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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Neutrinoless double beta decay and nuclear matrix elements

The fundamental importance of searching for neutrinoless double-beta decay (0{nu}{beta}{beta}-decay) is widely recognized. Observation of the decay would tell us that the total lepton number is not conserved and that, consequently, neutrinos are massive Majorana fermions. The 0{nu}{beta}{beta}-decay is discussed in context of neutrino oscillation data. The perspectives of the experimental 0{nu}{beta}{beta}-decay searches are analyzed. The importance of reliable determination of the 0{nu}{beta}{beta}-decay nuclear matrix elements is pointed out.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Field Validation of Dynamic Mechanical Torque Measurements for Geared Wind Turbines

Accurate knowledge of the mechanical loads of wind turbine gearboxes has become essential in modern, highly loaded gearbox designs, as maintaining or even improving gearbox reliability with increasing torque density demands is proving to be challenging. Unfortunately, the traditional method of measuring dynamic mechanical torque using strain gauges placed on the outer surface of a rotating shaft and transmitting the resulting signal is unsuitable for serial deployment due to technical and economic constraints. An alternative method based on fiber-optic strain sensors placed on the stationary outer surface of the gearbox ring gear has been proposed. Like shaft torsion, the radial deformation of the ring gear is proportionate to the rotor torque. Placing the sensors on a stationary component is a cost-effective alternative for serial implementation because the need for complex and expensive data transfer via wireless transmission or a slip ring is eliminated. In this paper, we present the results of an extensive field experiment conducted to evaluate the torque measurement accuracy of this novel sensing solution installed on the gearbox of a Gamesa G97 2-MW wind turbine at the National Renewable Energy Laboratory's Flatirons Campus. Torque measurements derived from fiber-optic strain sensors placed on the ring gear of the planetary stage are compared to conventional torque measurements from strain gauges placed on the main shaft. Two different torque estimation data processing methods were evaluated, with the method based on operational deflection shapes providing the most accurate results with an average normalized root mean square error below 0.7% for a load revolution distribution analysis. The effect of operating conditions on the torque estimate was also investigated, and the third planet-passing operational deflection shape was found to be the least sensitive to nontorque load-related effects. The fiber-optic strain sensors' successful operation during the complete test campaign has demonstrated a robust and accurate solution for fleet-wide enhanced gearbox remaining useful life estimation.

17 WIND ENERGY↗
DOE OSTI2016

Advanced Grid-Friendly Controls Demonstration Project for Utility-Scale PV Power Plants

A typical photovoltaic (PV) power plant consists of multiple power electronic inverters and can contribute to grid stability and reliability through sophisticated 'grid-friendly' controls. The availability and dissemination of actual test data showing the viability of advanced utility-scale PV controls among all industry stakeholders can leverage PV's value from being simply an energy resource to providing additional ancillary services that range from variability smoothing and frequency regulation to power quality. Strategically partnering with a selected utility and/or PV power plant operator is a key condition for a successful demonstration project. The U.S. Department of Energy's (DOE's) Solar Energy Technologies Office selected the National Renewable Energy Laboratory (NREL) to be a principal investigator in a two-year project with goals to (1) identify a potential partner(s), (2) develop a detailed scope of work and test plan for a field project to demonstrate the gird-friendly capabilities of utility-scale PV power plants, (3) facilitate conducting actual demonstration tests, and (4) disseminate test results among industry stakeholders via a joint NREL/DOE publication and participation in relevant technical conferences. The project implementation took place in FY 2014 and FY 2015. In FY14, NREL established collaborations with AES and First Solar Electric, LLC, to conduct demonstration testing on their utility-scale PV power plants in Puerto Rico and Texas, respectively, and developed test plans for each partner. Both Puerto Rico Electric Power Authority and the Electric Reliability Council of Texas expressed interest in this project because of the importance of such advanced controls for the reliable operation of their power systems under high penetration levels of variable renewable generation. During FY15, testing was completed on both plants, and a large amount of test data was produced and analyzed that demonstrates the ability of PV power plants to provide various types of new grid-friendly controls.

