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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

How reliable is distribution of relaxation times (DRT) analysis? A dual regression-classification perspective on DRT estimation, interpretation, and accuracy

The distribution of relaxation times (DRT) has gained increasing attention and adoption in recent years as a versatile method for analyzing electrochemical impedance spectroscopy (EIS) data obtained from complex devices like fuel cells, electrolyzers, and batteries. The DRT deconvolutes the impedance without a priori specification of a generative model, which is especially useful for interpretation and model selection when the governing principles of the system under study are not fully understood. However, DRT estimation is an ill-posed inversion problem that must be addressed with a subjective choice of regularization and tuning, which leaves substantial risk of misleading interpretations of EIS data. In this work, we suggest a new classification view of the DRT inversion to clarify DRT estimation and interpretation. We introduce a dual regression-classification framework that unifies the classification and regression views of the DRT inversion with wide-reaching implications for DRT analysis. The dual framework is employed to demonstrate a new kind of DRT inversion algorithm and develop novel evaluation metrics that capture previously ignored aspects of DRT accuracy. These approaches are applied to both synthetic data and experimental spectra collected from a protonic ceramic fuel cell and a lithium-ion battery to illustrate their broad utility. The dual inversion algorithm shows promising performance for accurate DRT estimation and autonomous model identification, while the dual evaluation approach produces metrics that meaningfully assess the strengths and risks of DRT algorithms. Here this work provides valuable insight for both practical application of the DRT to experimental data and further development of EIS analysis methods.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Neural network modeling of in situ fluid-filled pore size distributions in subsurface shale reservoirs under data constraints

Subsurface nuclear magnetic resonance (NMR) logs acquired in the wellbore environment are sensitive to fluid-filled pore size distribution, fluid mobility, permeability, and porosity in the near-wellbore reservoir volume. NMR response of a formation layer is processed to extract the T2 distribution, which approximates the fluid-filled pore size distribution. NMR logs are acquired in limited number of wells due to financial and operational challenges, which adversely affects reservoir characterization. We developed here two neural-network-based machine learning techniques, long short-term memory (LSTM) network and variational autoencoder with a convolutional layer (VAEc) network, to process the ‘easy-to-acquire’ formation mineral and fluid saturation logs to generate synthetic NMR T2 distributions in the absence of ‘hard-to-acquire’ NMR T2 distribution log. Both the predictive models are trained and tested on limited wireline log measurements randomly selected from a 300-ft depth interval of the Bakken shale formation. Synthesis performances of LSTM and VAEc models in terms of R 2 are 0.78 and 0.75, respectively. Noise is inevitable in logging data due to the complex wellbore and formation conditions. Notably, both the predictive models robustly synthesize the fluid-filled pore size distributions in the presence of 50% noise in input logs and 30% noise in training T2 data. The performance of the proposed methodology improves with access to larger volume of training data from other formation types. The proposed method is critical to the synthesis of in situ fluid-filled pore size distributions in shale formations under data constraints due to financial and operational challenges.

58 GEOSCIENCES↗
NASA NTRS1993

Mapping the distribution of vesicular textures on silicic lavas using the Thermal Infrared Multispectral Scanner

To investigate the effect of vesicularity on TIMS (Thermal Infrared Multispectral Scanner) imagery independent of chemical variations, we studied a large rhyolitic flow of uniform composition but textural heterogeneity. The imagery was recalibrated so that the digital number values for a lake in the scene matched a calculated ideal spectrum for water. TIMS spectra for the lava show useful differences in coarsely and finely vesicular pumice data, particularly in TIMS bands 3 and 4. Images generated by ratioing these bands accurately map out those areas known from field studies to be coarsely vesicular pumice. These texture-related emissivity variations are probably due to the larger vesicles being relatively deeper and separated by smaller septa leaving less smooth glass available to give the characteristic emission of the lava. In studies of inaccessible lava flows (as on Mars) areas of coarsely vesicular pumice must be identified and avoided before chemical variations can be interpreted. Remotely determined distributions of vesicular and glassy textures can also be related to the volatile contents and potential hazards associated with the emplacement of silicic lava flows on Earth.

