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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

CLUES TO THE STAR FORMATION IN NGC 346 ACROSS TIME AND SPACE

We have studied the properties of the stellar populations in the field of the NGC 346 cluster in the Small Magellanic Cloud, using the results of a novel self-consistent method that provides a reliable identification of pre-main sequence (PMS) objects actively undergoing mass accretion, regardless of their age. The 680 identified bona fide PMS stars show a bimodal age distribution, with two roughly equally numerous populations peaked, respectively, at {approx}1 Myr and {approx}20 Myr. We use the age and other physical properties of these PMS stars to study how star formation has proceeded across time and space in NGC 346. We find no correlation between the locations of young and old PMS stars, nor do we find a correspondence between the positions of young PMS stars and those of massive OB stars of similar age. Furthermore, the mass distribution of stars with similar age shows large variations throughout the region. We conclude that, while on a global scale it makes sense to talk about an initial mass function, this concept is not meaningful for individual star-forming regions. An interesting implication of the separation between regions where massive stars and low-mass objects appear to form is that high-mass stars might not be 'perfect' indicators of star formation and hence a large number of low-mass stars formed elsewhere might have so far remained unnoticed. For certain low surface density galaxies this way of preferential low-mass star formation may be the predominant mechanism, with the consequence that their total mass as derived from the luminosity may be severely underestimated and that their evolution is not correctly understood.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗
DOE OSTI2019

Ultrasonic Measurements of Temperature Distribution and Heat Fluxes Across Containments of Extreme Environments

Extreme conditions are common in energy conversion, material processing, nuclear, airspace, and military applications. In such environments, the most hardened insertion sensors do not perform reliably for long. Ultrasonic measurements, on the other hand, can be acquired noninvasively, with sensitive components kept away from the damaging environment. We have previously developed the ultrasonic method for measuring the spatial distribution of temperatures in solid materials applicable in the cases when large thermal gradients are present. In the developed approach, we use the echogenically segmented ultrasound propagation path, structured to contain engineered or naturally occurring echogenic features, to produce a train of echoes in response to an external pulse of ultrasonic excitation. The delays between the echoes, which encode the information on the temperature distribution in the corresponding segments, is used to reconstruct unknown temperature profile. In this paper, we outline the results of pilot-scale testing of the developed approach and demonstrate its application to the measurements of the temperature distribution across the containment of a high-temperature combustion process. The validation results show that the estimated temperature profile is correctly captured, and the measurement accuracy can be comparable with traditional insertion sensors, such as thermocouples. Overall, the testing has confirmed that the developed approach has matured to become an attractive alternative to conventional sensing in solving challenging problems of long-term temperature measurements in extreme environments. Heat fluxes and thermal stresses in the structure can then be characterized noninvasively using the measured temperature distribution as the basis.

Jia, Yunlu↗
DOE OSTI2024

Bulk cloud microphysical properties as seen from numerical simulation and remote sensing products: case study of a hailstorm event over the La Plata Basin

Hailstorms develop over the La Plata Basin, in south-eastern South America, more often during later winter and early austral spring, between September and October. These systems have significant socioeconomic impacts over the region. Thus, a better understanding of how atmospheric drivers modulate the formation of hailstorms is important to improve the forecast of such phenomena. In this study, we selected a hailstorm event observed over the eastern La Plata Basin during 14–15 July 2016 to evaluate the performance of the Brazilian developments on the Regional Atmospheric Modelling System (BRAMS) model. The ability of the model in simulating cloud microphysical properties was evaluated by comparing simulations driven by different global forcings against in situ and remote sensing observations. The model results showed good skill in capturing the basic characteristics of the thunderstorm, particularly in terms of the spatial distribution of hydrometeors. The simulated spatial distribution of hail covers locations where hail fall was reported. The BRAMS simulations suggest that, despite relatively low values of the convective available potential energy (CAPE) (700–1000 J kg -1 ), environments with strong 0–8-km bulk shear (60–70 kt, ~30.9–36.0 m s –1 ) can promote the formation of ice clouds and hail fall over the eastern La Plata Basin. To be more conclusive, however, further research is needed to understand how different combinations of CAPE and shear affect hail formation over the region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

