1982 to 83 cold season); the other implementing 850 to 200 mb wind analyses in conjunction with GOES imagery. A number of different evaluations of the synoptic evolution of moisture fields (enhanced infrared imagery, moisture channel data, FGGE humidity analysis, and in situ station and sounding observations) are compared. All have limitations; all can be utilized together; all together are still less than adequate in the tropical Pacific.

Mcguirk, J. P.↗
DOE OSTI2022

Data-driven modeling of power generation for a coal power plant under cycling

Increased penetration of renewables for power generation has negatively impacted the dynamics of conventional fossil fuel-based power plants. The power plants operating on the base load are forced to cycle, to adjust to the fluctuating power demands. This results in an inefficient operation of the coal power plants, which leads up to higher operating losses. To overcome such operational challenge associated with cycling and to develop an optimal process control, this work analyzes a set of models for predicting power generation. Moreover, the power generation is intrinsically affected by the state of the power plant components, and therefore our model development also incorporates additional power plant process variables while forecasting the power generation. We present and compare multiple state-of-the-art forecasting data-driven methods for power generation to determine the most adequate and accurate model. We also develop an interpretable attention-based transformer model to explain the importance of process variables during training and forecasting. The trained deep neural network (DNN) LSTM model has good accuracy in predicting gross power generation under various prediction horizons with/without cycling events and outperforms the other models for long-term forecasting. The DNN memory-based models show significant superiority over other state-of-the-art machine learning models for short, medium and long range predictions. The transformer-based model with attention enhances the selection of historical data for multi-horizon forecasting, and also allows to interpret the significance of internal power plant components on the power generation. This newly gained insights can be used by operation engineers to anticipate and monitor the health of power plant equipment during high cycling periods.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Adversarial super-resolution of climatological wind and solar data

Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a 50 × resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report.

14 SOLAR ENERGY↗
NASA NTRS1990

Ground Optical Lightning Detector (GOLD)

A photometer developed to characterize lightning from the ground is discussed. The detector and the electronic signal processing and data storage systems are presented along with field data measured by the system. The discussion will include improvements that will be incorporated to enhance the measurement of lightning and the data storage capability to record for many days without human involvement. Finally, the calibration of the GOLD system is presented.

Jackson, John, Jr.↗
DOE OSTI2026

Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing

Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

Agricultural Sciences↗
DOE OSTI2021

Enhanced Ion Conductivity through Hydrated, Polyelectrolyte-Grafted Cellulose Nanocrystal Films

Here,iIonically conductive, hydrated polyelectrolyte nanocomposites are prepared from iodomethane-treated poly(2-vinylpyridine) (mPV)-grafted cellulose nanocrystals (MxG-CNC-g-mPV). These polyelectrolyte-grafted nanoparticle (PEGN) films exhibit an order-of-magnitude higher iodide ion conductivity relative to mPV films and a high in-plane/through-plane anisotropy. The PEGN architecture prevents CNC aggregation, maximizing the CNC/polyelectrolyte interface. PEGN films were prepared with varying polymer graft density (0.03-0.12 chains/nm 2 ) and molecular weight (7k-30k g/mol). The greatest ion conductivity enhancement is observed with lower molecular weight, higher density grafts: ca. 89 ± 6 mS/cm (140 ±10 mS/cm accounting for the volume of the CNC) versus 3.3 ± 0.4 mS/cm for ungrafted mPV. Poly(styrene-block-2-vinylpyridine)-grafted CNCs were prepared in which the insulating polystyrene block or the conducting mPV block was directly attached to the CNCs; only the films with the mPV block closest to the CNCs exhibited an enhancement in conductivity relative to mPV. Together, these data point to beneficial CNC/polyelectrolyte interfacial effects, resulting in significant ionic conductivity enhancement along the length of the CNCs in these films.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Enhancing trap and fault seal analyses by integrating observations from HR3D seismic data with well logs and conventional 3D seismic data, Texas inner shelf

An understanding of trap and fault seal quality is critical for assessing hydrocarbon prospectivity. To achieve this, modern analytical techniques leverage well data and conventional industry-standard 3D seismic data to evaluate the trap, and any faults displacing the reservoir and top seal intervals. Above all, geological interpretation provides the framework of trap and fault seal analyses, but can be hindered by the data resolution, quality and acquisition style of the conventional seismic data. Furthermore, limiting the analysis to only the petroleum system at depth may lead to erroneous perceptions because interpreting overburden features, such as shallow faults or gas chimneys, can provide valuable observations with respect to container performance, and can to help validate trap and fault seal predictions. Here, a supplement to conventional 3D data are high-resolution 3D seismic (HR3D) data, which provide detailed images of the overburden geology. This study utilizes an HR3D seismic volume in the San Luis Pass area of the Texas inner shelf, where shallow fault tips and a sizeable gas chimney are interpreted over an unsuccessful hydrocarbon prospect. Static post-drill fault seal and trap analyses suggest that the primary fault displacing the structural closure could have withheld columns of gas c. 100 m high, but disagree with our HR3D seismic interpretations and dry-well analyses. From our results, we hypothesize that tertiary gas migration through fault conduits reduced the hydrocarbon column in the prospective Early Miocene reservoir, and may have resulted from continued movement along the intersecting faults. Overall, this study reinforces the importance of understanding the overburden geology and geohistory of faulted prospects, and demonstrates the utility of pre-drill HR3D acquisition when conducting trap and fault seal analyses.