Ondrusek, Jaime↗
DOE OSTI2025

Gravitational waves and galaxies cross-correlations: a forecast on GW biases for future detectors

ABSTRACT Gravitational waves (GWs) have rapidly become important cosmological probes since their first detection in 2015. As the number of detected events continues to rise, upcoming instruments like Einstein Telescope (ET) and Cosmic Explorer (CE) will observe millions of compact binary mergers. These detections, coupled with galaxy surveys by instruments such as the Dark Spectroscopic Energy Instrument (DESI), Euclid, and the Vera Rubin Observatory, will provide unique information on the large-scale structure of the universe by cross-correlating GWs with the distribution of galaxies hosting them. In this paper, we focus on how cross-correlations constrain the clustering bias of GWs emitted by the coalescence of binary black holes (BBHs). This parameter links BBHs to the underlying dark matter distribution, hence informing us how they populate galaxies. Using a multitracer approach, we forecast the precision of these measurements under different survey combinations. Our results indicate that current GW detectors will have limited precision, with measurement errors as high as $\displaystyle \sim 50~{{\ \rm per\ cent}}$. However, third-generation detectors like ET, when cross-correlated with Legacy Survey of Space and Time (LSST) data, can improve clustering bias measurements to within 2.5 per cent. Furthermore, we demonstrate that these cross-correlations can enable a per cent-level measurement of the magnification lensing effect on GWs. Despite this, there is a degeneracy between magnification and evolution biases, which hinders the precision of both. This degeneracy is most effectively addressed by assuming knowledge of one bias or targeting an optimal redshift range of $\displaystyle 1 \lt z \lt 2.5$. Our analysis opens new avenues for studying the distribution of BBHs and testing the nature of gravity through large-scale structure.

Zazzera, Stefano (ORCID:0000000158979221)↗
DOE OSTI2022

Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) for Transportation Hubs (Final Report)

This report summarizes the work performed under the award number EE0008524. The project develops the Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) platform as the next-generation transportation solution based on autonomous electric vehicles (AEV) serving passenger trips from and to urban transportation hubs, to substantially reduce transportation energy consumption. Extensive data collection and analyses were first conducted to understand the demand patterns and energy consumption of hub-based on-road trips. Then, a data-driven framework that consists of an analytical module and a simulation module was proposed. For the analytical module, five planning + operation tools were developed to support the planning and energy-efficient operations of urban AEV services: the charging station planning that robotically allocates charging supplies based on the stationary charging demand distribution; the transit planning and demand adaptive scheduling model that efficiently generates\ candidate transit routes from hubs to other places and dynamically adjusts the transit time table to fit the current demand; the online energy-efficient routing that learns the energy-optimal paths from observations of link-level energy consumption in real-time; the hub-based ridesharing that matches trip requests together with account for the uncertainty of future trip demand and vehicle supply; and finally, the integrated demand prediction and anomaly detection pipeline that leverages the flight/train time table and support other planning/operation tools. To demonstrate the performance of these tools, a scalable high-performance agent-based simulator was built. We divided the urban space into multiple service zones where each zone was considered as an agent for passenger generation and vehicle charging. Two types of AEV agents were coded to model two types of mobility services: AEV taxi and AEV transit. For the AEV taxi, the team implemented the functions of pickup/drop-off passengers, energy-efficient routing, ridesharing, fleet rebalancing, and recharging. For the AEV bus, the team implemented the functions of demand-adaptive route scheduling, passenger boarding, and recharging. A high-performance computing framework was introduced to receive various profiling information (such as link energy updates, vehicle speed) from the simulator instances and communicate the operational commands back to the instances. The numerical experiments show that each of the proposed operational algorithms can reduce energy consumption and improve system efficiency. Furthermore, there exists the need to collectively consider multiple planning + operational strategies as multiple strategies can influence each other in terms of performance impacts. Recommendations for future work related to AEV planning and simulation are discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1989