COSMOGRAPHY OF THE LOCAL UNIVERSE

The large-scale structure of the universe is a complex web of clusters, filaments, and voids. Its properties are informed by galaxy redshift surveys and measurements of peculiar velocities. Wiener filter reconstructions recover three-dimensional velocity and total density fields. The richness of the elements of our neighborhood is revealed with sophisticated visualization tools. A key component of this paper is an accompanying movie which can be viewed and downloaded at http://vimeo.com/pomarede/cosmography. The ability to translate and zoom helps the viewer follow structures in three dimensions and grasp the relationships between features on different scales while retaining a sense of orientation. The ability to dissolve between scenes provides a technique for comparing different information such as the observed distribution of galaxies, smoothed representations of the distribution accounting for selection effects, observed peculiar velocities, smoothed and modeled representations of those velocities, and inferred underlying density fields. The agreement between the large-scale structure seen in redshift surveys and that inferred from reconstructions based on the radial peculiar velocities of galaxies strongly supports the standard model of cosmology where structure forms from gravitational instabilities and galaxies form at the bottom of potential wells.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Distributed Sensors for Waste Plastics Gasification and Clean Hydrogen Productions

This project developed distributed fiber sensors for plastics gasification reactor for clean hydrogen production. These sensors can be directly inserted into gasification reactors to perform real-time, in-situ hydrogen and temperature profile measurements with 5-cm spatial resolution across the entire reactor chamber. Our research team will use this new sensor capability to study how feedstock mixtures, feedstock preparations, and air/steam flows impact feedstock consumption, hydrogen production, and harmful chemical production emissions.

08 HYDROGEN↗
DOE OSTI2023

GPS Spoofing Mitigation and Timing Risk Analysis in Networked Phasor Measurement Units via Stochastic Reachability

To address phasor measurement unit (PMU) vulnerability to spoofing, we propose the use of a set-valued state estimation technique known as stochastic reachability (SR)-based distributed Kalman filter (DKF) that computes secure global positioning system (GPS) timing across a network of receivers. Utilizing SR, we estimate not only GPS time but also its stochastic reachable set, which is parameterized by probabilistic zonotope (p-Zonotope). While requiring known measurement error bounds in only non-spoofed conditions, we designed a two-tiered approach. We first performed measurement-level spoofing mitigation via deviation of a measurement innovation from its expected p-Zonotope. We then performed state-level timing risk analysis via a determination of the intersection probability of the estimated p-Zonotope with an unsafe set that violates IEEE C37.118.1a-2014 standards. Finally, we validated our SR-DKF algorithm by subjecting it to a simulated receiver network to coordinate signal-level spoofing. We demonstrate improved timing accuracy and successful spoofing mitigation via the use of our SR-DKF algorithm. We also validated the robustness of the estimated timing risk as the number of receivers were varied.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Quantitative measurements of reactive oxygen species partitioning in electron transfer flavoenzyme magnetic field sensing

