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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Open data sets for assessing photovoltaic system reliability

Photovoltaic (PV) systems have become a cornerstone of renewable energy strategies, particularly due to the significant reduction in solar power costs over the past decade. However, the long-term reliability of PV installations presents a persistent challenge, requiring the development of advanced monitoring and predictive maintenance strategies. A wide range of data types is used to evaluate the health of PV systems, including environmental conditions, electrical performance, and inspection imagery. These data enable methodologies such as machine learning (ML) models for lifetime prediction and computer vision techniques for defect detection. However, the acquisition of high-quality and comprehensive data is difficult, particularly in terms of long-term consistency and data variety. Publicly available data sets serve as valuable resources for addressing these challenges, but they often suffer from fragmentation and are difficult to access. This paper presents a comprehensive review of existing open-source data sets related to PV degradation, analyzing their features, functionalities, and potential applications. We categorize these data sets based on the specific aspects of PV system information they cover, such as environmental conditions, operational monitoring, image inspection and module materials, and propose relevant tools and ML models for processing them. In addition, we propose practices for future data collection and usage, while also discussing potential directions in data-driven research. Our aim is to enhance data utilization and publication among researchers and industry professionals, promoting a deeper understanding of the role of data in enhancing the performance and durability of PV systems.

14 SOLAR ENERGY↗
DOE OSTI2014

Reducing computation in an i-vector speaker recognition system using a tree-structured universal background model

The majority of state-of-the-art speaker recognition systems (SR) utilize speaker models that are derived from an adapted universal background model (UBM) in the form of a Gaussian mixture model (GMM). This is true for GMM supervector systems, joint factor analysis systems, and most recently i-vector systems. In all of the identified systems, the posterior probabilities and sufficient statistics calculations represent a computational bottleneck in both enrollment and testing. We propose a multi-layered hash system, employing a tree-structured GMM–UBM which uses Runnalls’ Gaussian mixture reduction technique, in order to reduce the number of these calculations. Moreover, with this tree-structured hash, we can trade-off reduction in computation with a corresponding degradation of equal error rate (EER). As an example, we also reduce this computation by a factor of 15× while incurring less than 10% relative degradation of EER (or 0.3% absolute EER) when evaluated with NIST 2010 speaker recognition evaluation (SRE) telephone data.

42 ENGINEERING↗
DOE OSTI2014

Impact of fluorine based reactive chemistry on structure and properties of high moment magnetic material

The impact of the fluorine-based reactive ion etch (RIE) process on the structural, electrical, and magnetic properties of NiFe and CoNiFe-plated materials was investigated. Several techniques, including X-ray fluorescence, 4-point-probe, BH looper, transmission electron microscopy (TEM), and electron energy loss spectroscopy (EELS), were utilized to characterize both bulk film properties such as thickness, average composition, Rs, ρ, Bs, Ms, and surface magnetic “dead” layers' properties such as thickness and element concentration. Experimental data showed that the majority of Rs and Bs changes of these bulk films were due to thickness reduction during exposure to the RIE process. ρ and Ms change after taking thickness reduction into account were negligible. The composition of the bulk films, which were not sensitive to surface magnetic dead layers with nano-meter scale, showed minimum change as well. It was found by TEM and EELS analysis that although both before and after RIE there were magnetic dead layers on the top surface of these materials, the thickness and element concentration of the layers were quite different. Prior to RIE, dead layer was actually native oxidation layers (about 2 nm thick), while after RIE dead layer consisted of two sub-layers that were about 6 nm thick in total. Sub-layer on the top was native oxidation layer, while the bottom layer was RIE “damaged” layer with very high fluorine concentration. Two in-situ RIE approaches were also proposed and tested to remove such damaged sub-layers.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Measurement of the Top Quark Mass Using the Matrix Element Technique in Dilepton Final States

We present a measurement of the top quark mass in pp¯ collisions at a center-of-mass energy of 1.96 TeV at the Fermilab Tevatron collider. The data were collected by the D0 experiment corresponding to an integrated luminosity of 9.7 fb-1. The matrix element technique is applied to tt¯ events in the final state containing leptons (electrons or muons) with high transverse momenta and at least two jets. The calibration of the jet energy scale determined in the lepton+jets final state of tt¯ decays is applied to jet energies. This correction provides a substantial reduction in systematic uncertainties. We obtain a top quark mass of mt=173.93±1.84 GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Nuclear Astrophysics in the Storage Ring: Background Suppressed Simultaneous Measurement of (p,γ) and (p,n) Reactions

