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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

A serach for moderate- and high-energy neturino emission correlated with gamma-ray bursts

A temporal correlation analysis between moderate- (60 Mev less than or equal to E(sub nu)greater than or equal to 2500 MeV) and high-energy (E(sub nu) greater than or equal to 2000 MeV) neutrino interactions consist of two types: the moderate-energy interactions that are contained within the volume of IMB-3 and the upward-going muons produced by high-energy nu(sub mu) interactions in the rock around the detector. No evidence is found for moderate- or high-energy neutrino emission from GRBs nor for any neutrino/neutrino correlation. The nonobservation of nu/GRB correlations allows upper limits to be placed on the neutrino flux associated with GRBs.

Becker-Szendy, R.↗
DOE OSTI2024

Addressing Experimental Challenges in Probing Dark Energy with Accuracy and Precision with the Rubin Observatory Legacy Survey of Space & Time (LSST)

With support from this award, the Stanford group under the leadership of PI Patricia Burchat focused on pixel-level algorithms that are key to the investigation of dark energy with the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). The work was carried out through strong engagement in the DOE-supported LSST Dark Energy Science Collaboration (DESC), for which the PI served as Deputy Spokesperson (07/2019 - 07/2021) and Technical Coordinator (07/2021 - 07/2023). Members of the Burchat group are Full DESC members and are among scientists who have been accepted to join the Rubin Observatory System Integration, Test, and Commissioning (SIT-Com) effort. The specific intellectual focus of the group was the optimization of gravitational lensing as a probe of the evolution of the distribution of dark matter in space and time to learn about the nature of dark energy. The Stanford group pursued an integrated research program to understand in detail, and then remediate or calibrate, systematic effects that could limit the sensitivity of the LSST to cosmic shear as a dark energy probe -- particularly those related to our understanding of the atmospheric point spread function (PSF), challenges associated with blended galaxy images, and impacts of residual camera artifacts. Key to this work was developing, enhancing, and validating the fidelity of simulations that play a key role in the development of our understanding of subtle systematic biases and then using these simulations to develop techniques to characterize and mitigate biases, and associated algorithms for implementation in the LSST or DESC science pipelines. The group’s work included contributions to the open-source GalSim simulation package, which is widely used in the community; simulations and studies of spatial correlations of atmospheric PSFs; studies of temporal correlations of the atmospheric PSF; impacts of chromatic PSFs on cosmic shear calibration; continued studies of anisotropic Gaussian process interpolation of PSF models across the focal plane; and measuring and mitigating impacts of residual camera artifacts on cosmic shear. A primary impact of this work is that it increases the scientific discovery potential of the DOE and NSF investments in the Rubin Observatory Legacy Survey of Space \& Time, by addressing challenges in interpreting astronomical images through more accurate descriptions of the blurring of images due to the atmosphere. Specifically, this work impacts our ability to understand the fundamental nature of dark energy.

Burchat, Patricia Rose↗
DOE OSTI2025

Link Scheduling in Satellite Networks via Machine Learning Over Riemannian Manifolds

Low Earth Orbit (LEO) satellites play a crucial role in enhancing global connectivity, serving a complementary solution to existing terrestrial systems. In wireless networks, scheduling is a vital process that allocates time-frequency resources to users for interference management. However, LEO satellite networks face significant challenges in scheduling their links towards ground users due to the satellites’ mobility and overlapping coverage. This paper addresses the dynamic link scheduling problem in LEO satellite networks by considering spatio-temporal correlations introduced by the satellites’ movements. The first step in the proposed solution involves modeling the network over Riemannian manifolds, thanks to their representation as symmetric positive definite matrices. We introduce two machine learning (ML)-based link scheduling techniques that model the dynamic evolution of satellite positions and link conditions over time and space. To accurately predict satellite link states, we present a recurrent neural network (RNN) over Riemannian manifolds, which captures spatio-temporal characteristics over time. Furthermore, we introduce a separate model, the convolutional neural network (CNN) over Riemannian manifolds, which captures geometric relationships between satellites and users by extracting spatial features from the network topology across all links. Simulation results demonstrate that both RNN and CNN over Riemannian manifolds deliver comparable performance to the fractional programming-based link scheduling (FPLinQ) benchmark. Remarkably, unlike other ML-based models that require extensive training data, both models only need 30 training samples to achieve over 99% of the sum rate while maintaining similar computational complexity relative to the benchmark.