14 SOLAR ENERGY↗
DOE OSTI2023

Estimation of combinatoric background in seaquest using an event-mixing method

All experiments observing dilepton pairs (e.g. e + e -, μ + μ - ) must confront the existence of a combinatoricbackground caused by the combining of tracks not arising from the same physics vertex. Some method must be devised to calculate and remove this background. In this document we describe a particular event-mixing method relying on many of the unique aspects of the SeaQuest spectrometer and data. The method described here calculates the combinatoric background with correct normalization; i.e., there is no need to assign a floating normalization factor that is then determined in a subsequent fitting procedure. Numerous tests are applied to demonstrate the reliability of the method.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

NuSTAR J033202-2746.8: Direct constraints on the Compton reflection in a heavily obscured quasar at z ≈ 2

We report Nuclear Spectroscopic Telescope Array (NuSTAR) observations of NuSTAR J033202-2746.8, a heavily obscured, radio-loud quasar detected in the Extended Chandra Deep Field-South, the deepest layer of the NuSTAR extragalactic survey (∼400 ks, at its deepest). NuSTAR J033202-2746.8 is reliably detected by NuSTAR only at E > 8 keV and has a very flat spectral slope in the NuSTAR energy band (Γ=0.55{sub −0.64}{sup +0.62}; 3-30 keV). Combining the NuSTAR data with extremely deep observations by Chandra and XMM-Newton (4 Ms and 3 Ms, respectively), we constrain the broad-band X-ray spectrum of NuSTAR J033202-2746.8, indicating that this source is a heavily obscured quasar (N{sub H}=5.6{sub −0.8}{sup +0.9}×10{sup 23} cm{sup –2}) with luminosity L {sub 10-40} {sub keV} ≈ 6.4 × 10{sup 44} erg s{sup –1}. Although existing optical and near-infrared (near-IR) data, as well as follow-up spectroscopy with the Keck and VLT telescopes, failed to provide a secure redshift identification for NuSTAR J033202-2746.8, we reliably constrain the redshift z = 2.00 ± 0.04 from the X-ray spectral features (primarily from the iron K edge). The NuSTAR spectrum shows a significant reflection component (R=0.55{sub −0.37}{sup +0.44}), which was not constrained by previous analyses of Chandra and XMM-Newton data alone. The measured reflection fraction is higher than the R ∼ 0 typically observed in bright radio-loud quasars such as NuSTAR J033202-2746.8, which has L {sub 1.4} {sub GHz} ≈ 10{sup 27} W Hz{sup –1}. Constraining the spectral shape of active galactic nuclei (AGNs), including bright quasars, is very important for understanding the AGN population, and can have a strong impact on the modeling of the X-ray background. Our results show the importance of NuSTAR in investigating the broad-band spectral properties of quasars out to high redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Nanoscale dislocation shear loops at static equilibrium and finite temperature

Atomistic simulations are used to determine the resolved shear stress necessary for equilibrium and the resulting geometry of nanoscale dislocation shear loops in Al. Dislocation loops with different sizes and shapes are created via superposition of elemental triangular dislocation displacement fields in the presence of an externally imposed shear stress. First, a bisection algorithm is developed to determine systematically the resolved shear stress necessary for equilibrium at 0 K. Here, this approach allows for the identification of dislocation core structure and a correlation between dislocation loop size, shape and the computed shear stress for equilibrium. It is found, in agreement with predictions made by Scattergood and Bacon, that the equilibrium shape of a dislocation loop becomes more circular with increasing loop size. Second, the bisection algorithm is extended to study the influence of temperature on the resolved shear stress necessary for stability. An approach is presented to compute the effective lattice friction stress, including temperature dependence, for dislocation loops in Al. The temperature dependence of the effective lattice friction stress can be reliably computed for dislocation loops larger than 16.2 nm. However, for dislocation loops smaller than this threshold, the effective lattice friction stress shows a dislocation loop size dependence caused by significant overlap of the stress fields on the interior of the dislocation loops. Combined, static and finite temperature atomistic simulations provide essential data to parameterize discrete dislocation dynamics simulations.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS1975

Real time solar magnetograph Skylab mission Atlas

An atlas of all magnetic field observations made during the Skylab missions with the Real Time Solar Magnetograph system located at the Marshall Space Flight Center is presented. Also included are a description of the system and its operation; an outline of the data reductions performed; and a discussion of probable errors, noise, magnetic sensitivity, and system reliability.

