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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 OSTI2025

Temporal Study 2022-2024: Sample-Based Surface Water Dissolved Inorganic Carbon, Dissolved Organic Carbon, Total Nitrogen, Stable Isotopes, and Total Suspended Solids from across Multiple Watersheds in the Yakima River Basin, Washington, USA

This dataset supports a broader study examining the drivers of temporal variability in sediment respiration rates in the Yakima River Basin. The dataset provides geochemistry data generated from samples collected at bi-weekly or monthly intervals at six sites across the Yakima River Basin in Washington, USA. Sample and sensor data from previous years (2021-2022) can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1898912 and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1892054, respectively. Related sensor data from 2022-2024 will be published separately. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) field metadata; (5) dissolved inorganic carbon (DIC) and averages; (6) dissolved organic carbon (DOC; reported as non-purgeable organic carbon; NPOC) and averages; (7) total dissolved nitrogen (TN) and averages; (8) total suspended solids (TSS); (9) stable isotopes; (10) surface water sampling protocol; (11) sensor protocol; (12) methods codes; and (13) international generic sample number (IGSN) mapping file. All files are .csv or .pdf. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. For data and scripts associated with "Shifts in rain-snow partitioning drive faster water transit times in the US Pacific Northwest" (Butler et al., 2026), go to https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3025481

18-O↗
NASA NTRS1987

The development of test methodology for testing glassy materials

The inherent brittleness of glass invariably leads to a large variability in strength data and a time dependence in strength (i.e., static fatigue). Loading rate plays a large role in strength values. Glass is found to be weaker when supporting loads over long periods as compared to glass which undergoes rapid loading. In this instance the purpose of rapid loading is to fail the glass before any significant crack growth occurs. However, a decrease in strength occurs with a decrease in loading rate, pursuant to substantial crack extension. These properties complicate the structural design allowable for the utilization of glass components in applications such as mirrors for the Space Telescope and AXAF for Spacelab and the space station.

Tucker, Dennis S.↗
NASA NTRS1990

Thermodynamic structure of the stratocumulus-capped boundary layer on 7 July, 1987

As part of project First ISCCP Regional Experiment (FIRE), a mission was carried out on 7 July 1987 to study the thermodynamic structure of a boundary layer which is in transition from a clear to a cloudy state. The National Center for Aeronautical Research (NCAR) Electra flew a pattern in tight coordination with the NASA ER-2 aircraft near 122 West, 31.6 North off the coast of California. A description is given here of the thermodynamic structure. The purpose is to derive the entrainment rate and the fluxes of the thermodynamic variables. To this end researchers represent the data in conserved variable diagrams.

Boers, Reinout↗
NASA NTRS2021

Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations

The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere-ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal (<90 day) MLD variability. Yet, many multivariate observations-based analyses include implicit modeled subseasonal MLD variability. One analysis method is optimal interpolation of in situ data, but the interior analysis can be improved by leveraging surface data with regression or variational approaches. Here, we demonstrate how machine learning methods and satellite sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid-latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2° gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We validate our methodology by applying it to ocean model simulations. We find that incorporating sea surface data through a machine learning model improves the performance of spatiotemporal MLD variability estimation compared to optimal interpolation of Argo observations alone. These preliminary results are a promising first step for the application of machine learning to MLD prediction.

Machine Learning↗
NASA NTRS1989

Variability of X-ray emission from OB stars

The variability in soft X-ray emission of 12 OB stars is studied. Two different methods of analysis, one more suitable for detecting short-term variations, the other aimed at detecting long time-scale variations, are applied to all stars in the sample. The long-term variability analysis shows that Cyg-OB2 8A Zeta Pup and Delta Ori exhibit significant count rate variations between different data sections. Similar variations are marginally detected in 15 Mon; the count rate variations for the other eight stars are consistent with statistical fluctuations. The light curve of Cyg-OB2 8A suggests the existence of two different emission levels. The short-term variability analysis detects marginal variability in Tau Sco with an effective amplitude of about 30 percent and a time scale of about 50 s. The upper limits to the effective short-term variability amplitude for all other sample stars are in the 10-30 percent range.