42 ENGINEERING↗
NASA NTRSDate not supplied

Characterization of Structural Vibration and Acoustic Radiation with a Beam-Array Doppler Vibrometer

This paper discusses the operational principles of an assembly of two opto-electronic modules with their configurations based on a Laser-Array Doppler Vibrometer (LADV) and a Shack-Hartmann Wavefront Sensor (SHWFS). Both sensors can operate concurrently providing complementary data. While the SHWFS is a well-established system that enables low-bandwidth spatio-temporal characterization of aerodynamic turbulence, the LADV technology allows real-time detection of structural vibrations and associated acoustic radiation fields. Practical examples are shown of the instruments’ capabilities, focusing on the ability to simultaneously capture, visualize and quantitatively characterize full-field non-stationary structural dynamics and unsteady sound fields or transient flow fields around ground test facility airframe models or other structures of interest. The parallel multi-channel architecture of the LADV system enables synchronous detection and temporal correlation of indicators related to the spatio-temporal vibration of the structure under inspection. This unique feature is essential for real time detection and categorization of the structural and acoustic dynamics of transient events. For the present study, the LADV data have been successfully compared with measurements obtained with the SHWFS in the wake of a subsonic scale-model airfoil. The ability of the LADV-SHWFS coupled measurements to perform real time non-intrusive evaluation and characterization of dynamic processes at operationally relevant bandwidths should provide a deeper insight into the complex structural dynamics that contribute to radiated sound fields.

Vladimir B Markov↗
DOE OSTI2021

pnnl/grid_prediction

Two datadriven predictive approaches, namely, {\em Koopman Operator Theoretic (KOT)-based} model and {\em Graph Neural Network (GNN)-based model}, to enable effective power system state predictions. The KOT-based approaches (Robust DMD, deepDMD) capture the power system evolution as a linear dynamical system on an abstract space. The GNNs model the spatio-temporal correlations using graph convolutional network and are called Spatio-Temporal Graph Convolutional Network (STGCN). These predictive models are trained, tested and compared rigorously based on their predictions of frequencies in the IEEE 68 bus system when subjected to a disturbance. GridSTAGE framework developed at Pacific Northwest National Laboratory is leveraged to generate multiple datasets (in the form of PMU measurements) for training and testing by strategically creating load changes across the spatial locations of the network

Nandanoor, Sai Pushpak↗
NASA NTRS2010

Optimization of Noise Performance of Low-Back Ground Detector Arrays

The performance of the 2k x 2k Teledyne H2RG detectors is key to the success of the JWST mission. For broadband imaging, where background limited sensitivity is quickly reached, the key parameter is the quantum efficiency of the detectors, which is, in general, very good. For near infrared spectroscopy and narrow band imaging, we cannot generally reach the back-round limit, so instrument sensitivity is directly dependent on the read noise of the detector. We have initiated a program to analyze the noise characteristics of the H2RG detectors, studying the correlations among the detector outputs and with the reference output, as well as the temporal correlations in a given detector section. Lasing the measured characteristics of the noise correlations, we can determine the optimal coefficients for the removal of correlated noise as a function of frequency. By using available reference sources and adding more frequent references, we have been able to reduce the noise by a factor of two. We find that the detectors have significantly lower noise that observed in prior tests, and can offer significant improvements in the performance of the JWST instruments. We will present the analysis and mitigation techniques, discuss prospects for further improvements with existing detectors, and describe considerations for improvements in next-generation detectors.

Rauscher, Bernard J.↗
NASA NTRS2018

Global-scale Evaluation of SMAP, SMOS and ASCAT Soil Moisture Products Using Triple Collocation

Global-scale surface soil moisture products are currently available from multiple remote sensing platforms. Footprint-scale assessments of these products are generally restricted to limited number of densely-instrumented validation sites. However, by taking active and passive soil moisture products together with a third independent soil moisture estimates via land surface modeling, triple collocation (TC) can be applied to estimate the correlation metric of satellite soil moisture products (versus an unknown ground truth) over a quasi-global domain. Here, an assessment of Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity (SMOS) and Advanced SCATterometer (ASCAT) surface soil moisture retrievals via TC is presented. Considering the potential violation of TC error assumptions, the impact of active-passive and satellite-model error cross correlations on the TC-derived inter-comparison results is examined at in situ sites using quadruple collocation analysis. In addition, confidence intervals for the TC-estimated correlation metric are constructed from moving-block bootstrap sampling designed to preserve the temporal persistence of the original (unevenly-sampled) soil moisture time-series. This study is the first to apply TC to obtain a robust global-scale cross-assessment of SMAP, SMOS and ASCAT soil moisture retrieval accuracy in terms of anomaly temporal correlation. Our results confirm the overall advantage of SMAP (with a global average anomaly correlation of 0.76) over SMOS (0.66) and ASCAT (0.63) that has been established in several recent regional, ground-based studies. SMAP is also the best-performing product over the majority of applicable land pixels (52%), although SMOS and ASCAT each shows advantage in distinct geographic regions.