We report the application of the new elimination of Rutherford elastic scattering technique for the measurement of proton-induced reaction cross sections utilizing stored ions decelerated to astrophysical energies. This approach results in a background reduction factor of about 1 order of magnitude, enabling the first measurement of a (p, n) cross section in a storage ring. Here, the reaction channels 124 Xe⁢(p,n) and 124 Xe⁢(p,γ) have been studied just above the neutron threshold energy. The new data provide valuable constraints for Hauser-Feshbach theory and extrapolation of the (p,γ) cross section to lower energies. Most importantly, for nuclei of limited availability, the method represents a powerful improvement to efficiently study proton-induced reactions at energies within or close to the astrophysical Gamow window, bringing many reaction measurements within reach that were previously inaccessible in the laboratory.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

SVM-Based Parameter Identification for Composite ZIP and Electronic Load Modeling

This paper proposes a parameter identification technique for composite ZIP and electronic loads by leveraging the support vector machine (SVM) approach. Since the active power and the reactive power of electronic loads are piecewise functions of the voltage magnitude, the operating modes of electronic loads are determined by the voltage magnitude. To improve the accuracy of parameter identification, two filters (Hampel and Savitzky–Golay) are employed to preprocess measurements to reduce noise. The data after noise reduction serve as training data for the regression model that is solved by the SVM approach. Numerical results show that the SVM approach with filters can identify the parameters of the composite ZIP and electronic load model with high accuracy

Electronic load↗
DOE OSTI2015

GPU-accelerated regularized iterative reconstruction for few-view cone beam CT

Purpose: The present work proposes an iterative reconstruction technique designed for x-ray transmission computed tomography (CT). The main objective is to provide a model-based solution to the cone-beam CT reconstruction problem, yielding accurate low-dose images via few-views acquisitions in clinically acceptable time frames. Methods: The proposed technique combines a modified ordered subsets convex (OSC) algorithm and the total variation minimization (TV) regularization technique and is called OSC-TV. The number of subsets of each OSC iteration follows a reduction pattern in order to ensure the best performance of the regularization method. Considering the high computational cost of the algorithm, it is implemented on a graphics processing unit, using parallelization to accelerate computations. Results: The reconstructions were performed on computer-simulated as well as human pelvic cone-beam CT projection data and image quality was assessed. In terms of convergence and image quality, OSC-TV performs well in reconstruction of low-dose cone-beam CT data obtained via a few-view acquisition protocol. It compares favorably to the few-view TV-regularized projections onto convex sets (POCS-TV) algorithm. It also appears to be a viable alternative to full-dataset filtered backprojection. Execution times are of 1–2 min and are compatible with the typical clinical workflow for nonreal-time applications. Conclusions: Considering the image quality and execution times, this method may be useful for reconstruction of low-dose clinical acquisitions. It may be of particular benefit to patients who undergo multiple acquisitions by reducing the overall imaging radiation dose and associated risks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

The Building Adapter: Automatic Mapping of Commercial Buildings for Scalable Building Analytics

This project creates new solutions for the manual metadata mapping problem: the costly process of creating a match between a building’s sensor data streams and the inputs of a building analytics engine. This goal is achieved by creating and improving techniques for metadata inference: automatically constructing new contextual information for sensing and control points based on the sensor point names and the raw time series values. The objective is to enable vendors to apply building analytics to 90% of buildings with no manual mapping, and to 10% of buildings with a 90% reduction in manual mapping. These targets are set for all types of metadata required by current analytics engines, including type, location, equipment type, and other relationships. The outcome of this project is a suite of solutions to the manual mapping problem collectively called the Building Adapter that allows vendors to apply analytics engines to new buildings at a significantly reduced cost.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1988