Kilometric radiation power flux dependence on area of discrete aurora

Kilometer wavelength radiation, measured from distant positions over the North Pole and over the Earth's equator, was compared to the area of discrete aurora imaged by several low-altitude spacecraft. Through correlative studies of auroral kilometric radiation (AKR) with about two thousand auroral images, a stereoscopic view of the average auroral acceleration region was obtained. A major result is that the total AKR power increases as the area of the discrete auroral oval increases. The implications are that the regions of parallel potentials or the auroral plasma cavities, in which AKR is generated, must possess the following attributes: (1) they are shallow in altitude and their radial position depends on wavelength, (2) they thread flux tubes of small cross section, (3) the generation mechanism in them reaches a saturation limit rapidly, and (4) their distribution over the discrete auroral oval is nearly uniform. The above statistical results are true for large samples collected over a long period of time (about six months). In the short term, AKR frequently exhibits temporal variations with scales as short as three minutes (the resolution of the averaged data used). These fluctuations are explainable by rapid quenchings as well as fast starts of the electron cyclotron maser mechanism. There were times when AKR was present at substantial power levels while optical emissions were below instrument thresholds. A recent theoretical result may account for this set of observations by predicting that suprathermal electrons, of energies as low as several hundred eV, can generate second harmonic AKR. The indirect observations of second harmonic AKR require that these electrons have mirror points high above the atmosphere so as to minimize auroral light emissions. The results provide evidence supporting the electron cyclotron maser mechanism.

Saflekos, N. A.↗
DOE OSTI2018

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Deep learning (DL), a new generation of artificial neural network research, has transformed industries, daily lives, and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem-relevant features and capture highly complex data distributions. I argue that DL can help address several major new and old challenges facing research in water sciences such as interdisciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization. This review paper is intended to provide water resources scientists and hydrologists in particular with a simple technical overview, transdisciplinary progress update, and a source of inspiration about the relevance of DL to water. The review reveals that various physical and geoscientific disciplines have utilized DL to address data challenges, improve efficiency, and gain scientific insights. DL is especially suited for information extraction from image-like data and sequential data. Techniques and experiences presented in other disciplines are of high relevance to water research. Meanwhile, less noticed is that DL may also serve as a scientific exploratory tool. A new area termed AI neuroscience , where scientists interpret the decision process of deep networks and derive insights, has been born. This budding subdiscipline has demonstrated methods including correlation-based analysis, inversion of network-extracted features, reduced-order approximations by interpretable models, and attribution of network decisions to inputs. Moreover, DL can also use data to condition neurons that mimic problem-specific fundamental organizing units, thus revealing emergent behaviors of these units. Vast opportunities exist for DL to propel advances in water sciences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Conditional guided generative diffusion for particle accelerator beam diagnostics

Abstract Advanced accelerator-based light sources such as free electron lasers (FEL) accelerate highly relativistic electron beams to generate incredibly short (10s of femtoseconds) coherent flashes of light for dynamic imaging, whose brightness exceeds that of traditional synchrotron-based light sources by orders of magnitude. FEL operation requires precise control of the shape and energy of the extremely short electron bunches whose characteristics directly translate into the properties of the produced light. Control of short intense beams is difficult due to beam characteristics drifting with time and complex collective effects such as space charge and coherent synchrotron radiation. Detailed diagnostics of beam properties are therefore essential for precise beam control. Such measurements typically rely on a destructive approach based on a combination of a transverse deflecting resonant cavity followed by a dipole magnet in order to measure a beam’s 2D time vs energy longitudinal phase-space distribution. In this paper, we develop a non-invasive virtual diagnostic of an electron beam’s longitudinal phase space at megapixel resolution (1024 × 1024) based on a generative conditional diffusion model. We demonstrate the model’s generative ability on experimental data from the European X-ray FEL.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Data-Driven Method for Groundwater-Level Mapping and Monitoring-Well Network Optimization at Hanford