3D seismic↗
DOE OSTI2026

Asi Nuclear Energy Sensors Data Portal Chatbot And Data Structuring Tool

The Idaho National Laboratory (INL) is advancing the development of an AI-powered chatbot and data structuring tool specifically designed to accelerate data mining processes for sensor-related information and seamlessly integrate the results into the ASI Sensors Data Portal (https://nes.energy.gov/). By doing so, the software aims to enhance the accessibility, usability, and organization of sensor data for nuclear energy applications. The software initial phase focuses on retrieving comprehensive datasets, prioritizing the past five years of publicly available information from the Office of Scientific and Technical Information (OSTI). These datasets will be meticulously processed to ensure compatibility, employing cleaning and preprocessing steps to eliminate irrelevant, incomplete, or corrupted information, thus establishing a robust foundation for subsequent AI use. The data will serve as the backbone for training an AI model and chatbot, which will act as an interactive tool enabling users to ask complex, context-specific questions and receive accurate, validated answers derived from constrained literature. In parallel, the project incorporates a data structuring process supported by AI to organize sensor information from multiple sources into a standardized format. This structured data will include detailed sensor specifications, such as measurement range, applications, accuracy, and operating conditions, generated and documented with AI. These specifications will be systematically integrated into the sensor portal. To maintain the highest levels of accuracy and relevance, all AI-generated outputs will be reviewed and validated by subject matter experts (SMEs), with additional fields or parameters added as needed. Future stages of the project aim to expand the dataset beyond OSTI to include other sources and potentially incorporate unclassified controlled information (UCI) with restricted access protocols to address security and confidentiality requirements.

Mapes, NormanJ. [Idaho National Laboratory (INL), ↗
DOE OSTI2024

Data for the Expression of Genes Encoding Novel Sesame Oleosin Variants Facilitates Enhanced Triacylglycerol Accumulation in Arabidopsis Leaves and Seeds

Triacylglycerols (TAG), accumulate within lipid droplets (LD), predominantly surrounded by OLEOSINs (OLE), that protect TAG from hydrolysis. We tested the hypothesis that identifying and removing degradation signals from OLE would promote its abundance, preventing TAG degradation and enhancing TAG accumulation. We tested whether mutating potential ubiquitin-conjugation sites in a previously reported improved Sesamum indicum OLE ( SiO ) variant, o3-3 Cys-OLE ( SiCO herein), would stabilize it and increase its lipogenic potential. SiCOv1 was created by replacing all five lysines in SiCO with arginines. Separately, six cysteine residues within SiCO were deleted to create SiCOv2. SiCOv1 and SiCOv2 mutations were combined to create SiCOv3. Transient expression of SiCOv3 in Nicotiana benthamiana increased TAG by two-fold relative to SiCO. Constitutive expression of SiCOv3 or SiCOv5 , containing the five predominant TAG-increasing mutations from SiCOv3 , in Arabidopsis along with mouse DGAT2 ( mD ) increased TAG accumulation by 54% in leaves and 13% in seeds compared with control lines coexpressing SiCO and mD . Lipid synthesis rates increased, consistent with an increase in lipid sink strength that sequesters newly synthesized TAG, thereby relieving the constitutive BADC-dependent inhibition of ACCase reported for WT Arabidopsis. These OLE variants represent novel factors for potentially increasing TAG accumulation in a variety of oil crops.

Feedstock Production↗
DOE OSTI2026

Multidimensional scaling informed by F -statistic: Visualizing grouped microbiome data with inference

Multidimensional scaling (MDS) is a widely used dimensionality reduction technique in microbial ecology data analysis that captures the multivariate structure of the data while preserving pairwise distances between samples. While improvements in MDS have enhanced the ability to reveal group-specific data patterns, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. Here, in this study, we introduce a new MDS-based ordination method, “F-informed MDS,” which configures the data distribution based on the F-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using semisynthetic datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that F-informed MDS is comparable to state-of-the-art methods in preserving both local and global data structures. Its application to a diatom-associated bacterial community suggests the role of this new method in interpreting the community’s response to the host. Our approach offers a well-founded refinement of MDS that aligns with statistical test results, which can be beneficial for broader multidimensional data analyses in microbiology and ecology. This new visualization tool can be incorporated into standard microbiome data analyses.

Biological and medical sciences↗