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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

The Double-edged Sword of Data-driven Super-Resolution: Adversarial Super-resolution Models

Data-driven super-resolution (SR) methods are often integrated into imaging pipelines as preprocessing steps to improve downstream tasks such as classification and detection. However, these SR models introduce a previously unexplored attack surface into imaging pipelines. In this paper, we present AdvSR, a framework demonstrating that adversarial behavior can be embedded directly into SR model weights during training, requiring no access to inputs at inference time. Unlike prior attacks that perturb inputs or rely on backdoor triggers, AdvSR operates entirely at the model level. By jointly optimizing for reconstruction quality and targeted adversarial outcomes, AdvSR produces models that appear benign under standard image quality metrics while inducing downstream misclassification. We evaluate AdvSR on three SR architectures (SRCNN, EDSR, SwinIR) paired with a YOLOv11 classifier and demonstrate that AdvSR models can achieve high attack success rates with minimal quality degradation. These findings highlight a new model-level threat for imaging pipelines, with implications for how practitioners source and validate models in safety-critical applications.

Sullivan, Haley [ORNL] (ORCID:0000000274069217)↗
DOE OSTI2022

Brownian bridge-based speed imputation technique for truck energy consumption and emissions estimation

The available truck Global Positioning System (GPS) data, typically collected with large time gaps, rely on imputation techniques to obtain second-by-second data that are required in models for estimating truck energy consumption and emissions. However, existing speed imputation techniques either require a large amount of high-resolution data for model training or rely on special movement assumptions. Here, to fill the gap and effectively apply the low-resolution truck GPS datasets, this paper proposes a simple imputation technique that adopts the Brownian bridge structure to impute missing speed data. The proposed technique introduces a feasible imputation region and a combined drift into the imputation procedure to capture vehicle acceleration constraint, travel distance constraint, and speed volatility. The calibrated model is applied to a set of low-resolution truck GPS data. The results demonstrate the robustness of the proposed technique in enhancing estimation accuracy when using low-resolution GPS data to estimate fuel consumption and emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2015

Forest Disturbance Across the Conterminous United States from 1985-2012: The Emerging Dominance of Forest Decline

Evidence of shifting dominance among major forest disturbance agent classes regionally to globally has been emerging in the literature. For example, climate-related stress and secondary stressors on forests (e.g., insect and disease, fire) have dramatically increased since the turn of the century globally, while harvest rates in the western US and elsewhere have declined. For shifts to be quantified, accurate historical forest disturbance estimates are required as a baseline for examining current trends. We report annual disturbance rates (with uncertainties) in the aggregate and by major change causal agent class for the conterminous US and five geographic subregions between 1985 and 2012. Results are based on human interpretations of Landsat time series from a probability sample of 7200 plots (30 m) distributed throughout the study area. Forest disturbance information was recorded with a Landsat time series visualization and data collection tool that incorporates ancillary high-resolution data. National rates of disturbance varied between 1.5% and 4.5% of forest area per year, with trends being strongly affected by shifting dominance among specific disturbance agent influences at the regional scale. Throughout the time series, national harvest disturbance rates varied between one and two percent, and were largely a function of harvest in the more heavily forested regions of the US (Mountain West, Northeast, and Southeast). During the first part of the time series, national disturbance rates largely reflected trends in harvest disturbance. Beginning in the mid-90s, forest decline-related disturbances associated with diminishing forest health (e.g., physiological stress leading to tree canopy cover loss, increases in tree mortality above background levels), especially in the Mountain West and Lowland West regions of the US, increased dramatically. Consequently, national disturbance rates greatly increased by 2000, and remained high for much of the decade. Decline-related disturbance rates reached as high as 8% per year in the western regions during the early-2000s. Although low compared to harvest and decline, fire disturbance rates also increased in the early- to mid-2000s. We segmented annual decline-related disturbance rates to distinguish between newly impacted areas and areas undergoing gradual but consistent decline over multiple years. We also translated Landsat reflectance change into tree canopy cover change information for greater relevance to ecosystem modelers and forest managers, who can derive better understanding of forest-climate interactions and better adapt management strategies to changing climate regimes. Similar studies could be carried out for other countries where there are sufficient Landsat data and historic temporal snapshots of high-resolution imagery.

Cohen, Warren B.↗
NASA NTRS1999

Regional Studies and Applications with a Variable Resolution Stretched Grid Data Assimilation System

The variable resolution stretched grid (SG) version of the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) incorporating the GEOS SG-GCM, is used for regional analysis, forecast, and climate applications. The region of interest with enhanced horizontal resolution, mostly used in experiments, is a rectangle over the U.S. The SG-DAS is capable of reproducing regional mesoscale fields, patterns and diagnostics that are not produced by the medium uniform resolution run with the same amount of grid points as for the SG. The SG-DAS regional analyses and diagnostics are used for: validation of regional climate simulation experiments produced with the SG-GCM for the U.S. 1988 summer drought; and are planned to be used for atmospheric chemistry transport experiments. Also, a case study is conducted on a super-typhoon development in December 1997. The SG-DAS appears to be a viable candidate for a variety of regional studies and applications.