Collura, A.↗
NASA NTRS2019

Saturn Atmospheric Dynamics One Year After Cassini: Long-Lived Features and Time Variations in the Drift of the Hexagon

We examine Saturn's atmospheric dynamics with observations in the visible range from ground-based telescopes and Hubble Space Telescope (HST). We present a detailed analysis of observations acquired during 2018 obtaining drift rates of major meteorological systems from the equator to the North polar hexagon. A system of polar storms that appeared in the planet in March 2018 and remained active with a complex phenomenology at least until September is analyzed elsewhere [Sanchez-Lavega et al., 2019]. Many of the regular cloud features visible in 2018 are long-lived and can be identified in Saturn images in 2017, and in some cases, for up to a decade using also Cassini ISS images. Without considering the polar storms, the most interesting long-lived cloud systems are: i) A bright white spot in the Equatorial Zone that can be tracked continuously since 2014 with minimal changes in its zonal velocity, which was 444:3 +or- 3:1 ms(exp -1) in 2014 and 452:4 + or - 1:7 ms(exp -1) in 2018. This velocity is remarkably different from the zonal winds at the cloud level at its latitude during the Cassini mission, and is closer to zonal winds obtained at the time of the Voyagers flybys and to zonal winds from Cassini VIMS infrared images of the lower atmosphere. ii) A large long-lived Anticyclone Vortex, here AV, that formed after the Great White Spot of 2010-2011. This vortex has changed significantly in visual contrast, drift rate and latitude with minor changes in size over the last years. iii) A system of subpolar vortices at latitudes 60-65N present at least since 2011. These vortices and additional atmospheric features here studied follow drift rates consistent with zonal winds obtained by Cassini. We also present a study of the positions of the vertices of Saturn's North polar hexagon from 2015 to 2018. These measurements are compared with previous analyses during the Cassini mission (2007-2014), observations with HST in the 90s, and data from the Voyagers in 1980-1981 to explore the long term variability of the hexagon's drift rate. We find variations in the drift rate of the hexagon through these epochs that can not be fit by seasonal changes in the polar area. Instead, the different drift rates reinforce the role of the North Polar Spot that was present in the Voyager epoch and in the early 90s to cause a faster drift rate of the hexagon at that time compared with the current slower one.

R Hueso↗
DOE OSTI2026

Spectra-to-exposure conversion using polynomial response models for gamma-ray field characterization

Accurate measurement of exposure rate from gamma-ray spectral data remains a critical challenge during radiological emergency response operations. Conventional methods rely on pre-defined static conversion factors derived from fixed geometries and isotopic compositions, which often fail to capture real-world environmental variability. This study presents a generalized approach as a "next-step" for converting gamma-ray spectral data into exposure rate using polynomial response models. The method introduces a flexible weighting scheme based on the in-situ detector response to distributed sources, enabling a pathway towards improved correspondence between measured spectra and "ground-truth" exposure rates. Experimental data from sodium iodide NaI(Tl) detectors were used to validate the approach as, at least equivalent to the current count-to-exposure method employed in emergency response CONOPS. Results show that the polynomial weighting model is sufficiently equal to the count-to-exposure method and may help improve accuracy given its adaptability to real-world conditions.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS2000

Non-Tidal Non-Seasonal Oceanic Mass Redistribution Estimated from the TOPEX/Poseidon Observation

Topex/Poseidon altimetry data are used to estimate the non-tidal mass redistribution as a function of space-time. The goal is to study the contribution of ocean circulations in the geodynamic effects including Earth's rotational and gravitational variations. We examine the non-seasonal anomalies at monthly sampling rate over the T/P span of eight years, concentrating especially on interannual variabilities. Since the sea-surface height data obtained from altimetry is the combined effect of steric change (primarily thermal effect) and the mass flux, and because the former has zero contribution to the geodynamic effects, one needs to do a so-called steric correction by removing from the altimetry data the steric contributions. We achieve it using multiyear monthly sea-surface temperature maps, together with monthly "climatology" for mixed-layer depth maps for mean-months of the year. We analyze both sets of the altimetry data and the steric correction data using the empirical orthogonal function/principal component analysis (in which we take care of issues associated with the area-weighting and non-zero mean), and examine the most important modes, either globally or regionally. In particular, the ENSO in the tropical Pacific/Indian Oceans exhibits the most prominent pattern. The net mass transport after the steric correction can then be compared with: (1) ocean general circulation model outputs for the same period of time (such as from POCM-4B); (2) non-atmospheric Earth rotation variations obtained from space geodesy data and atmospheric angular momentum data; (3) non-atmospheric low-degree gravitational variations from satellite-laser-ranging observations and global atmospheric data.

