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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

From observation to replication: machine-learning-driven quantification and replication of fine-scale fish kinematics and behavior

Long-term quantification of fish behavior is essential for aquatic ecology, wildlife telemetry, and biomechanical device development. However, the observation duration required to obtain reliable behavioral and kinematic metrics remains unclear, and few tools exist to physically reproduce natural swimming motion for controlled experimentation. We address these challenges by developing a generalizable framework that models behavioral reliability (Spearman–Brown reliability index) as a function of observation duration and derives metric-specific monitoring thresholds. Using juvenile white sturgeon (Acipenser transmontanus) as a case study, we demonstrate that the minimum duration needed for reliable estimates varies substantially across kinematic features: to exceed a reliability of 0.8, total distance traveled requires 12 days, average curvature (mm?¹) 15 days, tail-beat frequency (Hz) 8 days, and average speed (body length/s) 17 days. We further bridge digital analysis and physical testing by developing a hardware-in-the-loop simulator that reconstructs machine-learning-derived swimming kinematics with high fidelity (correlation coefficient 0.98–0.99, RMSE 1.22–1.27 mm over a 5-minute segment). This platform enables realistic, repeatable motion stimuli for evaluating aquatic sensing technologies and bio-integrated devices under controlled conditions. Together, these contributions provide a scalable approach for designing long-term behavioral studies and a data-driven connection between ecological observation and robotic experimentation.

Hwang, SungJoo↗
DOE OSTI2022

Stochastic agent-based model for predicting turbine-scale raptor movements during updraft-subsidized directional flights

Rapid expansion of wind energy development across the world has highlighted the need to better understand turbine-caused avian mortality. The risk to golden eagles (Aquila chrysaetos) is of particular concern due to their small population size and conservation status. Golden eagles subsidize their flight in part by soaring in orographic updrafts, which can place them in conflict with wind turbines utilizing the same low-altitude wind resource. Understanding the behavior of soaring raptors in varying atmospheric conditions can therefore be relevant to predicting and mitigating their risk of collision. We present a predictive movement model that simulates individual paths of golden eagles during directional flight (such as migration) that is subsidized by orographic updraft. We modeled eagles in a 50 km by 50 km study area in Wyoming containing three wind power plants with documented golden eagle collisions with turbines. The movement model is applicable to any region where ground elevation is known at turbine scale (50 m) and wind conditions are known at facility scale (3 km). For a given set of atmospheric conditions, the model simulates movements of thousands of orographic soaring eagles to produce a density map quantifying the relative probability of eagle presence. We validated the simulated tracks with GPS telemetry data showing four directional tracks made by golden eagles transiting through the area in 2019 and 2020. For each eagle track, validation was performed using the ratio of the model-simulated eagle presence likelihood with uniform eagle presence and the presence computed using directed random-walk movements. We found that the predictive performance of the model was significantly better (likelihood ratio 1) for low-altitude movements than high-altitude movements that can involve thermal-soaring. We employed the model to produce seasonal presence maps for migrating golden eagles. We found significant turbine-level variations in eagle presence between northerly and southerly migration routes through the study area. Overall, the proposed model offers a generalizable, probabilistic, and predictive tool to assist wind energy developers, ecologists, wildlife managers, and industry consultants in estimating the potential for conflict between soaring birds and wind turbines, thereby reducing the need for site-specific data on golden eagle movements.

17 WIND ENERGY↗
DOE OSTI2018

Fundamental understanding and rational design of high energy structural microbatteries

Microbatteries play a critical role in determining the lifetime of downsized sensors, wearable devices and medical applications, etc. More often, structural batteries are required from the perspective of aesthetics and space utilization, which is however rarely explored. Herein, we discuss the fundamental issues associated with the rational design of practically usable high energy microbatteries. The tubular shape of the cell further allows the flexible integration of microelectronics. A functioning acoustic micro-transmitter continuously powered by this tubular battery has been successfully demonstrated. Multiple design features adopted to accommodate large mechanical stress during the rolling process are discussed providing new insights in designing the structural microbatteries for emerging technologies.