Fox-Rabinovitz, Michael↗
NASA NTRS1984

Tropospheric-stratospheric exchange, part 1.1A

Much of the observational evidence of large scale tropospheric-stratospheric exchange has been obtained by radiosonde and satellite radiane data. So far mesosphere-stratosphere-troposphere (MST) radars have made mininal contributions, in part due to their recent use as a meteorological tool, intermittent operation at some facilities and sparse geographic distribution. However, as more MST facilities come on-line in more locations, the good time and height resolution data throughtout the troposphere and much of the stratosphere obtainable by MST radars will enhance the detail of stratospheric and tropospheric circulations and interactions. On smaller scales MST radars have already been used to examine convective forcing from the troposphere into the stratosphere and subsequent launching of gravity waves (LARSEN et al., 1982). Observations of persistent turbulent layers in the stratosphere over Arecibo, attributable to inertial oscillations, appear to propagate away from a source region near the tropopause (SATO and WOODMAN, 1982). MST radars offer the availability of high resolution wind data in height and time needed to observe interactions between the troposphere and stratosphere. The lack of geographic coverage (e.g., equatorial regions) and insufficient data bases at many MST facilities presently inhibit studies of large-scale interactions. At present MST radars can be used to examine smaller scale interactions.

Cornish, C. R.↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Hyper‐Local Temperature Prediction Using Detailed Urban Climate Informatics

The accurate modeling of urban microclimate is a challenging task given the high surface heterogeneity of urban land cover and the vertical structure of street morphology. Recent years have witnessed significant efforts in numerical modeling and data collection of the urban environment. Nonetheless, it is difficult for the physical‐based models to fully utilize the high‐resolution data under the constraints of computing resources. The advancement in machine learning (ML) techniques offers the computational strength to handle the massive volume of data. In this study, we proposed a modeling framework that uses ML approach to estimate point‐scale street‐level air temperature from the urban‐resolving meso‐scale climate model and a suite of hyper‐resolution urban geospatial data sets, including three‐dimensional urban morphology, parcel‐level land use inventory, and weather observations from a sensor network. We implemented this approach in the City of Chicago as a case study to demonstrate the capability of the framework. The proposed approach vastly improves the resolution of temperature predictions in cities, which will help the city with walkability, drivability, and heat‐related behavioral studies. Moreover, we tested the model's reliability on out‐of‐sample locations to investigate the modeling uncertainties and the application potentials to the other areas. This study aims to gain insights into next‐gen urban climate modeling and guide the observation efforts in cities to build the strength for the holistic understanding of urban microclimate dynamics.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1983

Preliminary airborne imaging spectrometer vegetation data

An airborne, pushbroom-style multispectral (1.2-2.4 micron) imaging sensor system known as Airborne Imaging Spectrometer (AIS) has been flown over the Pico Anticline region of the Santa Susana Mountains, California (U.S.A.). Initial interpretation of preliminary data sets suggest that the high spectral resolution data from AIS provide superior vegetation discrimination and mapping capabilities when compared with other imaging systems (MSS, TMS). AIS spectral data may also contain information concerning the state of health of both native and cultivated vegetation. Such capabilities (discrimination, mapping, and assessment of state of health) will be most useful in geobotanical assessment of this and other areas.

Rock, B. N.↗
NASA NTRS1984

Remote detection of geobotanical anomalies associated with hydrocarbon microseepage using thematic mapper simulator (TMS) and airborne imaging spectrometer (AIS) data

An interpretation of TMS and AIS data sets collected from Lost River, West Virginia, are presented, along with a brief review of the supervised vegetation classification approach to vegetation mapping used at Lost River. A preliminary study of AIS data suggests that contiguous high-spectral resolution data from a very limited portion of the spectrum (1.2-1.5 micron) provide a greater discriminatory capability than do broad-band sensors such as the TMS covering of wider spectral range (0.45-2.35 microns).

Rock, B. N.↗
NASA NTRS1999

An Improved Optical Spectral Line List for RR Telescopii

The symbiotic nova RR Telescopli has been observed with the 3.9m telescope of the Anglo-Australian Observatory (AAO), using the University College London Echelle Spectrograph in conjunction with a Tek CCD. It displays a rich emission line spectrum, ranging in excitation from N I to [Ni VIII]. We present a list of 824 measured lines, with their suggested identifications and absolute line intensities, covering a wavelength range from 3180 A to 9455 A. The absolute line intensities have been derived by comparing the high resolution data with a flux-calibrated low resolution spectrum taken with the Australian National University 2.3 m telescope. All of the lines have been successfully identified. Comparing our results with those of previous studies indicates that the RR Tel system is advancing towards higher degrees of ionization.

Crawford, F. L.↗
NASA NTRS2000

Eruption Evolution of Major Volcanoes on Io: Galileo Takes a Close Look

Using Galileo Near Infrared Mapping Spectrometer (NIMS) and Solid State Imaging Experiment (SSI) data it has been possible to monitor the course of individual eruptions on Io. Analysis of pre-I24 data has been aided by high-resolution data obtained during the I24 and I25 encounters.