Chao, Benjamin F.↗
NASA NTRS2004

Spray Characteristics of a Hybrid Twin-Fluid Pressure-Swirl Atomizer

The spray performance of a fuel injection system applicable for use in main combustion chamber of an oxidizer-rich staged combustion (ORSC) cycles is presented. The experimental data reported here include mean drop size and drop size distribution, spray cone half-angle, and momentum rate (directly related to spray penetration). The maximum entropy formalism, MEF, method to predict drop size distribution is applied and compared to the experimental data. Geometric variables considered include the radius of the injector inlet orifice plate through which oxidizer flows (&) and the exposed length from the fuel inlet to the injector exit plane (L2). Operating conditions that were varied include the liquid mass flow rate and air mass flow rate. For orifices B and C there is a significant dependence of D3Z on both the air and liquid mass flow rates, as well as on L2. For the A orifice, the momentum rate of the air flow appears to exceed a threshold value above which a constant D32 is obtained. Using the MEF method, a semi-analytical process was developed to model the spray distribution using two input parameters (q = 0.4 and Dso). The momentum rate of the spray is directly related to the air and liquid mass flow rates. The cone half angle of the spray ranges from 25 to 17 degrees. The data resulting from this project will eventually be used to develop advanced rocket systems.

Durham, M. J.↗
NASA NTRS1976

Control of thermal balance by a liquid circulating garment based on a mathematical representation of the human thermoregulatory system

Test data and a mathematical model of the human thermoregulatory system were used to investigate control of thermal balance by means of a liquid circulating garment (LCG). The test data were derived from five series of experiments in which environmental and metabolic conditions were varied parametrically as a function of several independent variables, including LCG flowrate, LCG inlet temperature, net environmental heat exchange, surrounding gas ventilation rate, ambient pressure, metabolic rate, and subjective/obligatory cooling control. The resultant data were used to relate skin temperature to LCG water temperature and flowrate, to assess a thermal comfort band, to demonstrate the relationship between metabolic rate and LCG heat dissipation, and so forth. The usefulness of the mathematical model as a tool for data interpretation and for generation of trends and relationships among the various physiological parameters was also investigated and verified.

Kuznetz, L. H.↗
DOE OSTI2025

eDNAjoint: An R package for interpreting paired or semi‐paired environmental DNA and traditional survey data in a Bayesian framework

Abstract Environmental DNA (eDNA) sampling is increasingly used in surveys of species distribution as a potentially sensitive and efficient monitoring method. Yet access to modelling tools designed specifically for interpreting this new data type lags behind its ubiquity. While occupancy modelling software has dominated the analytical landscape for eDNA data analysis of single species, this type of model may not always be the most appropriate. The rate of eDNA detection often corresponds to species density, rather than just occupancy, and researchers often have access to observations from non‐genetic sampling methods at the same sites. To provide users access to a modelling framework designed to maximize the use of all available data, we developed an R package, eDNAjoint . The package provides an easy‐to‐use interface for fitting a ‘joint’ model that integrates data from paired or semi‐paired eDNA and traditional surveys in a Bayesian framework. The model can be used to estimate parameters like the probability of a false positive eDNA detection and mean catch rate at a site, and the package allows access to multiple model variations and Bayesian prior customization. Additional functionality can be used for model selection, summarising posteriors and comparing the relative sensitivities of the two survey methods. We demonstrate the use of eDNAjoint by fitting a variation of the model with site‐level covariates that scale the sensitivity of eDNA sampling relative to traditional sampling. The example workflow uses binary eDNA and seine count data for the endangered tidewater goby ( Eucyclogobius newberryi ) from a study by Schmelzle and Kinziger (2016). This use case includes a prior sensitivity analysis and an evaluation of the relationship between detection rates and environmental variables. eDNAjoint has the potential to greatly increase the range of users who will be able to rigorously analyse eDNA and traditional survey data in a Bayesian framework, understand if and how eDNA can improve monitoring practices, and gain confidence in the interpretability of eDNA data.