Biological magnetic field sensing that gives rise to physiological responses is of considerable importance in quantum biology. The radical pair mechanism (RPM) is a fundamental quantum process that can explain some of the observed biological magnetic effects. In magnetically sensitive radical pair (RP) reactions, coherent spin dynamics between singlet and triplet pairs are modulated by weak magnetic fields. The resulting singlet and triplet reaction products lead to distinct biological signaling channels and cellular outcomes. A prevalent RP in biology is between flavin semiquinone and superoxide (O 2 •–) in the biological activation of molecular oxygen. This RP can result in a partitioning of reactive oxygen species (ROS) products to form either O 2 •– or hydrogen peroxide (H 2 O 2 ). Here, we examine magnetic sensing of recombinant human electron transfer flavoenzyme (ETF) reoxidation by selectively measuring O 2 •– and H 2 O 2 product distributions. ROS partitioning was observed between two static magnetic fields at 20 nT and 50 μT, with a 13% decrease in H 2 O 2 singlet products and a 10% increase in O 2 •– triplet products relative to 50 µT. RPM product yields were calculated for a realistic flavin/superoxide RP across the range of static magnetic fields, in agreement with experimental results. For a triplet born RP, the RPM also predicts about three times more O 2 •– than H 2 O 2 , with experimental results exhibiting about four time more O 2 •– produced by ETF. The method presented here illustrates the potential of a novel magnetic flavoprotein biological sensor that is directly linked to mitochondria bioenergetics and can be used as a target to study cell physiology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Automatic Calibration and Health Monitoring of Infrastructure Sensors

Smart transportation infrastructure relies on networks of heterogeneous sensors - cameras, radars, and lidars - continuously monitoring traffic conditions. However, executing the initial spatial calibration of multiple sensors and the subsequent health monitoring presents significant operational challenges. Environmental factors, mechanical vibrations, and gradual drift cause spatial misalignment, degrading fusion performance and tracking accuracy. Traditional calibration approaches require manual intervention with specialized targets or survey equipment, resulting in service interruptions and high maintenance costs. This work presents an automated framework for initial calibration and continuous health monitoring without human intervention or service disruption. Our approach addresses two critical problems: (1) detecting when sensors become miscalibrated during operation, and (2) automatically re-establishing spatial alignment using only operational traffic data. The health monitoring component analyzes measurement innovations - differences between sensor observations and predicted object states - to detect systematic biases indicative of calibration drift. By computing bias magnitude, directional consistency, and rejection rates, the system identifies miscalibrations as small as 0.5 meters. Unlike traditional methods requiring known calibration targets, our diagnostic operates continuously on live traffic observations, enabling early detection before fusion quality degrades. The automatic recalibration algorithm leverages overlapping sensor fields-of-view and temporal correlation of vehicle observations. Using graph-based optimization, the system automatically discovers which sensor pairs observe common regions, estimates pairwise spatial transformations using RANSAC-based robust estimation, and jointly optimizes all sensor poses through bundle adjustment. The framework handles practical deployment challenges, including different sensor sampling rates (1-10 Hz), varying installation positions, unknown orientations, and limited overlap regions (>10%). When approximate sensor positions are available from installation surveys (+/-1m accuracy), the algorithm additionally estimates sensor orientations, refining both position and rotation to sub-meter and sub-degree accuracy. We validate the framework on multi-hour traffic datasets from six heterogeneous sensors with sampling rates ranging from 1 Hz to 10 Hz. Results demonstrate successful calibration even with sparse overlap (<20%) and automatic detection of miscalibrations exceeding 0.8 meters. This work enables a "deploy-and-forget" sensor infrastructure that maintains calibration autonomously, reducing maintenance costs while improving tracking accuracy. The techniques generalize beyond transportation to any multi-sensor monitoring application requiring robust spatial alignment, including smart cities, industrial monitoring, and surveillance systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

OpenET: Filling a Critical Data Gap in Water Management for the Western United States

The lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data-driven irrigation management strategies, and expanding incentive-driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field-scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community-driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well-established satellite-based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web-based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite-driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Manipulation of Scattering Spectra with Topology of Light and Matter