global-scale↗
NASA NTRS1994

The correlation of solar flare hard X-ray bursts with Doppler blueshifted soft X-ray flare emission

We have investigated the temporal correlation between hard X-ray bursts and the intensity of Doppler blueshifted soft X-ray spectral line emission. We find a strong correlation for many events that have intense blueshifted spectral signatures and some correlation in events with modest blueshifts. The onset of hard X-rays frequently coincides to within a few seconds with the onset of blueshifted emission. The peak intensity of blueshifted emission is frequently close in time to the peak of the hard X-ray emission. Decay rates of the blueshifted and hard X-ray emission are similar, with the decay of the blueshifted emission tending to lag behind the hard X-ray emission in some cases. There are, however, exceptions to these conclusions, and, therefore, the results should not be generalized to all flares. Most of the data for this work were obtained from instruments flown on the Japanese Yohkoh solar spacecraft.

Bentley, R. D.↗
DOE OSTI2020

Microscopic Theory of Long-Time Center-of-Mass Self-Diffusion and Anomalous Transport in Ring Polymer Liquids

We construct a microscopic theory at the level of segment-scale correlated space–time intermolecular forces for the long-time center-of-mass (CM) diffusion constant and intermediate-time non-Fickian transport in dense solutions and melts of ring polymers. The approach combines ideas of polymer, colloid, and liquid-state statistical mechanics to quantify how the multifractal intra-ring conformational structure and inter-ring packing correlations determine dynamic caging constraints and time-dependent friction. Breakdown of Rouse theory is predicted to occur due to length scale-dependent temporal correlation of forces exerted on pairs of tagged ring segments from surrounding polymers. At large enough degrees of polymerization (N), a stronger scaling of the CM diffusion constant (D ∝ N –2 ) is predicted, with a crossover N D proportional to the product of the system-specific macromolecular volume fraction and dimensionless compressibility. In analogy with caging effects in glass-forming fluids, the theory does appear to begin to fail at sufficiently high N/N D for the center-of-mass diffusivity, likely due to another crossover to an even slower activated transport regime. However, use of N/N D with the theoretically predicted N D , in conjunction with dynamic blob scaling ideas for local Rouse friction, collapses semidilute and concentrated solution simulation data onto a master curve. Based on the same physical ideas employed to predict the diffusion constant, a generalized Langevin equation description is formulated for intermediate-time CM transport. It predicts two subdiffusive regimes that emerge due to the self-similar nature of internal ring structure. Analytic and numerical predictions for the apparent non-Fickian exponent as a function of time, N/N D and dimensionless compressibility, the evolution of the maximum degree of subdiffusive motion with N/N D , and the time scale for recovering Fickian diffusion are made, all of which are in good accordance with melt simulations. Furthermore, the present work sets the stage to address activated dynamics and glass formation on the macromolecular scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1977

Morphology and physical parameters of a solar flare

The chromospheric and coronal morphology of the flare of June 15, 1973, (1B/M3) in NOAA active region 131 (McMath 12379) is discussed, and results of quantitative analysis of Skylab soft X-ray observations of the event are presented. H-alpha and white-light observations reveal evidence of emerging flux near the site of the H-alpha flare onset. Preflare and early flare filament activity and its spatial and temporal correlation with the flare are discussed. Spatially resolved soft X-rays are compared with H-alpha and magnetic-field measurements. Full-disk high-temporal-resolution soft X-ray flux measurements and time profiles of the resultant temperature and density calculations are also presented which yield a peak temperature of about 14.2 million K and a peak electron density of approximately 3.5 deg 10 to the -10th power per cu cm and show a preflash-phase temperature rise to about 9 million K. The peak values should be compared with the respective values of approximately 25 million K and 4.5 by 10 to the -10th power per cu cm deduced from the spatially resolved data.