Keller, Abigail G. [Department of Environment Scie↗
NASA NTRS1993

Investigation of spectral analysis techniques for randomly sampled velocimetry data

It is well known that velocimetry (LV) generates individual realization velocity data that are randomly or unevenly sampled in time. Spectral analysis of such data to obtain the turbulence spectra, and hence turbulence scales information, requires special techniques. The 'slotting' technique of Mayo et al, also described by Roberts and Ajmani, and the 'Direct Transform' method of Gaster and Roberts are well known in the LV community. The slotting technique is faster than the direct transform method in computation. There are practical limitations, however, as to how a high frequency and accurate estimate can be made for a given mean sampling rate. These high frequency estimates are important in obtaining the microscale information of turbulence structure. It was found from previous studies that reliable spectral estimates can be made up to about the mean sampling frequency (mean data rate) or less. If the data were evenly samples, the frequency range would be half the sampling frequency (i.e. up to Nyquist frequency); otherwise, aliasing problem would occur. The mean data rate and the sample size (total number of points) basically limit the frequency range. Also, there are large variabilities or errors associated with the high frequency estimates from randomly sampled signals. Roberts and Ajmani proposed certain pre-filtering techniques to reduce these variabilities, but at the cost of low frequency estimates. The prefiltering acts as a high-pass filter. Further, Shapiro and Silverman showed theoretically that, for Poisson sampled signals, it is possible to obtain alias-free spectral estimates far beyond the mean sampling frequency. But the question is, how far? During his tenure under 1993 NASA-ASEE Summer Faculty Fellowship Program, the author investigated from his studies on the spectral analysis techniques for randomly sampled signals that the spectral estimates can be enhanced or improved up to about 4-5 times the mean sampling frequency by using a suitable prefiltering technique. But, this increased bandwidth comes at the cost of the lower frequency estimates. The studies further showed that large data sets of the order of 100,000 points, or more, high data rates, and Poisson sampling are very crucial for obtaining reliable spectral estimates from randomly sampled data, such as LV data. Some of the results of the current study are presented.

Sree, Dave↗
NASA NTRS1985

Flight tests and preliminary aerodynamic parameter extraction of an externally piloted vehicle aircraft model

Model aircraft flight test investigations have been undertaken to develop a low cost flight data acquisition system and to evaluate aerodynamic parameter estimation techniques. The externally piloted vehicles (EPVs) used are instrumented with sensors measuring acceleration, angular rates, angles of attack and sideslip, flight path velocity, and control deflections. Measured state variables are downlink telemetered to a ground-based real time flight data acquisition system; the data thus recorded are used by flight test engineers and EPV pilots to monitor conditions and aid in piloting tasks. Attention is given to comparisons of F-16 EPV data with wind tunnel data.

White, E. R.↗
DOE OSTI2026

Data-based filtered dissipation rate modelling for multi-modal turbulent combustion: evaluating a priori model generalizability

Manifold-based models offer a computationally efficient alternative to directly transporting the thermochemical state in computational simulations of turbulent reacting flows, projecting the high-dimensional thermochemical state-space onto a low-dimensional manifold. Recent efforts have yielded a manifold-based model applicable to multi-modal combustion, enabling reconstruction of the thermochemical state from solutions to two-dimensional manifold equations in mixture fraction and generalized progress variable that are parameterised by three scalar dissipation rates. In coarse-grained simulations such as Large Eddy Simulation (LES), closure of the multi-modal manifold equations and subfilter variances/covariance requires closure of three filtered scalar dissipation rates. Here, the present work adopts a data-based approach, providing closure for the three filtered scalar dissipation rates via deep neural networks (DNNs). High-fidelity datasets corresponding to an autoigniting n-dodecane jet flame and a bluff body swirl-stabilized confined lifted spray flame of two aviation fuels (Jet-A and C1) with different ignition propensities are leveraged to generate training data that spans a diverse range of thermodynamic conditions and combustion modes, including low- and high-temperature ignition regimes in addition to premixed and nonpremixed behaviour. A final DNN model is trained to enforce inherent physical constraints by learning nonlinear functional transformations of the three filtered scalar dissipation rates. The generalizability of this constrained DNN model is demonstrated a priori via conditional statistics evaluated on the lifted spray flame with C1–a configuration that had not been included in the training data. Excellent DNN agreement with conditional DNS statistics is observed, and integrated gradients are computed to identify the most sensitive input variables. The similarity of the marginal PDFs of the most informative input variables and outputs across configurations are quantified via the Wasserstein metric, demonstrating that data-based models may successfully generalize to unseen parametric conditions so long as the most informative input variables share similar distributions across training and testing datasets.