Hagyard, M. J.↗
NASA NTRS1985

Investigation on experimental techniques to detect, locate and quantify gear noise in helicopter transmissions

A robotic system to automate the detection, location, and quantification of gear noise using acoustic intensity measurement techniques has been successfully developed. Major system components fabricated under this grant include an instrumentation robot arm, a robot digital control unit and system software. A commercial, desktop computer, spectrum analyzer and two microphone probe complete the equipment required for the Robotic Acoustic Intensity Measurement System (RAIMS). Large-scale acoustic studies of gear noise in helicopter transmissions cannot be performed accurately and reliably using presently available instrumentation and techniques. Operator safety is a major concern in certain gear noise studies due to the operating environment. The man-hours needed to document a noise field in situ is another shortcoming of present techniques. RAIMS was designed to reduce the labor and hazard in collecting data and to improve the accuracy and repeatability of characterizing the acoustic field by automating the measurement process. Using RAIMS a system operator can remotely control the instrumentation robot to scan surface areas and volumes generating acoustic intensity information using the two microphone technique. Acoustic intensity studies requiring hours of scan time can be performed automatically without operator assistance. During a scan sequence, the acoustic intensity probe is positioned by the robot and acoustic intensity data is collected, processed, and stored.

Flanagan, P. M.↗
DOE OSTI2020

Decreasing wind speed extrapolation error via domain-specific feature extraction and selection

Abstract. Model uncertainty is a significant challenge in the wind energy industry and can lead to mischaracterization of millions of dollars' worth of wind resources. Machine learning methods, notably deep artificial neural networks (ANNs), are capable of modeling turbulent and chaotic systems and offer a promising tool to produce high-accuracy wind speed forecasts and extrapolations. This paper uses data collected by profiling Doppler lidars over three field campaigns to investigate the efficacy of using ANNs for wind speed vertical extrapolation in a variety of terrains, and it quantifies the role of domain knowledge in ANN extrapolation accuracy. A series of 11 meteorological parameters (features) are used as ANN inputs, and the resulting output accuracy is compared with that of both standard log-law and power-law extrapolations. It is found that extracted nondimensional inputs, namely turbulence intensity, current wind speed, and previous wind speed, are the features that most reliably improve the ANN's accuracy, providing up to a 65 % and 52 % increase in extrapolation accuracy over log-law and power-law predictions, respectively. The volume of input data is also deemed important for achieving robust results. One test case is analyzed in depth using dimensional and nondimensional features, showing that the feature nondimensionalization drastically improves network accuracy and robustness for sparsely sampled atmospheric cases.

17 WIND ENERGY↗
DOE OSTI2012

Wind Turbine Gearbox Condition Monitoring Round Robin Study – Vibration Analysis

The Gearbox Reliability Collaborative (GRC) at the National Wind Technology Center (NWTC) tested two identical gearboxes. One was tested on the NWTCs 2.5 MW dynamometer and the other was field tested in a turbine in a nearby wind plant. In the field, the test gearbox experienced two oil loss events that resulted in damage to its internal bearings and gears. Since the damage was not severe, thetest gearbox was removed from the field and retested in the NWTCs dynamometer before it was disassembled. During the dynamometer retest, some vibration data along with testing condition information were collected. These data enabled NREL to launch a Wind Turbine Gearbox Condition Monitoring Round Robin project, as described in this report. The main objective of this project was to evaluatedifferent vibration analysis algorithms used in wind turbine condition monitoring (CM) and find out whether the typical practices are effective. With involvement of both academic researchers and industrial partners, the project sets an example on providing cutting edge research results back to industry.

17 WIND ENERGY↗
DOE OSTI2016

WE-H-BRC-09: Simulated Errors in Mock Radiotherapy Plans to Quantify the Effectiveness of the Physics Plan Review

