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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Full-waveform, Laser Altimeter Measurements of Vegetation Vertical Structure and Sub-canopy Topography in Support of the North American Carbon Program

Full-waveform, scanning laser altimeters (i.e. lidar) provide a unique and precise view of the vertical and horizontal structure of vegetation across wide swaths. These unique laser altimeters systems are able to simultaneously image sub-canopy topography and the vertical structure of any overlying vegetation. These data reveal the true 3-D distribution of vegetation in leaf-on conditions enabling important biophysical parameters such as canopy height and aboveground biomass to be estimated with unprecedented accuracy. An airborne lidar mission was conducted in the summer of 2003 in support of preliminary studies for the North America Carbon Program. NASA's Laser Vegetation Imaging Sensor (LVIS) was used to image approximately 2,000 sq km in Maine, New Hampshire, Massachusetts and Maryland. Areas with available ground and other data were included (e.g., experimental forests, FLUXNET sites) in order to facilitate numerous bio- and geophysical investigations. Data collected included ground elevation and canopy height measurements for each laser footprint, as well as the vertical distribution of intercepted surfaces (i.e. the return waveform). Data are currently available at the LVIS website (http://lvis.gsfc.nasa.gov/). Further details of the mission, including the lidar system technology, the locations of the mapped areas, and examples of the numerous data products that can be derived from the return waveform data products are available on the website and will be presented. Future applications including potential fusion with other remote sensing data sets and a spaceborne implementation of wide-swath, full-waveform imaging lidar will also be discussed.

Blair, B.↗
NASA NTRS2012

Bridging Informatics and Earth Science: a Look at Gregory Leptoukh's Contributions

With the tragic passing this year of Gregory Leptoukh, the Earth and Space Sciences community lost a tireless participant in--and advocate for--science informatics. Throughout his career at NASA, Dr. Leptoukh established a theme of bridging the gulf between the informatics and science communities. Nowhere is this more evident than his leadership in the development of Giovanni (GES DISC Interactive Online Visualization ANd aNalysis Infrastructure). Giovanni is an online tool that serves to hide the often-complex technical details of data format and structure, making science data easier to explore and use by Earth scientists. To date Giovanni has been acknowledged as a contributor in 500-odd scientific articles. In recent years, Leptoukh concentrated his efforts on multi-sensor data inter-comparison, merging and fusion. This work exposed several challenges at the intersection of data and science. One of these was the ease with which a naive user might generate spurious comparisons, a potential hazard that was the genesis of the Multi-sensor Data Synergy Advisor (MDSA). The MDSA uses semantic ontologies and inference rules to organize knowledge about dataset quality and other salient characteristics in order to advise users on potential caveats for comparing or merging two datasets. Recently, Leptoukh also led the development of AeroStat, an online Giovanni instance to investigate aerosols via statistics from station and satellite comparisons and merged maps of data from more than one instrument. Aerostat offers a neural net based bias adjustment to harmonize the data by removing systematic offsets between datasets before merging. These examples exhibit Leptoukh's talent for adopting advanced computer technologies in the service of making science data more accessible to researchers. In this, he set an example that is at once both vital and challenging for the ESSI community to emulate.

Source record↗
DOE OSTI2023

The fast camera (Fastcam) imaging diagnostic systems on the DIII-D tokamak

Two camera systems are installed on the DIII-D tokamak at the toroidal positions of 90° (90° system) and 225° (225° system), respectively. The cameras have two types of relay optics, namely, a coherent optical fiber bundle and a periscope system. The periscope system provides absolute intensity calibration stability while sacrificing resolution (10 lp/mm), while the fiber system provides high resolution (16 lp/mm) while sacrificing calibration stability. The periscope is available only for the 90° system. The optics of the 225° system were designed for view stability, repeatability, and easy maintenance. The cameras are located inside optimized neutron, x ray and magnetic shielding in order to reduce electronics damage, reboots, and magnetic and neutron interference, increasing the overall system reliability. An automated filter wheel, providing remote filter change, allows for remote wavelength selection. A software suite automates camera acquisition and data storage, allowing for remote operation and reduced operator involvement. System metadata is used to streamline the data analysis workflow, particularly for intensity calibration. Here, the spatial calibration uses multiple observable wall features, resulting in a reconstruction accuracy ≤2 cm.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion

Combinatorial fusion analysis (CFA) is an approach for combining multiple scoring systems using the rank-score characteristic function and cognitive diversity measure. One example is to combine diverse machine learning models to achieve better prediction quality. In this work, we apply CFA to the synthesis of metal halide perovskites containing organic ammonium cations via inverse temperature crystallization. Using a data set generated by high-throughput experimentation, four individual models (support vector machines, random forests, weighted logistic classifier, and gradient boosted trees) were developed. We characterize each of these scoring systems and explore 66 possible combinations of the models. When measured by the precision on predicting crystal formation, the majority of the combination models improves the individual model results. The best combination models outperform the best individual models by 3.9 percentage points in precision. In addition to improving prediction quality, we demonstrate how the fusion models can be used to identify mislabeled input data and address issues of data quality. In particular, we identify example cases where all single models and all fusion models do not give the correct prediction. Experimental replication of these syntheses reveals that these compositions are sensitive to modest temperature variations across the different locations of the heating element that can hinder or enhance the crystallization process. In summary, we demonstrate that model fusion using CFA can not only identify a previously unconsidered influence on reaction outcome but also be used as a form of quality control for high-throughput experimentation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Assessing methods in fusion and fitting for time series construction in remote sensing-based earth observations

This study evaluates the comparative performance of spatiotemporal fusion and time-series fitting methods for constructing high-spatiotemporal-resolution remote sensing time-series data. Due to in-class similarity of fusion methods and fitting methods, we employ the Fit-FC (Fitting, spatial Filtering, and residual Compensation) model as a representative fusion method and the linear harmonic fitting model as a representative fitting method. Both Fit-FC and the linear harmonic fitting are widely used for high-spatiotemporal-resolution time-series data construction, and we modify the original Fit-FC model to enable automatic time-series fusion. To ensure data representativeness, we use 3 years (2019–2021) of Harmonized Landsat and Sentinel-2 surface reflectance datasets and Terra MCD43A4 products. Eight experimental regions are selected worldwide to guarantee generalization of the comparative performance between fusion and fitting methods, covering diverse land-use types (cropland, developed land, forest, and grassland) and varying climatological conditions. Time-series of NDVI and surface reflectance are analyzed under both actual observations and simulated data-missing scenarios. The constructed time-series data reveals that (1) the modified Fit-FC and linear harmonic fitting model achieve excellent performance in constructing high-resolution time-series images; (2) the fusion method outperforms the fitting method in constructing time-series of NDVI and surface reflectance images in cropland-, forest-, and grassland-dominated regions; (3) both methods achieve comparable performance in developed-dominated regions; (4) the fusion method is more robust to missing data, and better captures abrupt phenological transitions under conditions of continuous missing data; (5) the fitting method is computationally more efficient, making it suitable for large-scale time-series image reconstruction. This study provides valuable insights for selecting optimal strategies to generate high-resolution time-series images across diverse application scenarios and lays a foundation for extensions to other vegetation indices or land surface variables.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

An ECT/ERT dual-modality sensor for oil-water two-phase flow measurement

This paper presents a new sensor for ECT/ERT dual-modality system which can simultaneously obtain the permittivity and conductivity of the materials in the pipeline. Quasi-static electromagnetic fields are produced by the inner electrodes array sensor of electrical capacitance tomography (ECT) system. The results of simulation show that the data of permittivity and conductivity can be simultaneously obtained from the same measurement electrode and the fusion of two kinds of data may improve the quality of the reconstructed images. For uniform oil-water mixtures, the performance of designed dual-modality sensor for measuring the various oil fractions has been tested on representative data and the results of experiments show that the designed sensor broadens the measurement range compared to single modality.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Avian Activity Classification Using Recurrent Networks to Fuse Videos with Metadata on Imbalanced Datasets

Activity classification plays a crucial role in various real-life scenarios involving both humans and animals. There is an increasing need for precise activity classification focused on avian-solar interactions, as the usage of solar energy facilities, such as photovoltaic array power stations, has been observed to impact bird species richness, behavior, and activity. However, there has been no work to develop an automated system to monitor and classify these avian-solar interactions. All current methods rely on human observers, which is time and human resources costly and subject to errors related to searcher efficiency. With the recent success of Deep Learning models in activity classification problems, this paper develops a recurrent neural network-based model to automatically classify six avian activities around solar energy facilities. Our proposed model integrates critical feature engineering metadata with video frame data, enabling improved learning and more accurate activity classification. Furthermore, we address the challenge of data imbalance during training and demonstrate the efficacy of our model in detecting and classifying different activities within video tracks. Additionally, we analyze the saliency/backpropagation map of the trained proposed model and validate its decision-making rationale.