CCD camera system for cometary research

The objective is to upgrade the NASA/GSFC 36 inch telescope instrumentation, primarily with a new charge coupled device (CCD) camera system, to permit an effective monitoring program of cometary activity by means of narrowband imaging and spectroscopic techniques. Researchers have twice taken delivery of the CCD camera system from Princeton Scientific Instruments and twice returned it within six weeks for repair. During the times they had the camera system in the lab, they measured the instrumental performance of the TEK 512 x 512 CCD chip (e.g., readout noise, dark current, etc) and developed the complete operational software for the camera system plus several useful observing and data reduction routines for use at the telescope. The CCD camera system is controlled by an IBM-AT computer. The peripheral equipment and software to permit the efficient transfer of large amounts of data to the LASP's computers (VAXs) and subsequent timely reductions are also in place. The Io torus (S II) emission was monitored with a Fabry-Perot scanning spectrometer, in conjunction with the International Jupiter Watch. The CCD camera system will be coupled to a narrowband interference filter imager and a long-slit spectrograph to provide regular and well-calibrated spatial and spectral observations of comets.

Oliversen, R. J.↗
DOE OSTI2021

A Multiplexed Hybrid Velocimeter

Optical velocimetry systems play a crucial role in studying the dynamic behavior of materials under extreme conditions. Two prominent systems used at the Nevada National Security Site are the velocity interferometer system for any reflector(VISAR) and the photon Doppler velocimeter (PDV) system. Their ability to accurately measure surface motion ranging from 0 to > 20km=s without interacting with material flow enables experimenters to construct thermodynamic equations of state for materials of interest. A number of modifications to VISAR and PDV have taken place over the past five decades, leading to improved performance, increased recording capacity, and a reduction in operational burden. Prior efforts to develop a hybrid velocimeter combining the advantages of both PDV and VISAR into a single diagnostic did not take into account progress in PDV technology or new optical components that had not been developed. This thesis describes the design and implementation of a multiplexed hybrid velocimeter (MHV) based on time-multiplexing techniques, an optical hybrid, and polarization-maintaining fiber and components. Data from experiments, compared with current velocity systems, serve to demonstrate the hybrid's performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials

Design of new microelectronic materials is characterized by several challenges such as high-dimensionality of the atomic structure-composition variable space, formidable cost of directly using high-fidelity simulations for design optimization, dispersity in literature-reported similar materials and synthesis methods, complex physical mechanisms, and mixed qualitative and quantitative design variables that lead to a disjointed design space. Even though machine learning (ML) techniques have been employed to expedite materials innovation, existing methods treat ML and design optimization as two separate processes, failing to resolve the fundamental challenges associated with high dimensionality and mixed-variable complexity. We have developed a ML enhanced mixed-variable material design optimization framework to efficiently extract useful information from existing data in literature and physics-based simulations to guide the autonomous search for optimal materials. Our proposed framework is composed of four computational modules: (1) a natural language processing (NLP) based virtual screening module, (2) classification based concept exploration module, (3) a density functional theory (DFT)-based high-fidelity evaluation model, and (4) a novel latent-variable Gaussian process (LVGP) ML model for mixed-variable problems with uncertainty quantification, which seamlessly integrates with Bayesian Optimization (BO) and achieves superb efficiency through embedded physics-based dimension reduction. Our approach is demonstrated and validated using the testbed of functional materials exhibiting metal-insulation transitions (MITs), with the targeted reversible resistivity changes (∼10^5) near room temperature. At the end of the 30-month project, we have developed a series of new ML techniques using NLP, conditional variational autoencoders, active learning, latent-variable Gaussian processes, integrated with Bayesian optimization. Our project has resulted in new predicted MITs compounds and improved understanding of MITs microscopic mechanisms, which in turn will revolutionize microelectronics science to provide energy-saving solutions. Our research has improved both creativity and efficiency in transforming rare-event discoveries of new functional materials to persistent innovations. In addition to open-sourcing the online MIT database and the classification model, the LVGP open source code has been downloaded more than 15,000 times within two years. More than 40 MIT compounds have been identified and many have been pursued experimentally via collaborators. The research results are published in close to 20 collaborative papers in high-impact journals, such as Chem. Mater., Appl. Phys. Rev., Sci. Rep., among others of design space.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Wave Detection and Tracking Within a Rotating Detonation Engine Through Object Detection