Purpose: A standard tool for ensuring the quality of radiation therapy treatments is the initial physics plan review. However, little is known about its performance in practice. The goal of this study is to measure the effectiveness of physics plan review by introducing simulated errors into “mock” treatment plans and measuring the performance of plan review by physicists. Methods: We generated six mock treatment plans containing multiple errors. These errors were based on incident learning system data both within the department and internationally (SAFRON). These errors were scored for severity and frequency. Those with the highest scores were included in the simulations (13 errors total). Observer bias was minimized using a multiple co-correlated distractor approach. Eight physicists reviewed these plans for errors, with each physicist reviewing, on average, 3/6 plans. The confidence interval for the proportion of errors detected was computed using the Wilson score interval. Results: Simulated errors were detected in 65% of reviews [51–75%] (95% confidence interval [CI] in brackets). The following error scenarios had the highest detection rates: incorrect isocenter in DRRs/CBCT (91% [73–98%]) and a planned dose different from the prescribed dose (100% [61–100%]). Errors with low detection rates involved incorrect field parameters in record and verify system (38%, [18–61%]) and incorrect isocenter localization in planning system (29% [8–64%]). Though pre-treatment QA failure was reliably identified (100%), less than 20% of participants reported the error that caused the failure. Conclusion: This is one of the first quantitative studies of error detection. Although physics plan review is a key safety measure and can identify some errors with high fidelity, others errors are more challenging to detect. This data will guide future work on standardization and automation. Creating new checks or improving existing ones (i.e., via automation) will help in detecting those errors with low detection rates.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Data Generation for Machine Learning Interatomic Potentials and Beyond

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data. A paramount challenge lies in constructing training sets that capture specific domains in the vast chemical and structural space. This Review navigates the intricate landscape of essential components and integrity of training data that ensure the extensibility and transferability of the resulting models. We delve into the details of active learning, discussing its various facets and implementations. We outline different types of uncertainty quantification applied to atomistic data acquisition and the correlations between estimated uncertainty and true error. The role of atomistic data samplers in generating diverse and informative structures is highlighted. Furthermore, we discuss data acquisition via modified and surrogate potential energy surfaces as an innovative approach to diversify training data. The Review also provides a list of publicly available data sets that cover essential domains of chemical space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Comparison of Machine Learning Approaches for Prediction of the Equivalent Alkane Carbon Number for Microemulsions Based on Molecular Properties

The chemical properties of oils are vital in the design of microemulsion systems. The hydrophilic–lipophilic difference equation used to predict microemulsions’ phase behavior expresses the oils’ physiochemical properties as the equivalent alkane carbon number (EACN). The experimental determination of EACN requires knowledge of the temperature dependence of the microemulsion system and the effects of different surfactant concentrations. Thus, the experimental determination is time-intensive and tedious, requiring days to months for proper separations. Furthermore, the experiments require high purity of chemicals because microemulsions are sensitive to impurities. Our work focuses on the quick and reliable predictions of the EACN with machine learning (ML) models. Due to the immaturity of ML chemical predictions, we compare three graph neural networks (GNNs) and a gradient-boosted tree algorithm, known as XGBoost. The GNNs use the molecular structures represented as simplified molecular-input line-entry system (SMILES) codes for the initial input, which allows us to assess whether geometry optimization is necessary for reliable results. The XGBoost model also begins with the SMILES representations of the molecules but uses molecular descriptors instead of geometry optimizations. As a result, the best model tested (crystal graph convolutional neural network with Merck molecular force field-94) has an error of 1.15 EACN units of the true EACN for unknown data with the errors skewed toward zero and an R² score of 0.9

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Evaluation of a deformable registration algorithm for subsequent lung computed tomography imaging during radiochemotherapy

Purpose: Rating both a lung segmentation algorithm and a deformable image registration (DIR) algorithm for subsequent lung computed tomography (CT) images by different evaluation techniques. Furthermore, investigating the relative performance and the correlation of the different evaluation techniques to address their potential value in a clinical setting. Methods: Two to seven subsequent CT images (69 in total) of 15 lung cancer patients were acquired prior, during, and after radiochemotherapy. Automated lung segmentations were compared to manually adapted contours. DIR between the first and all following CT images was performed with a fast algorithm specialized for lung tissue registration, requiring the lung segmentation as input. DIR results were evaluated based on landmark distances, lung contour metrics, and vector field inconsistencies in different subvolumes defined by eroding the lung contour. Correlations between the results from the three methods were evaluated. Results: Automated lung contour segmentation was satisfactory in 18 cases (26%), failed in 6 cases (9%), and required manual correction in 45 cases (66%). Initial and corrected contours had large overlap but showed strong local deviations. Landmark-based DIR evaluation revealed high accuracy compared to CT resolution with an average error of 2.9 mm. Contour metrics of deformed contours were largely satisfactory. The median vector length of inconsistency vector fields was 0.9 mm in the lung volume and slightly smaller for the eroded volumes. There was no clear correlation between the three evaluation approaches. Conclusions: Automatic lung segmentation remains challenging but can assist the manual delineation process. Proven by three techniques, the inspected DIR algorithm delivers reliable results for the lung CT data sets acquired at different time points. Clinical application of DIR demands a fast DIR evaluation to identify unacceptable results, for instance, by combining different automated DIR evaluation methods.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A geospatial risk analysis graphical user interface for identifying hazardous chemical emission sources