Structured lights, including beams carrying spin and orbital angular momenta, radially and azimuthally polarized vector beams, as well as spatiotemporal optical vortices, have attracted significant interest due to their unique amplitude, phase front, polarization, and temporal structures, enabling a variety of applications in optical and quantum communications, micromanipulation, and super-resolution imaging. In parallel, structured optical materials, metamaterials, and metasurfaces consisting of engineered unit cells—meta-atoms, opened new avenues for manipulating the flow of light and optical sensing. While several studies explored structured light effects on the individual meta-atoms, their shapes are largely limited to simple spherical geometries. However, the synergy of the structured light and complex-shaped meta-atoms has not been fully explored. Here, in this paper, the role of the helical wavefront of Laguerre–Gaussian beams in the excitation and suppression of higher-order resonant modes inside all-dielectric meta-atoms of various shapes, aspect ratios, and orientations, is demonstrated and the excitation of various multipolar moments that are not accessible via unstructured light illumination is predicted. The presented study elucidates the role of the complex phase distribution of the incident light in shape-dependent resonant scattering, which is of utmost importance in a wide spectrum of applications ranging from remote sensing to spectroscopy.

42 ENGINEERING↗
DOE OSTI2014

Complex statistics and diffusion in nonlinear disordered particle chains

We investigate dynamically and statistically diffusive motion in a Klein-Gordon particle chain in the presence of disorder. In particular, we examine a low energy (subdiffusive) and a higher energy (self-trapping) case and verify that subdiffusive spreading is always observed. We then carry out a statistical analysis of the motion, in both cases, in the sense of the Central Limit Theorem and present evidence of different chaos behaviors, for various groups of particles. Integrating the equations of motion for times as long as 10{sup 9}, our probability distribution functions always tend to Gaussians and show that the dynamics does not relax onto a quasi-periodic Kolmogorov-Arnold-Moser torus and that diffusion continues to spread chaotically for arbitrarily long times.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

Limitations in Determining Multifractal Spectra from Pore-Solid Soil Aggregate Images

Multifractal methods have the potential to be useful tools for characterizing spatial distributions of soil pores from microtomographic images of undisturbed soil cores and soil aggregates. The objective of this study was to examine the limitations of multifractal analyses in binary (void and solid) soil images and to explore conditions under which multifractal spectra can be obtained. Multifractal characteristics of binary soil images are bounded within certain limiting values corresponding to nonfractal scaling. In this study, we first addressed the theoretical limitations of multifractal analysis of binary images and examined the nonfractal scaling boundaries in multifractal calculations by the method of moments. Then we developed boundary conditions for multifractal calculations by the direct method. Results revealed that fractal scaling is potentially possible only across a relatively narrow range of cell sizes restricted by the nonfractal scaling boundaries. Moreover, the range of cell sizes where fractal scaling is potentially possible varies with pore size. That is, in multifractal calculations it changes continuously with changes in the q value. For the soil aggregates examined in this study, this range varied from two to eight pixels for low q values to 128 pixels for high q values. The varying range makes calculations of true multifractal spectra for binary soil image data impossible. These results are consistent with a general theoretical notion that binary soil images are not multifractal in a strict mathematical sense. We suggest, however, that application of multifractal formalism can generate 'pseudo-multifractal spectra' that might still be useful for summarizing pore distribution information and for comparing pore data among different agricultural management regimes and soil type.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Fast Dynamic Internal Predictive Power Scheduling Approach for Power Management in Microgrids: Preprint

This paper presents a Dynamic Internal Predictive Power Scheduling (DIPPS) approach for optimizing power management in microgrids, particularly focusing on external power exchanges among diverse prosumers. DIPPS utilizes a dynamic objective function with a time-varying binary parameter to control the timing of power transfers to the external grid, facilitated by efficient usage of energy storage for surplus renewable power. The microgrid power scheduling problem is modeled as a mixed-integer nonlinear programming (MINLP-PS) and subsequently transformed into a mixed-integer linear programming (MILPPS) optimization through McCormick's relaxation to reduce computational complexity. A predictive window window with 6 data points is solved at an average of 0.92s, a 97.6% improvement over the 38.27s required for the MINLP-PS formulation, implying the numerical feasibility of the DIPPS approach for real-time implementation. Finally, the approach is validated against a static objective using real-world load data across three case studies with different time-varying parameters, demonstrating the ability of DIPPS to optimize power exchanges and efficiently utilize distributed resources while shifting the external power transfers to specified time durations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Power Plant Testing of Ultrasonic Measurements of Temperature Distributions and Heat Fluxes to Heat Exchange Surfaces