Smith, J. B., Jr.↗
DOE OSTI2026

Temporal Coarse Graining for Classical Stochastic Noise in Quantum Systems

Simulations of quantum systems with Hamiltonian classical stochastic noise can be challenging when the noise exhibits temporal correlations over a multitude of time scales, such as for 1/f noise in solid-state quantum information processors. Here we present an approach for simulating Hamiltonian classical stochastic noise that performs temporal coarse-graining by effectively integrating out the high-frequency components of the noise. We focus on the case where the stochastic noise can be expressed as a sum of Ornstein-Uhlenbeck processes. Temporal coarse-graining is then achieved by conditioning the stochastic process on a coarse realization of the noise, expressing the conditioned stochastic process in terms of a sum of smooth, deterministic functions and bridge processes with boundaries fixed at zero, and performing the ensemble average over the bridge processes. For Ornstein-Uhlenbeck processes, the deterministic components capture all dependence on the coarse realization, and the stochastic bridge processes are not only independent but taken from the same distribution with correlators that can be expressed analytically, allowing the associated noise propagators to be precomputed once for all simulations. This combination of noise trajectories on a coarse time grid and ensemble averaging over bridge processes has practical advantages, such as a simple concatenation rule, that we highlight with numerical examples.

Albash, Tameem [Sandia National Lab. (SNL-NM), Alb↗
NASA NTRS2017

Tropospheric Ozonesonde Profiles at Long-Term U.S. Monitoring Sites: 2. Links Between Trinidad Head, CA, Profile Clusters and Inland Surface Ozone Measurements

Much attention has been focused on the transport of ozone (O3) to the western U.S., particularly given the latest revision of the National Ambient Air Quality Standard to 70 parts per billion by volume (ppbv) of O3. This makes quantifying the contributions of stratosphere-to-troposphere exchange, local pollution, and pollution transport to this region essential. To evaluate free-tropospheric and surface O3 in the western U.S., we use self-organizing maps to cluster 18 years of ozonesonde profiles from Trinidad Head, CA. Three of nine O3 mixing ratio profile clusters exhibit thin laminae of high O3 above Trinidad Head. The high O3 layers are located between 1 and 6 km above mean sea level and reside above an inversion associated with a northern location of the Pacific subtropical high. Ancillary data (reanalyses, trajectories, and remotely sensed carbon monoxide) help identify the high O3 sources in one cluster, but distinguishing mixed influences on the elevated O3 in other clusters is difficult. Correlations between the elevated tropospheric O3 and surface O3 at high-altitude monitors at Lassen Volcanic and Yosemite National Parks, and Truckee, CA, are marked and long lasting. The temporal correlations likely result from a combination of transport of baseline O3 and covarying meteorological parameters. Days corresponding to the high O3 clusters exhibit hourly surface O3 anomalies of +5-10 ppbv compared to a climatology; the positive anomalies can last up to 3 days after the ozonesonde profile. The profile and surface O3 links demonstrate the importance of regular ozonesonde profiling at Trinidad Head.

STE↗
DOE OSTI2025

Muon Time-of-Flight studies for cosmic background rejection in the Short Baseline Near Detector

The Short-Baseline Neutrino (SBN) program at Fermilab is a cutting-edge project in experimental neutrino physics. One of its main goals is to systematically investigate the possible existence of eV-scale sterile neutrinos. This phenomenon has been hypothesized to explain some anomalies found in short-range experiments and, if confirmed, would imply a substantial extension of the Standard Model. SBN also offers an important opportunity to deepen the understanding of neutrino-nucleus interactions in the GeV energy range, through the use of Liquid Argon Time Projection Chambers (LArTPC) detectors, a fundamental technology also for the future DUNE experiment. The SBN experimental infrastructure consists of three detectors aligned along the Booster Neutrino Beamline at Fermilab. Among them, the detector located closest to the neutrino source, SBND (Short-Baseline Near Detector), positioned approximately 110 meters from the target, plays a key role in directly characterizing the initial neutrino flux. This allows for a direct comparison with the measurements from the far detector, ICARUS, located about 600 meters from the source, in order to search for potential signs of anomalous neutrino oscillations. My master's thesis focuses on the commissioning and characterization activities of the SBND detector, with particular reference to the Cosmic Ray Tagger (CRT). The CRT is a subsystem for identifying and rejecting events produced by cosmic rays, which constitute the main source of background for surface experiments like SBND. The activity began with the commissioning of the final components of the detector, as well as their validation to verify their correct functioning and signal acquisition. A central part of my work involved studying the veto efficiency of the CRT system, analyzing the rate of cosmic ray-induced events to quantify any loss of neutrino-induced events caused by cosmic background. This allowed for a more precise evaluation of the systematic impact of the CRT on the useful physics sample. A further phase of my analysis involved an in-depth study of the temporal correlation between the CRT signals and those acquired by the LArTPC's internal photodetector system, consisting of photomultiplier tubes and X-ARAPUCA devices. The objective is to explore the possibility of using combined temporal information as an additional criterion for discriminating between cosmic signals and signals genuinely due to neutrino interaction. Preliminary results indicate the presence of characteristic temporal signatures that could be exploited to improve event selection and increase the purity of the neutrino-induced sample. These methodologies will certainly contribute to the optimization of SBND analysis strategies and, more generally, to a better understanding of background mechanisms in next-generation LArTPC experiments.