25 ENERGY STORAGE↗
DOE OSTI2022

Estimating snow cover from high-resolution satellite imagery by thresholding blue wavelengths

We report the extent and duration of snow cover, a critical component of the hydrologic cycle and the global climate system, is expected to shift dramatically under climate change. Therefore, developing high-resolution assessments of snow cover change is crucial for estimating the impact of changing snow cover on watershed and ecosystems processes in cold regions. Remote sensing tools provide a powerful method for mapping snow-covered area (SCA) across a landscape. The most common method for estimating SCA utilizes the normalized difference snow index (NDSI), which relies on spectral measurements in the shortwave-infrared wavelengths (SWIR). NDSI can effectively estimate catchment- to regional-scale SCA, but it cannot be used to assess fine-scale SCA because of current limitations on the spatial resolution of satellite-derived SWIR measurements. Here, we map SCA using a threshold of blue wavelengths and high-resolution satellite imagery. The thresholding method, which we call the Blue Snow Threshold algorithm (BST), has previously been used with digital camera imagery. We refine and automate the algorithm for use with cloud-free high-resolution satellite imagery and find that the BST can be used to assess fine-scale SCA. For validation, we compared BST-derived estimates of SCA to a) airborne lidar surveys, b) Landsat fractional SCA, and c) snow disappearance dates from Snow Telemetry (SNOTEL) stations. When compared to airborne lidar surveys of SCA, the BST predicted SCA had a range of F-scores between 0.81 and 0.94 in four study areas in California and Colorado. We also found general agreement between SCA and snow disappearance at multiple SNOTEL sites across the western United States. Given the relatively recent availability of high-resolution satellite imagery with spectral measurements in the visible wavelengths but lacking in SWIR, the BST offers a reliable and easy-to-apply tool for examining fine-scale snow-related processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Real-Time Monitoring of Gas-Phase and Dissolved CO 2 Using a Mixed-Matrix Composite Integrated Fiber Optic Sensor for Carbon Storage Application

Novel chemical sensors that improve detection and quantification of CO 2 are critical to ensuring safe and cost-effective monitoring of carbon storage sites. Fiber optic (FO) based chemical sensor systems are promising field-deployable systems for real-time monitoring of CO 2 in geological formations for long-range distributed sensing. Here, a mixed-matrix composite integrated FO sensor system was developed with a purely optical readout that reliably operates as a detector for gas-phase and dissolved CO 2 . A mixed-matrix composite sensor coating consisting of plasmonic nanocrystals and hydrophobic zeolite embedded in a polymer matrix was integrated on the FO sensor. The mixed-matrix composite FO sensor showed excellent reversibility/stability in a high humidity environment and sensitivity to gas-phase CO 2 over a large concentration range. This remarkable sensing performance was enabled by using plasmonic nanocrystals to significantly enhance the sensitivity and a hydrophobic zeolite to effectively mitigate interference from water vapor. The sensor exhibited the ability to sense CO 2 in the presence of other geologically relevant gases, which is of importance for applications in geological formations. A prototype FO sensor configuration which possesses a robust sensing capability for monitoring dissolved CO 2 in natural water was demonstrated. Reproducibility was confirmed over many cycles, both in a laboratory setting and in the field. More importantly, we demonstrated on-line monitoring capabilities with a wireless telemetry system, which transferred the data from the field to a website. The combination of outstanding CO 2 sensing properties and facile coating processability makes this mixed-matrix composite FO sensor a good candidate suitable for practical carbon storage applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Observed Spatiotemporal Changes in the Mechanisms of Extreme Water Available for Runoff in the Western United States

We used a quality-controlled Snowpack Telemetry (SNOTEL) data set (1979–2017) combined with the nonparametric regional Kendall test (RKT) to examine changes in annual maximum water available for runoff at the land surface under four hydrometeorological conditions (snowmelt only, rain-on-snow, all melt with or without rainfall, and all melt plus rainfall on snow-free ground) over the mountainous regions of the western U.S. Our RKT analyses indicated significant declining trends at point (SNOTEL) scale under all four conditions. The annual maximum of all melt plus rainfall decreased primarily in the southwestern U.S., while the frequency of rain-on-snow events increased significantly in the northwestern U.S. The rate of annual maximum snowmelt only decreased in 10 of 11 ecological regions across the western U.S. These results suggest that under a warming climate, the four types of annual maximum water available for runoff have been substantially decreasing at local scale across the western U.S.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Forest Canopy Density Effects on Snowpack Across the Climate Gradients of the Western United States Mountain Ranges