Extreme environmental conditions are common in energy conversion applications, in which the most hardened insertion sensors often do not perform reliably for long. On the other hand, ultrasonic measurements can be acquired noninvasively, with sensitive components kept away from the damaging environment. We have previously developed the ultrasonic method for measuring the spatial distribution of temperatures in solid materials applicable when large thermal gradients are present. In the developed approach, we use the echogenically segmented ultrasound propagation path, structured to contain engineered or naturally occurring echogenic features, to produce a train of echoes in response to an external pulse of ultrasonic excitation. This paper outlines the testing results of this approach applied to the temperature distribution measurements to a waterwall of a 500 MW power-plant utility boiler. The validation results obtained over the prolonged power plant test show that the estimated temperature profile is correctly captured, and the measurement accuracy can be comparable with traditional insertion sensors, such as thermocouples. Overall, the testing has confirmed that the developed approach has matured to become an attractive alternative to conventional sensing in solving challenging problems of long-term temperature measurements in extreme environments. Heat fluxes and thermal stresses in the structure can then be characterized noninvasively using the measured temperature distribution as the basis.

Walton, Kenneth↗
DOE OSTI2022

Hydrological Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

Hydrologic sciences depend on data monitoring, analyses, and simulations of hydrologic processes to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (lab, plot, and field experiments) and secondary sources (remote sensing, UAVs, hydrologic models) that typically follow FAIR Principles (Findable, Accessible, Interoperable, and Reusable: (go-fair.org)). Easy availability of FAIR data has become possible because the hydrology-oriented organizations have pushed the community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. However, it has become difficult to decipher various complex hydrologic processes with increasing data. Machine learning, a branch of artificial intelligence, provide more accurate and faster alternatives to better understand different hydrological processes. The Integrated, Coordinated, Open, Networked (ICON) framework provides a pathway for water users to include and respect diversity, equity, and inclusivity. In addition, ICONs support the integration of peoples with historically marginalized identities into this professional discipline of water sciences. This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them.

(ICON) principles to address↗
DOE OSTI2025

Giant Cloud Condensation Nuclei Facilitate Drizzle Formation in Stratocumulus—Insights From a Combined Observation‐Modeling Framework

The mechanism for initiating drizzle drop remains a gap in the current understanding of warm rain formation. One prevalent hypothesis suggests that the presence of Giant Cloud Condensation Nuclei (GCCN) generates drizzle‐sized drops necessary to trigger the Collision‐Coalescence (C‐C) process. Here, in this study, this hypothesis is investigated using a novel framework that integrates in situ observations, remote sensing measurements, and idealized models. Results show that GCCN can efficiently generate drizzle drops through condensation, producing a broad Droplet Size Distribution (DSD) comparable to in situ observations. The large drizzle drop and broad DSD strongly facilitate C‐C, further accelerating drizzle initiation. To compare with observation, the model‐generated DSDs are used to generate radar Doppler spectra where radar reflectivity and Doppler skewness is estimated. The simulated radar quantities correspond well with radar observations, providing critical evidence for the GCCN‐induced drizzle initiation mechanism.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Optimisation of frequency-modulated characteristics of output radiation in a lidar with Raman amplification

The reported study is aimed at increasing the power in the transmission path of a lidar with Raman amplification for longpath sensing of methane by optimising the frequency-modulated characteristics of the output radiation. The pump current of the used distributed-feedback master laser was modulated by a linearfrequency signal with simultaneous application of a non-synchronous high-frequency signal. For such a modulation regime, the Raman amplifier provided the mean output power of 2.5 W at a wavelength of 1650 nm. The spectral broadening did not significantly decrease the lidar sensitivity at long paths. (lidars)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