Davies, A. G.↗
NASA NTRS2012

Using NASA's Giovanni System to Simulate Time-Series Stations in the Outflow Region of California's Eel River

Oceanographic time-series stations provide vital data for the monitoring of oceanic processes, particularly those associated with trends over time and interannual variability. There are likely numerous locations where the establishment of a time-series station would be desirable, but for reasons of funding or logistics, such establishment may not be feasible. An alternative to an operational time-series station is monitoring of sites via remote sensing. In this study, the NASA Giovanni data system is employed to simulate the establishment of two time-series stations near the outflow region of California s Eel River, which carries a high sediment load. Previous time-series analysis of this location (Acker et al. 2009) indicated that remotely-sensed chl a exhibits a statistically significant increasing trend during summer (low flow) months, but no apparent trend during winter (high flow) months. Examination of several newly-available ocean data parameters in Giovanni, including 8-day resolution data, demonstrates the differences in ocean parameter trends at the two locations compared to regionally-averaged time-series. The hypothesis that the increased summer chl a values are related to increasing SST is evaluated, and the signature of the Eel River plume is defined with ocean optical parameters.

Acker, James G.↗
NASA NTRS2013

Spatial and Temporal Dust Source Variability in Northern China Identified Using Advanced Remote Sensing Analysis

The aim of this research is to provide a detailed characterization of spatial patterns and temporal trends in the regional and local dust source areas within the desert of the Alashan Prefecture (Inner Mongolia, China). This problem was approached through multi-scale remote sensing analysis of vegetation changes. The primary requirements for this regional analysis are high spatial and spectral resolution data, accurate spectral calibration and good temporal resolution with a suitable temporal baseline. Landsat analysis and field validation along with the low spatial resolution classifications from MODIS and AVHRR are combined to provide a reliable characterization of the different potential dust-producing sources. The representation of intra-annual and inter-annual Normalized Difference Vegetation Index (NDVI) trend to assess land cover discrimination for mapping potential dust source using MODIS and AVHRR at larger scale is enhanced by Landsat Spectral Mixing Analysis (SMA). The combined methodology is to determine the extent to which Landsat can distinguish important soils types in order to better understand how soil reflectance behaves at seasonal and inter-annual timescales. As a final result mapping soil surface properties using SMA is representative of responses of different land and soil cover previously identified by NDVI trend. The results could be used in dust emission models even if they are not reflecting aggregate formation, soil stability or particle coatings showing to be critical for accurately represent dust source over different regional and local emitting areas.

spectral mixing analysis↗
NASA NTRS2008

Services, Perspective and Directions of the Space Physics Data Facility

The multi-mission data and orbit services of NASA's Space Physics Data Facility (SPDF) project offer unique capabilities supporting science of the Heliophysics Great Observatory and that are highly complementary to other services now evolving in the international heliophysics data environment. The VSPO (Virtual Space Physics Observatory) service is an active portal to a wide rage of distributed data sources. CDAWeb (Coordinated Data Analysis Web) offers plots, listings and file downloads for current data from many missions across the boundaries of missions and instrument types. CDAWeb now includes extensive new data from STEREO and THEMIS, plus new ROCSAT IPEI data, the latest data from all four TIMED instruments and high-resolution data from all DE-2 experiments. SSCWeb, Helioweb and out 3D Animated Orbit Viewer (TIPSOD) provide position data and identification of spacecraft and ground conjunctions. OMNI Web, with its new extension to 1- and 5-minute resolution, provides interplanetary parameters at the Earth's bow shock. SPDF maintains NASA's CDF (Common Data Format) standard and a range of associated tools including format translation services. These capabilities are all now available through web services based APIs, one element in SPDF's ongoing work to enable heliophysics community development of Virtual discipline Observatories (e.g. VITMO). We will demonstrate out latest data and capabilities, review the lessons we continue to learn in what science users need and value in this class of services, and discuss out current thinking to the future role and appropriate focus of the SPDF effort in the evolving and increasingly distributed heliophysics data environment.

McGuire, Robert E.↗
DOE OSTI2020

Recent Development of Frequency Estimation Methods for Future Smart Grid

The frequency estimated by the Phasor Measurement Unit (PMU) is a critical index of power system status and supports many smart grid applications. The future smart grid features high penetration of renewables and more fast-moving power electronics inverters but raises challenges to the reliable frequency estimation. This article presents three methods to address these challenges. First, an enhanced zero-crossing algorithm was developed to track the fast-changing frequency in system dynamics. Second, we propose a technology that can tolerate the system transient and suppress the outliers. Third, an algorithm was developed to export high time-resolution frequency estimations with minimum computational effort. All of the proposed methods are realized in hardware and compared with classical frequency estimation methods. The testing results indicate that the proposed methods have excellent performance. They can be used in future PMUs and provide reliable and high time resolution data for smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