As the operational time window of experimental rotating detonation engines (RDEs) is expanded and the technology matures toward integration within gas turbines, monitoring techniques must evolve to offer computationally efficient and highly time-resolved diagnostics. In this study, computer vision object detection methodology that seeks to reduce data processing time and calculate wave velocity within drastically reduced time intervals as compared to traditional high-frame-rate RDE images analysis techniques is proposed. The adapted you-only-look-once object detection network is trained to detect individual detonation waves within single down-axis RDE images. The wave location and rotational direction detected within a frame are tracked through a series of high-speed images to calculate the frame-to-frame wave velocity with the time-step resolution of $\mathrm{20 μs}$ across a series of frames. The analysis of the annotation box size and image linearization effects is presented, demonstrating the lowest frame-to-frame velocity total uncertainty of $\mathrm{±3.8\%}$ and the highest classification speed of 9.5 frames per second using linearized images. Linearized images “unwrap” the RDE annulus pixel region to a reduced image size. Here, this new method offers great reductions in data processing times and unsteady detonation behavior insight at intervals more comparable to the timescales of detonation wave interactions via the application of machine learning to experimental RDE data.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Ensemble averaged structure–function relationship for nanocrystals: effective superparamagnetic Fe clusters with catalytically active Pt skin [Ensemble averaged structure-function relationship for composite nanocrystals: magnetic bcc Fe clusters with catalytically active fcc Pt skin]

Practical applications require the production and usage of metallic nanocrystals (NCs) in large ensembles. Besides, due to their cluster-bulk solid duality, metallic NCs exhibit a large degree of structural diversity. This poses the question as to what atomic-scale basis is to be used when the structure–function relationship for metallic NCs is to be quantified precisely. In this paper, we address the question by studying bi-functional Fe core-Pt skin type NCs optimized for practical applications. In particular, the cluster-like Fe core and skin-like Pt surface of the NCs exhibit superparamagnetic properties and a superb catalytic activity for the oxygen reduction reaction, respectively. We determine the atomic-scale structure of the NCs by non-traditional resonant high-energy X-ray diffraction coupled to atomic pair distribution function analysis. Using the experimental structure data we explain the observed magnetic and catalytic behavior of the NCs in a quantitative manner. Lastly, we demonstrate that NC ensemble-averaged 3D positions of atoms obtained by advanced X-ray scattering techniques are a very proper basis for not only establishing but also quantifying the structure–function relationship for the increasingly complex metallic NCs explored for practical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Measuring the effectiveness of high-performance Co-Optima biofuels on suppressing soot formation at high temperature

Soot emissions in combustion are unwanted consequences of burning hydrocarbon fuels. The presence of soot during and following combustion processes is an indication of incomplete combustion and has several negative consequences including the emission of harmful particulates and increased operational costs. Efforts have been made to reduce soot production in combustion engines through utilizing oxygenated biofuels in lieu of traditional nonoxygenated feedstocks. The ongoing Co-Optimization of Fuels and Engines (Co-Optima) initiative from the US Department of Energy (DOE) is focused on accelerating the introduction of affordable, scalable, and sustainable biofuels and high-efficiency, low-emission vehicle engines. The Co-Optima program has identified a handful of biofuel compounds from a list of thousands of potential candidates. In this study, a shock tube was used to evaluate the performance of soot reduction of five high-performance biofuels downselected by the Co-Optima program. Current experiments were performed at test conditions between 1,700 and 2,100 K and 4 and 4.7 atm using shock tube and ultrafast, time-resolve laser absorption diagnostic techniques. The combination of shock heating and nonintrusive laser detection provides a state-of-the-art test platform for high-temperature soot formation under engine conditions. Soot reduction was found in ethanol, cyclopentanone, and methyl acetate; conversely, an α-diisobutylene and methyl furan produced more soot compared to the baseline over longer test times. For each biofuel, several reaction pathways that lead towards soot production were identified. The data collected in these experiments are valuable information for the future of renewable biofuel development and their applicability in engines.