Avian activity classification; bidirectional LSTM;↗
DOE OSTI2016

Detailed high-resolution three-dimensional simulations of OMEGA separated reactants inertial confinement fusion experiments

Here, we present results from the comparison of high-resolution three-dimensional (3D) simulations with data from the implosions of inertial confinement fusion capsules with separated reactants performed on the OMEGA laser facility. Each capsule, referred to as a “CD Mixcap,” is filled with tritium and has a polystyrene (CH) shell with a deuterated polystyrene (CD) layer whose burial depth is varied. In these implosions, fusion reactions between deuterium and tritium ions can occur only in the presence of atomic mix between the gas fill and shell material. The simulations feature accurate models for all known experimental asymmetries and do not employ any adjustable parameters to improve agreement with experimental data. Simulations are performed with the RAGE radiation-hydrodynamics code using an Implicit Large Eddy Simulation (ILES) strategy for the hydrodynamics. We obtain good agreement with the experimental data, including the DT/TT neutron yield ratios used to diagnose mix, for all burial depths of the deuterated shell layer. Additionally, simulations demonstrate good agreement with converged simulations employing explicit models for plasma diffusion and viscosity, suggesting that the implicit sub-grid model used in ILES is sufficient to model these processes in these experiments. In our simulations, mixing is driven by short-wavelength asymmetries and longer-wavelength features are responsible for developing flows that transport mixed material towards the center of the hot spot. Mix material transported by this process is responsible for most of the mix (DT) yield even for the capsule with a CD layer adjacent to the tritium fuel. Consistent with our previous results, mix does not play a significant role in TT neutron yield degradation; instead, this is dominated by the displacement of fuel from the center of the implosion due to the development of turbulent instabilities seeded by long-wavelength asymmetries. Through these processes, the long-wavelength asymmetries degrade TT yield more than the DT yield and thus bring DT/TT neutron yield ratios into agreement with experiment. Finally, we present a detailed comparison of the flows in 2D and 3D simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Iterative algorithms for processing experimental data

The need to solve linear and nonlinear integral equations arise, e.g., in recovering plasma parameters from the data of multichannel diagnostics. The paper presents an iterative method for solving integral equations with a singularity at the upper limit of integration. The method consists in constructing successive approximations and calculating the integral by quadrature formulas in each integration interval. An example of application of the iterative algorithm to numerically solve an integral equation similar to those arising in recovering the plasma density profile from reflectometry data is presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Sensor Fusion-Based Vehicle Detection and Tracking Using a Single Camera and Radar at a Traffic Intersection

Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to achieve cost-effective and efficient vehicle detection and tracking. Initially, vehicles are independently detected and classified using the camera and radar. Then, the constant-velocity model within a Kalman filter is employed to predict vehicle locations, while the Hungarian algorithm is used to associate these predictions with sensor measurements. Finally, vehicle tracking is accomplished by merging kinematic information from predictions and measurements through the Kalman filter. A case study conducted at an intersection demonstrates the effectiveness of the proposed sensor fusion method for traffic detection and tracking, including performance comparisons with individual sensors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Azimuthal Drive Asymmetry in Inertial Confinement Fusion Implosions on the National Ignition Facility

Data from nuclear diagnostics present correlated signatures of azimuthal implosion asymmetry in recent indirect-drive inertial confinement fusion (ICF) implosion campaigns performed at the National Ignition Facility (NIF). The mean hot-spot velocity, inferred from the Doppler shift of 14 MeV neutrons produced by deuterium-tritium (DT) fusion, is systematically directed toward one azimuthal half of the NIF target chamber, centered on φ ≈ 7 0 ° . Areal density ( ρ R ) asymmetry of the converged DT fuel, inferred from nuclear activation diagnostics, presents a minimum ρ R in the same direction as the hot-spot velocity and with Δ ρ R amplitude correlated with velocity magnitude. These two correlated observations, which are seen in all recent campaigns with cryogenic layers of DT fuel, are an understood signature of asymmetry in the fuel convergence, implying a systematic azimuthal drive asymmetry across a wide range of shot and target configurations. The direction of the implied radiation asymmetry is observed to cluster toward the hohlraum diagnostic windows. Furthermore, this low-mode asymmetry degrades hot-spot conditions at peak convergence and limits implosion performance and yield

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Proton Spectra from He 3 + T and He 3 + He 3 Fusion at Low Center-of-Mass Energy, with Potential Implications for Solar Fusion Cross Sections