This report summarizes the initial results and outcomes of a physics-informed, data-driven groundwater level (GWL) mapping capability for the Hanford Site. GWL mapping at Hanford is typically conducted annually and requires a significant amount of computational and expert resources, and it does not allow assessment of the informational value of specific monitoring wells. The proposed method produces spatially and temporally resolved fields consistent with sparse, irregularly sampled, and nonuniformly distributed well measurements. Implemented successfully, this capability will allow rapid mapping of groundwater levels and provide an opportunity to optimize monitoring activities (both location and sampling frequency) based on data information value evaluation. The approach integrates a diffusion-based generative model – trained on MODFLOW simulation data from the Plateau-to-River (P2R) model – with score-based data assimilation (SDA), allowing observation-conditioned mapping without retraining for each monitoring-network layout.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Analytics for Investigation of Disease Outbreaks (AIDO)

Objective: Analytics for the Investigation of Disease Outbreaks (AIDO) is a web-based tool designed to enhance a user’s understanding of unfolding infectious disease events. A representative library of over 650 outbreaks across a wide selection of diseases allows similar outbreaks to be matched to the conditions entered by the user. These historic outbreaks contain detailed information on how the disease progressed as well as what measures were implemented to control its spread, allowing for a better understanding within the context of other outbreaks.Introduction: Situational awareness, or the understanding of elemental components of an event with respect to both time and space, is critical for public health decision-makers during an infectious disease outbreak. AIDO is a web-based tool designed to contextualize incoming infectious disease information during an unfolding event for decision-making purposes.Methods: Public health analysts of the Biology Division at Los Alamos National Laboratory curated a diverse library of historic disease outbreaks from publicly available official reports and peer reviewed literature to serve as a representation of the range of potential outbreak scenarios for a given disease. Available outbreak metadata are used to identify properties that relate to the magnitude and/or duration of the outbreak. Properties vary by disease, as they are related to disease-specific characteristics like transmission, disease manifestation, risk factors related to disease severity, and environmental factors specific to the given location. These properties are then incorporated into a similarity algorithm (s in Figure 1) to identify outbreaks that are similar to user inputs.Results: AIDO currently includes libraries for 39 diseases that are diverse across pathogen type (viral, bacterial and parasitic) as well as transmission type (vectorborne (e.g., Dengue, Malaria), foodborne (e.g., Salmonella, Campylobacteriosis), waterborne (e.g., Cholera), and person-to-person transmitted (e.g., Measles)). In addition to providing a similarity score to the user’s outbreak, we provide aggregated comparisons to multiple historical outbreaks, descriptive statistics to show the distribution of property values for each disease, and extensive contextual information about each outbreak.Conclusions: The analytics provided by AIDO allow users to interact with a unique data set of historic outbreaks and the associated metadata to contextualize incoming information and generate hypotheses about appropriate decisions. The tool is continually updated with new functionalities and additional data.

Daughton, Ashlynn↗
DOE OSTI2023

Code for BALDR Study 07.05 v.1.0.0

SAND2024-01293O The Code for BALDR Study 07.05 software reproduces results from the BALDR study concerning "A Semi-Supervised Learning Method to Produce Explainable Radioisotope Proportion Estimates for NaI-based Synthetic and Measured Gamma Spectra." The code for BALDR Study 07.05 can reproduce a scientific study following the step numbers within the file names. Using synthetic and measured data to find the best model for the radioisotope proportion estimation task of interest, the study generated results for inclusion in a paper. The high-level methods involved are neural networks, semi-supervised learning, and out-of-distribution detection. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Morrow, Tyler↗
NASA NTRS1977

Effect of Winglets on a First-Generation Jet Transport Wing. 2: Pressure and Spanwise Load Distributions for a Semispan Model at High Subsonic Speeds

Pressure and spanwise load distributions on a first-generation jet transport semispan model at high subsonic speeds are presented for the basic wing and for configurations with an upper winglet only, upper and lower winglets, and a simple wing-tip extension. Selected data are discussed to show the general trends and effects of the various configurations.