Data-based modelling↗
DOE OSTI2025

Quality-Controlled Meteorological Data from the Flood Control District of Maricopa County (FCDMC) Network, Phoenix, Arizona (1987-2024)

This dataset contains 15- or 30-minute interval meteorological data from the Flood Control District of Maricopa County (FCDMC), Arizona, USA, covering eight key variables across multiple sensor stations between 1987 and 2024. Each variable is stored as a separate CSV file, containing time-series data that have undergone rigorous quality control (QC) procedures and, where appropriate, short-gap interpolation for consistency. The quality control (QC) pipeline consisted of four sequential tests: (1) a range test to ensure all values fall within physically realistic limits, (2) a step test to identify abrupt and implausible changes between consecutive records, (3) a proximity test that validates flagged values from step test using data from nearby stations and exceedance probability thresholds, and (4) a persistence test to detect and remove periods of unrealistically constant readings. These thresholds were calibrated to Arizona’s environmental conditions and sensor specifications. After QC, short gaps (≤2 hours) were linearly interpolated to ensure consistent temporal resolution, except for wind variables. Due to a major upgrade in FCDMC’s data transmission system, only ALERT-2 protocol data (2016–2024) for wind variables are included; earlier ALERT-1 data were excluded because of irregular sampling and high missing rates. This dataset supports regional climate and infrastructure resilience studies by providing standardized, high-resolution meteorological data for the greater Phoenix metropolitan area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Respiration Signal Pattern Analysis for Doppler Radar Sensor with Passive Node and Its Application in Occupancy Sensing of a Stationary Subject

Doppler radar node occupancy sensors are promising for applications in smart buildings due to their simple circuits and price advantage compared to quadrature radar sensors. However, single-channel sensitivity limitations may result in low sensitivity and misinterpreted motion rates if the detected subject is at or close to “null” points. We designed and tested a novel method to eliminate such limits, demonstrating that passive nodes can be used to detect a sedentary person regardless of position. This method is based on characteristics of chest motion due to respiration, found via both simulations and experiments based on a sinusoidal model and a more realistic model of cardiorespiratory motion. In addition, respiratory rate variability is considered to distinguish a true human presence from a mechanical target. Sensor node data were collected simultaneously with an infrared camera system, which provided a respiration signal reference, to test the algorithm with 19 human subjects and a mechanical target. The results indicate that a human presence was detected with 100% accuracy and successfully differentiated from a mechanical target in a controlled environment. The developed method can greatly improve the occupancy detection accuracy of single-channel radar-based occupancy sensors and facilitate their adoption in smart building applications.

Song, Chenyan↗
NASA NTRS1976

The parameters of a VCM test for the selection of space materials

Materials for space applications must have low outgassing rates at normal operating temperatures and the outgassing products should include a minimum of condensables at the temperatures of nearby surfaces. A screening method, developed years ago, consists of holding a material sample at 398 K (125 C) for 24 hours and measuring its percentage total mass loss (TML) and the percentage volatile condensable mass (VCM) accreted on a 298 K (25 C) collector. In general, the material is acceptable if the TML is less than 1 percent and the VCM is less than 0.1 percent. This paper presents an analysis of the test and its results. It shows that the results of TML and VCM can be used to provide data on outgassing rates and to derive an 'equivalent activation energy' for the material. The energy variables are needed for the calculation of pressure in compartments, the molecular contamination of critical surfaces, and other problems in molecular flow and surface physics. The analysis points out the criticality of the test and its limits with regards to the detection of condensables from certain materials.

Scialdone, J. J.↗
NASA NTRS1988

Linear response theory for annealing of radiation damage in semiconductor devices

A theoretical study of the radiation/annealing response of MOS ICs is described. Although many experiments have been performed in this field, no comprehensive theory dealing with radiation/annealing response has been proposed. Many attempts have been made to apply linear response theory, but no theoretical foundation has been presented. The linear response theory outlined here is capable of describing a broad area of radiation/annealing response phenomena in MOS ICs, in particular, both simultaneous irradiation and annealing, as well as short- and long-term annealing, including the case when annealing is nearing completion. For the first time, a simple procedure is devised to determine the response function from experimental radiation/annealing data. In addition, this procedure enables us to study the effect of variable temperature and dose rate, effects which are of interest in spaceflight. In the past, the shift in threshold potential due to radiation/annealing has usually been assumed to depend on one variable: the time lapse between an impulse dose and the time of observation. While such a suggestion of uniformity in time is certainly true for a broad range of radiation annealing phenomena, it may not hold for some ranges of the variables of interest (temperature, dose rate, etc.). A response function is projected which is dependent on two variables: the time of observation and the time of the impulse dose. This dependence on two variables allows us to extend the theory to the treatment of a variable dose rate. Finally, the linear theory is generalized to the case in which the response is nonlinear with impulse dose, but is proportional to some impulse function of dose. A method to determine both the impulse and response functions is presented.

Litovchenko, Vitaly↗