Limitations inherent to field studies such as limited spatial and temporal coverage has made it very challenging, if not impossible, to explicitly disentangle the effect of canopy density from climate on snowpack dynamics. Hence, our understanding of how variations in canopy density can affect snow processes under diverse climate conditions has been limited. To address this knowledge gap, this study uses a physics-based modeling approach to represent most key processes that drive snow accumulation and melt in the open and forest with varied forest density, across the climate gradients of the Western U.S. mountain ranges, as represented by 228 Snow Telemetry (SNOTEL) locations. Simulations suggest that, under most winter climate conditions, canopy density exerts a strong influence on under-canopy snowpack dynamics and their controlling processes. Changing canopy density can shift the direction of the modeled differential peak snow accumulation and snow disappearance date (SDD) between the open and forest. For example, while simulated snowpack lasts longer in the open than the high-density forest at most wet/warm and dry/cold locations by up to 55 days, about 90% of all locations show a longer snow duration in the low-density forest by up to 27 days as compared to the open. Interannual variability in climate can also change the magnitude and direction of canopy impact on snow processes. By comparing model simulations to published field observations, and we also identify conditions that require additional field observations to improve process-scale understanding of forest-snow interaction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Implementation and Evaluation of a Unified Turbulence Parameterization Throughout the Canopy and Roughness Sublayer in Noah‐MP Snow Simulations

Abstract The Noah‐MP land surface model (LSM) relies on the Monin‐Obukhov (M‐O) Similarity Theory (MOST) to calculate land‐atmosphere exchanges of water, energy, and momentum fluxes. However, MOST flux‐profile relationships neglect canopy‐induced turbulence in the roughness sublayer (RSL) and parameterize within‐canopy turbulence in an ad hoc manner. We implement a new physics scheme (M‐O‐RSL) into Noah‐MP that explicitly parameterizes turbulence in RSL. We compare Noah‐MP simulations employing the M‐O‐RSL scheme (M‐O‐RSL simulations) and the default M‐O scheme (M‐O simulations) against observations obtained from 647 Snow Telemetry (SNOTEL) stations and two AmeriFlux stations in the western United States. M‐O‐RSL simulations of snow water equivalent (SWE) outperform M‐O simulations over 64% and 69% of SNOTEL sites in terms of root‐mean‐square‐error (RMSE) and correlation, respectively. The largest improvements in skill for M‐O‐RSL occur over closed shrubland sites, and the largest degradations in skill occur over deciduous broadleaf forest sites. Differences between M‐O and M‐O‐RSL simulated snowpack are primarily attributable to differences in aerodynamic conductance for heat underneath the canopy top, which modulates sensible heat flux. Differences between M‐O and M‐O‐RSL within‐canopy and below‐canopy sensible heat fluxes affect the amount of heat transported into snowpack and hence change snowmelt when temperatures are close to or above the melting point. The surface energy budget analysis over two AmeriFlux stations shows that differences between M‐O and M‐O‐RSL simulations can be smaller than other model biases (e.g., surface albedo). We intend for the M‐O‐RSL physics scheme to improve performance and uncertainty estimates in weather and hydrological applications that rely on Noah‐MP.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

Forward Modeling of 3-D Ion Properties in Jupiter’s Magnetosphere Using Juno/JADE-I Data

The Jovian Auroral Distributions Experiment Ion sensor (JADE-I) on NASA’s Juno mission provides in-situ measurements of ions from 0.1 to 46.2 keV/q inside Jupiter’s magnetosphere. JADE-I is used to study the plasma with two types of datasets from the same measurement: Time-of-flight (TOF) and SPECIES. The TOF dataset provides mass-per-charge measurements with a range of 1–64 amu/q but oversamples particles over 6π steradian viewing per spacecraft spin and has little directional information. On the other hand, the SPECIES dataset can provide a good measurement of the flow direction but does not provide mass-per-charge information due to the telemetry limit. In this study, we developed a 2-step forward modeling method that combines the advantages and avoids the disadvantages of TOF and SPECIES data to derive the 3-D properties of heavy ions. Assuming that the ion velocity distribution can be described with the kappa distribution, we first perform the forward model fit of the TOF data to calculate the relative abundance of heavy ion species. Then we fix the relative abundance and perform the second forward model fit on the SPECIES data. Here, using this method, we obtain the densities of different heavy ions, the shared temperature and kappa value, and the 3-D flow velocity vector. Some data examples of the equatorial plasma disk before Perijove 24 are included to demonstrate the method. Plasma properties can then be mapped to explore spatial and temporal variabilities in Jupiter’s magnetosphere.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains

Abstract In this study we construct and compare three different deep learning (DL) models for estimating daily snow water equivalent (SWE) from high‐resolution gridded meteorological fields over the Rocky Mountain region. To train the DL models, Snow Telemetry (SNOTEL) station‐based SWE observations are used as the prediction target. All DL models produce higher median Nash‐Sutcliffe Efficiency (NSE) values than a conceptual SWE model and interpolated gridded data sets, although mean squared errors also tend to be higher. Sensitivity of the SWE prediction to the model's input variables is analyzed using an explainable artificial intelligence (XAI) method, yielding insight into the physical relationships learned by the models. This method reveals the dominant role precipitation and temperature play in snowpack dynamics. In applying our models to estimate SWE throughout the Rocky Mountains, an extrapolation problem arises since the statistical properties of SWE (e.g., annual maximum) and geographical properties of individual grid points (e.g., elevation) differ from the training data. This problem is solved by normalizing the SWE with its historical maximum value to alleviate extrapolation for all tested DL models. Our work shows that the DL models are promising tools for estimating SWE, and sufficiently capture relevant physical relationships to make them useful for spatial and temporal extrapolation of SWE values.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Datasets for characterizing extreme events relevant to hydrologic design over the conterminous United States

Despite the close linkage between extreme floods and snowmelt, particularly through rain-on-snow (ROS), hydrologic infrastructure is mostly designed based on standard precipitation Intensity-Duration-Frequency curves (PREC-IDF) that neglect snow processes in runoff generation. For snow-dominated regions, such simplification could result in substantial errors in estimating extreme events and infrastructure design risk. To address this long-standing problem, we applied the Next Generation IDF (NG-IDF) technique to estimate design basis extreme events for different durations and return periods in the conterminous United States (CONUS) to distinctly represent the contribution of rain, snowmelt, and ROS events to the amount of water reaching the land surface. A suite of datasets were developed to characterize the magnitude, trend, seasonality, and dominant mechanism of extreme events for over 200,000 locations. Infrastructure design risk associated with the use of PREC-IDF was estimated. Accuracy of the model simulations used in the analyses was confirmed by long-term snow data at over 200 Snowpack Telemetry stations. The presented spatially continuous datasets are readily usable and instrumental for supporting site-specific infrastructure design.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Three-dimensional migration behavior of juvenile salmonids in reservoirs and near dams

To acquire 3-D tracking data on juvenile salmonids, Juvenile Salmon Acoustic Telemetry System (JSATS) cabled hydrophone arrays were deployed in the forebays of two dams on the Snake River and at a mid-reach reservoir between the dams. The depth distributions of fish were estimated by statistical analyses performed on large 3-D tracking data sets from ~33,500 individual acoustic tagged yearling and subyearling Chinook salmon and juvenile steelhead at the two dams in 2012 and subyearling Chinook salmon at the two dams and the mid-reach reservoir in 2013. This research investigated the correlation between vertical migration behavior and passage routes. The depth distributions of fish within the forebays of the dams were significantly different from fish passing the mid-reach reservoir. Fish residing deeper in the forebay tended to pass the dam using deeper powerhouse routes. This difference in depth distributions indicated that the depth distribution of fish at the mid-reach reservoir was not related to behaviors of fish passing through certain routes of the adjacent dams. For fish that were detected deeper than 17.5 m in the forebays, the probability of powerhouse passage (i.e., turbine) increased significantly. Another important finding was the variation in depth distributions during dam passage associated with the diel period, especially the crepuscular periods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Feasibility of Tracking Fish with Acoustic Transmitters in the Ice Harbor Dam Tailrace

The Juvenile Salmon Acoustic Telemetry System (JSATS) has been used at many dams but has never been deployed in the near-dam tailrace environment. The use of JSATS in the tailrace is of interest to fishery managers to evaluate downstream passage behavior of juvenile salmonids and dam approach behavior of upstream migrating adult salmon and lamprey. The acoustic noise level and detection range of JSATS were studied to determine the feasibility of deploying JSATS in the Ice Harbor Dam tailrace. The noise level measured from the powerhouse deck was less than 104 dB re 1 μPa except for the turbine outlet near the spillway and 350 m downstream of the dam, the noise level was less than 106 dB. The measured noise levels would allow a theoretical detection range of 100 m to 350 m and 85 m to 320 m, respectively. Validation experiments showed that the detection range is 113 to 184 m using hydrophones deployed from the powerhouse deck and 148 m using hydrophones deployed 500 m downstream of the dam.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

A 3D approximate maximum likelihood solver for localization of fish implanted with acoustic transmitters