Background: Performing back trajectory and forward trajectory using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a reliable approach for assessing particle transport after release among mid-field atmospheric models. HYSPLIT has an externally facing online interface that allows non-expert users to run the model trajectories without requiring extensive training or programming. However, the existing HYSPLIT interface is limited if simulations have a large amount of meteorological data and timesteps that are not coincident. The objective of this study is to design and develop a more robust tool to rapidly evaluate hazard transport conditions and to perform risk analysis, while still maintaining an intuitive and user-friendly interface. Methods: HYSPLIT calculates forward and backward trajectories of particles based on wind speed, wind direction, and the corresponding location, timestamp, and Pasquill stability classes of the regions of the atmosphere in terms of the wind speed, the amount of solar radiation, and the fractional cloud cover. The computed particle transport trajectories, combined with the online Proton Transfer Reaction-Mass Spectrometry (PTR-MS) data (https://figshare.com/articles/dataset/ARL_Data_from_PROS_station_at_Hanford_site/19993964), can be used to identify and quantify the sources and affected area of the hazardous chemicals’ emission using the potential source distribution function (PSDF). PSDF is an improved statistical function based on the well-known potential source contribution function (PSCF) in establishing the air pollutant source and receptor relationship. Performing this analysis requires a range of meteorological and pollutant concentration measurements to be statistically meaningful. The existing HYSPLIT graphical user interface (GUI) does not easily permit computations of trajectories of a dataset of meteorological data in high temporal frequency. To improve the performance of HYSPLIT computations from a large dataset and enhance risk analysis of the accidental release of material at risk, a geospatial risk analysis tool (GRAT-GUI) is created to allow large data sets to be processed instantaneously and to provide ease of visualization. Results: The GRAT-GUI is a native desktop-based application and can be run in any Windows 10 system without any internet access requirements, thus providing a secure way to process large meteorological datasets even on a standalone computer. GRAT-GUI has features to import, integrate, and convert meteorological data with various formats for hazardous chemical emission source identification and risk analysis as a self-explanatory user interface. The tool is available at https://figshare.com/articles/software/GRAT/19426742.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1973

The applicability and effectiveness of cluster analysis

An insight into the characteristics which determine the performance of a clustering algorithm is presented. In order for the techniques which are examined to accurately cluster data, two conditions must be simultaneously satisfied. First the data must have a particular structure, and second the parameters chosen for the clustering algorithm must be correct. By examining the structure of the data from the Cl flight line, it is clear that no single set of parameters can be used to accurately cluster all the different crops. The effectiveness of either a noniterative or iterative clustering algorithm to accurately cluster data representative of the Cl flight line is questionable. Thus extensive a prior knowledge is required in order to use cluster analysis in its present form for applications like assisting in the definition of field boundaries and evaluating the homogeneity of a field. New or modified techniques are necessary for clustering to be a reliable tool.

Ingram, D. S.↗
NASA NTRS2017

Solar Magnetic Activity and Solar-Stellar Connections

Prediction of solar magnetic activity on various temporal scales is a fundamental element of space weather, which requires a wide range of theoretical and observational expertise in solar phenomena from the deep interior to the corona. Historical observations have revealed many features of cyclic variations of the solar activity; but these data are dramatically insufficient to draw a physical picture of global magnetic field evolution. New observational data, currently available from space missions and ground-based observatories, provide us with detailed information about solar dynamics and magnetism. However, because of the relatively short duration of data series and the great variety of data types and quality, it is challenging to assimilate these data in theoretical models and make reliable forecasts. The recent unexpectedly weak solar activity cycles, as well as observations of rotational and magnetic topology transitions in solar-type stars, suggest that the Sun and its magnetic dynamo are currently in a very interesting evolutionary stage. This could relate to the difficulty in getting a model of the Sun to produce solar-like – rather than anti-solar-like – differential rotation, to reproduce the rotation profile obtained from helioseismology, and to predict solar activity cycles.

magnetic↗