Corallo, Annalea [Ferrara U.]↗
NASA NTRS1995

Correlated brightness variations in solar radiative output from the photosphere to the corona

Correlated brightness variations are shown to occur in time series of coronal soft X-rays exclusive of prominent active regions, chromospheric ultraviolet radiation, and the photospheric total solar irradiance corrected for sunspot effects. These temporal correlations suggest that upwardly extending magnetic fields may have a large scale impact on the solar atmosphere in addition to their demonstrable role of generating localized active regions. The correlations have implications for improving and extending solar spectrum variability models.

Lean, J. L.↗
DOE OSTI2022

Faster network disruption from layered oscillatory dynamics

Nonlinear complex network-coupled systems typically have multiple stable equilibrium states. Following perturbations or due to ambient noise, the system is pushed away from its initial equilibrium, and, depending on the direction and the amplitude of the excursion, it might undergo a transition to another equilibrium. It was recently demonstrated [M. Tyloo, J. Phys. Complex. 3 03LT01 (2022)] that layered complex networks may exhibit amplified fluctuations. Here, I investigate how noise with system-specific correlations impacts the first escape time of nonlinearly coupled oscillators. Interestingly, I show that, not only the strong amplification of the fluctuations is a threat to the good functioning of the network but also the spatial and temporal correlations of the noise along the lowest-lying eigenmodes of the Laplacian matrix. Finally, I analyze first escape times on synthetic networks and compare noise originating from layered dynamics to uncorrelated noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Identifying Key Drivers of Wildfires in the Contiguous US Using Machine Learning and Game Theory Interpretation

Abstract Understanding the complex interrelationships between wildfire and its environmental and anthropogenic controls is crucial for wildfire modeling and management. Although machine learning (ML) models have yielded significant improvements in wildfire predictions, their limited interpretability has been an obstacle for their use in advancing understanding of wildfires. This study builds an ML model incorporating predictors of local meteorology, land‐surface characteristics, and socioeconomic variables to predict monthly burned area at grid cells of 0.25° × 0.25° resolution over the contiguous United States. Besides these predictors, we construct and include predictors representing the large‐scale circulation patterns conducive to wildfires, which largely improves the temporal correlations in several regions by 14%–44%. The Shapley additive explanation is introduced to quantify the contributions of the predictors to burned area. Results show a key role of longitude and latitude in delineating fire regimes with different temporal patterns of burned area. The model captures the physical relationship between burned area and vapor pressure deficit, relative humidity (RH), and energy release component (ERC), in agreement with the prior findings. Aggregating the contribution of predictor variables of all the grids by region, analyses show that ERC is the major contributor accounting for 14%–27% to large burned areas in the western US. In contrast, there is no leading factor contributing to large burned areas in the eastern US, although large‐scale circulation patterns featuring less active upper‐level ridge‐trough and low RH two months earlier in winter contribute relatively more to large burned areas in spring in the southeastern US.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Effects of Correlated Errors on the Analysis of Space Geodetic Data

As thermal errors are reduced instrumental and troposphere correlated errors will increasingly become more important. Work in progress shows that troposphere covariance error models improve data analysis results. We expect to see stronger effects with higher data rates. Temperature modeling of delay errors may further reduce temporal correlations in the data.

thermal errors↗