09 BIOMASS FUELS↗
DOE OSTI2016

HST HOT-JUPITER TRANSMISSION SPECTRAL SURVEY: CLEAR SKIES FOR COOL SATURN WASP-39b

We present the Hubble Space Telescope (HST) Space Telescope Imaging Spectrograph (STIS) optical transmission spectroscopy of the cool Saturn-mass exoplanet WASP-39b from 0.29-1.025 μ m, along with complementary transit observations from Spitzer IRAC at 3.6 and 4.5 μ m. The low density and large atmospheric pressure scale height of WASP-39b make it particularly amenable to atmospheric characterization using this technique. We detect a Rayleigh scattering slope as well as sodium and potassium absorption features; this is the first exoplanet in which both alkali features are clearly detected with the extended wings predicted by cloud-free atmosphere models. The full transmission spectrum is well matched by a clear H{sub 2}-dominated atmosphere, or one containing a weak contribution from haze, in good agreement with the preliminary reduction of these data presented in Sing et al. WASP-39b is predicted to have a pressure-temperature profile comparable to that of HD 189733b and WASP-6b, making it one of the coolest transiting gas giants observed in our HST STIS survey. Despite this similarity, WASP-39b appears to be largely cloud-free, while the transmission spectra of HD 189733b and WASP-6b both indicate the presence of high altitude clouds or hazes. These observations further emphasize the surprising diversity of cloudy and cloud-free gas giant planets in short-period orbits and the corresponding challenges associated with developing predictive cloud models for these atmospheres.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1985

Mathematical analysis study for radar data processing and enhancement. Part 1: Radar data analysis

A study is performed under NASA contract to evaluate data from an AN/FPS-16 radar installed for support of flight programs at Dryden Flight Research Facility of NASA Ames Research Center. The purpose of this study is to provide information necessary for improving post-flight data reduction and knowledge of accuracy of derived radar quantities. Tracking data from six flights are analyzed. Noise and bias errors in raw tracking data are determined for each of the flights. A discussion of an altiude bias error during all of the tracking missions is included. This bias error is defined by utilizing pressure altitude measurements made during survey flights. Four separate filtering methods, representative of the most widely used optimal estimation techniques for enhancement of radar tracking data, are analyzed for suitability in processing both real-time and post-mission data. Additional information regarding the radar and its measurements, including typical noise and bias errors in the range and angle measurements, is also presented. This is in two parts. This is part 1, an analysis of radar data.

James, R.↗
NASA NTRS1985

Mathematical analysis study for radar data processing and enchancement. Part 2: Modeling of propagation path errors

A study is performed under NASA contract to evaluate data from an AN/FPS-16 radar installed for support of flight programs at Dryden Flight Research Facility of NASA Ames Research Center. The purpose of this study is to provide information necessary for improving post-flight data reduction and knowledge of accuracy of derived radar quantities. Tracking data from six flights are analyzed. Noise and bias errors in raw tracking data are determined for each of the flights. A discussion of an altitude bias error during all of the tracking missions is included. This bias error is defined by utilizing pressure altitude measurements made during survey flights. Four separate filtering methods, representative of the most widely used optimal estimation techniques for enhancement of radar tracking data, are analyzed for suitability in processing both real-time and post-mission data. Additional information regarding the radar and its measurements, including typical noise and bias errors in the range and angle measurements, is also presented. This report is in two parts. This is part 2, a discussion of the modeling of propagation path errors.

James, R.↗
NASA NTRS2002

Investigation of Error Patterns in Geographical Databases

The objective of the research conducted in this project is to develop a methodology to investigate the accuracy of Airport Safety Modeling Data (ASMD) using statistical, visualization, and Artificial Neural Network (ANN) techniques. Such a methodology can contribute to answering the following research questions: Over a representative sampling of ASMD databases, can statistical error analysis techniques be accurately learned and replicated by ANN modeling techniques? This representative ASMD sample should include numerous airports and a variety of terrain characterizations. Is it possible to identify and automate the recognition of patterns of error related to geographical features? Do such patterns of error relate to specific geographical features, such as elevation or terrain slope? Is it possible to combine the errors in small regions into an error prediction for a larger region? What are the data density reduction implications of this work? ASMD may be used as the source of terrain data for a synthetic visual system to be used in the cockpit of aircraft when visual reference to ground features is not possible during conditions of marginal weather or reduced visibility. In this research, United States Geologic Survey (USGS) digital elevation model (DEM) data has been selected as the benchmark. Artificial Neural Networks (ANNS) have been used and tested as alternate methods in place of the statistical methods in similar problems. They often perform better in pattern recognition, prediction and classification and categorization problems. Many studies show that when the data is complex and noisy, the accuracy of ANN models is generally higher than those of comparable traditional methods.

Dryer, David↗