Better understanding of fish behavior is vital for recovery of many endangered species including salmon. The Juvenile Salmon Acoustic Telemetry System (JSATS) was developed to observe the out-migratory behavior of juvenile salmonids tagged by surgical implantation of acoustic micro-transmitters and to estimate the survival when passing through dams on the Snake and Columbia Rivers. A robust three-dimensional solver was needed to accurately and efficiently estimate the time sequence of locations of fish tagged with JSATS acoustic transmitters, to describe in sufficient detail the information needed to assess the function of dam-passage design alternatives. An approximate maximum likelihood solver was developed using measurements of time difference of arrival from all hydrophones in receiving arrays on which a transmission was detected. Field experiments demonstrated that the developed solver performed significantly better in tracking efficiency and accuracy than other solvers described in the literature.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

An injectable acoustic transmitter for juvenile salmon

Salmon recovery, and the potential detrimental effects of dams on fish, has been attracting national attention in due to great environmental and economic implications. Acoustic Telemetry has been the primary method for studying salmon passage. However, the size of the existing transmitters limits the minimum size of fish that can be studied, introducing bias to the study results. We developed the first acoustic fish transmitter that can be implanted by injection instead of surgery. The new injectable transmitter offers improved performance and 30% weight reduction. Because the new transmitter costs significantly less to use, substantially reduces adverse effects of implantation, and provides additional biological benefits for tagged fish, it will become the enabling technology for studying migration behavior and survival of species and sizes of fish that have never been studied before. This will lead to critical information for salmon recovery and the development of fish-friendly hydroelectric systems.

42 ENGINEERING↗
DOE OSTI2025

Analysing a multi‐stage cyber threat and its impact on the power system

Abstract Electric power systems are composed of physical and cyber sub‐systems. The sub‐systems depend on each other. If the cyber sub‐system is compromised by a cyber threat, what is the impact on the physical system? This paper presents a case study that shows the steps of a multi‐stage cyber threat involving a database injection attack, and what happens to the power system if this threat is not detected in its early stages. The threat first affects one utility but it can spread to the balancing authority, which is responsible for keeping the voltage and frequency stable in the power grid. During the cyber threat, the authors also show defence tools, such as a cyber‐physical data fusion tool that displays and analyses power and cyber telemetry.

Al Homoud, Leen [Texas A&amp,M University College ↗
DOE OSTI2014

Planck 2013 results. II. Low Frequency Instrument data processing

We describe the data processing pipeline of the Planck Low Frequency Instrument (LFI) data processing centre (DPC) to create and characterize full-sky maps based on the first 15.5 months of operations at 30, 44, and 70 GHz. In particular, we discuss the various steps involved in reducing the data, from telemetry packets through to the production of cleaned, calibrated timelines and calibrated frequency maps. Data are continuously calibrated using the modulation induced on the mean temperature of the cosmic microwave background radiation by the proper motion of the spacecraft. Sky signals other than the dipole are removed by an iterative procedure based on simultaneous fitting of calibration parameters and sky maps. Noise properties are estimated from time-ordered data after the sky signal has been removed, using a generalized least squares map-making algorithm. A destriping code (Madam) is employed to combine radiometric data and pointing information into sky maps, minimizing the variance of correlated noise. Noise covariance matrices, required to compute statistical uncertainties on LFI and Planck products, are also produced. Main beams are estimated down to the ≈-20 dB level using Jupiter transits, which are also used for the geometrical calibration of the focal plane.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Next-Generation Intensity–Duration–Frequency Curves to Reduce Errors in Peak Flood Design

Precipitation-based intensity–duration–frequency (PREC-IDF) curves are a standard tool used to derive design floods for hydraulic infrastructure worldwide. In snow-dominated regions where a large percentage of flood events are caused by snowmelt and rain-on-snow events, the PREC-IDF design approach can lead to substantial underestimation or overestimation of design floods and associated infrastructure. In this study, next-generation IDF (NG-IDF) curves, which characterize the actual water reaching the land surface, are introduced into the design process to improve hydrologic design. The authors compared peak design flood estimates from the National Resource Conservation Service TR-55 hydrologic model driven by NG-IDF and PREC-IDF curves at 399 Snowpack Telemetry (SNOTEL) stations across the western United States, all of which had at least 30 years of high-quality records. They found that about 70% of the stations in the western United States showed the potential for underdesign, for which the PREC-IDF curves underestimated peak design floods by as much as 324%. These results demonstrated the need to update the use of PREC-IDF curves to the use of NG-IDF curves for hydrologic design in snow-dominated regions.

Yan, Hongxiang↗