Montoya, L. C.↗
NASA NTRS1980

Results of an ISEE-1 experiment to study the interactions between energetic particles and discrete VLF waves in the magnetosphere

Despite the malfunctioning of the digital portion of the experiment which is encoding the absolute amplitude of the wave spectrum with a fixed bias of approximately 20 dB, the analog portion of the instrument is acquiring excellent data concerning the wave function and relative amplitude. Results obtained over a 2-year period which have important implications for magnetospheric wave-particle interactions are examined in the areas of emission generation by nonconducted coherent waves, and cold plasma distribution in the inner magnetosphere.

Source record↗
NASA NTRS1982

Ultrasonic Transducer Analyzer

Ultrasonic transducer-beam-intensity distributions are determined by analyzing echoes from a spherical ball. Computers control equipment and process data. Important beam characteristics, such as location of best beam focus and beam diameter at focus, can be determined quickly from extensive set of plots generated by apparatus.

Grounds, M. K.↗
NASA NTRS1991

Magellan mission planning and orbital operations

The goal of NASA's Magellan mission is to understand the geological and geophysical processes that have shaped the planet Venus. Through synthetic aperture radar, radiometric, altimetric, and gravity measurements, the scientific community hopes to improve its knowledge of the planet's topography and internal mass distribution. This paper provides an overview of the Magellan mission; describes the spacecraft sequence software design, development, and test; addresses the nominal operational flow of stored sequence design and generation; briefly describes the ground processing of engineering telemetry and imaging data; and then summarizes the Magellan operational experience with non-routine behavior of the spacecraft during the cruise and mapping portions of the prime mission.

Venus↗
NASA NTRS1994

Analysis of magnetic field levels at KSC

The scope of this work is to evaluate the magnetic field levels of distribution systems and other equipment at Kennedy Space Center (KSC). Magnetic fields levels in several operational areas and various facilities are investigated. Three dimensional mappings and contour are provided along with the measured data. Furthermore, the portion of magnetic fields generated by the 60 Hz fundamental frequency and the portion generated by harmonics are examined. Finally, possible mitigation techniques for attenuating fields from electric panels are discussed.

Christodoulou, Christos G.↗
NASA NTRS1994

Topography and tectonics of the central New Madrid seismic zone: Results of numerical experiements using a three-dimensional boundary element program

We present results of a series of numerical experiments designed to test hypothetical mechanisms that derive deformation in the New Madrid seismic zone. Experiments are constrained by subtle topography and the distribution of seismicity in the region. We use a new boundary element algorithm that permits calcuation of the three-dimensional deformation field. Surface displacement fields are calculated for the New Madrid zone under both far-field (plate tectonics scale) and locally derived driving strains. Results demonstrate that surface displacement fields cannot distinguish between either a far-field simple or pure shear strain field or one that involves a deep shear zone beneath the upper crustal faults. Thus, neither geomorphic nor geodetic studies alone are expected to reveal the ultimate driving mechanism behind the present-day deformation. We have also tested hypotheses about strain accommodation within the New Madrid contractional step-over by including linking faults, two southwest dipping and one vertical, recently inferred from microearthquake data. Only those models with step-over faults are able to predict the observed topography. Surface displacement fields for long-term, relaxed deformation predict the distribution of uplift and subsidence in the contractional step-over remarkably well. Generation of these displacement fields appear to require slip on both the two northeast trending vertical faults and the two dipping faults in the step-over region, with very minor displacements occurring during the interseismic period when the northeast trending vertical faults are locked. These models suggest that the gently dippling central step-over fault is a reverse fault and that the steeper fault, extending to the southeast of the step-over, acts as a normal fault over the long term.

Gomberg, Joan↗
NASA NTRS1997

A Model for Lower Hybrid Wave Excitation Compared with Observations by Viking

The mechanism of lower hybrid wave (LHW) excitation due to the O+ relative drift in a plasma subjected to low-frequency waves (LFWs) is used for analysis of Viking satellite data for events in the cusp/cleft region. In some cases, such a mechanism leads to LHW energy densities and ion distribution functions close to those observed, suggesting the proposed mechanism is a plausible candidate to explain certain classes of LHW generation events in space plasmas.

Khazanov, G. V.↗