Few-body nuclear physics often relies upon phenomenological models, with new efforts at the ab initio theory reported recently; both need high-quality benchmark data, particularly at low center-of-mass energies. We use high-energy-density plasmas to measure the proton spectra from 3 He + T and 3 He + 3 He fusion. The data disagree with R -matrix predictions constrained by neutron spectra from T + T fusion. Here, we present a new analysis of the 3 He + 3 He proton spectrum; these benchmarked spectral shapes should be used for interpreting low-resolution data, such as solar fusion cross-section measurements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Application of maximum likelihood methods to laser Thomson scattering measurements of low density plasmas

Laser Thomson scattering (LTS) is an established plasma diagnostic technique that has seen recent application to low density plasmas. It is difficult to perform LTS measurements when the scattered signal is weak as a result of low electron number density, poor optical access to the plasma, or both. Photon counting methods are often implemented in order to perform measurements in these low signal conditions. However, photon counting measurements performed with photo-multiplier tubes are time consuming and multi-photon arrivals are incorrectly recorded. In order to overcome these shortcomings a new data analysis method based on maximum likelihood estimation was developed. The key feature of this new data processing method is the inclusion of non-arrival events in determining the scattered Thomson signal. Maximum likelihood estimation and its application to Thomson scattering at low signal levels is presented and application of the new processing method to LTS measurements performed in the plume of a 2-kW Hall-effect thruster is discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Fusion of Multiple Models for Improving Gross Primary Production Estimation With Eddy Covariance Data Based on Machine Learning

Abstract Terrestrial gross primary production (GPP) represents the magnitude of CO 2 uptake through vegetation photosynthesis, and is a key variable for carbon cycles between the biosphere and atmosphere. Light use efficiency (LUE) models have been widely used to estimate GPP for its physiological mechanisms and availability of data acquisition and implementation, yet each individual GPP model has exhibited large uncertainties due to input errors and model structure, and further studies of systematic validation, comparison, and fusion of those models with eddy covariance (EC) site data across diverse ecosystem types are still needed in order to further improve GPP estimation. We here compared and fused five GPP models (VPM, EC‐LUE, GOL‐PEM, CHJ, and C‐Fix) across eight ecosystems based on FLUXNET2015 data set using the ensemble methods of Bayesian Model Averaging (BMA), Support Vector Machine (SVM), and Random Forest (RF) separately. Our results showed that for individual models, EC‐LUE gave a better performance to capture interannual variability of GPP than other models, followed by VPM and GLO‐PEM, while CHJ and C‐Fix were more limited in their estimation performance. We found RF and SVM were superior to BMA on merging individual models at various plant functional types (PFTs) and at the scale of individual sites. On the basis of individual models, the fusion methods of BMA, SVM, and RF were examined by a five‐fold cross validation for each ecosystem type, and each method successfully improved the average accuracy of estimation by 8%, 18%, and 19%, respectively.

Environmental Sciences & Ecology↗
DOE OSTI2025

Deep Cyber-Physical Situational Awareness for Energy Systems: A Secure Foundation for Next-Generation Energy Management

This document provides the final report for the CYPRES project. The purpose is (1) to highlight and summarize its major accomplishments and (2) to provide guidance on how its outcomes have informed and can inform important additional research and technology transfer. The goal of CYPRES was the research, development, and demonstration of a security-oriented next generation cyber-physical EMS for electric power systems that detects malicious and abnormal events through the fusion of cyber and physical data. To achieve this, the CYPRES project team researched, developed, and built a prototype of the solution, referred to as the CYPRES EMS. The CYPRES EMS is a proof-of-concept cyber-physical platform that demonstrates the management of the energy system, communications, security, and cyber-physical grid modeling and analytics. As part of the capabilities of the CYPRES EMS, the team designed and developed a suite of power system applications for monitoring, risk analyses, detection, and control that are inherently cyberaware. At its core, the project aimed to research, develop, and demonstrate a security-oriented next-generation cyber-physical Energy Management System (EMS) capable of detecting malicious and abnormal events through the innovative fusion of cyber and physical data. This approach represents a fundamental shift from traditional EMS, reimagining how critical infrastructure can be protected through unified cyber-aware and physics-aware secure data flow pipelines. The project’s cornerstone deliverable, the CYPRES EMS, serves as a proof-of-concept cyber-physical platform that revolutionizes the management of energy systems, communications, security, and cyber-physical grid modeling and analytics. This prototype implements a comprehensive suite of power system applications for monitoring, risk analyses, detection, and control, all designed with inherent cyber awareness. The system’s architecture extends from end-devices in the field through to control center applications, establishing a secure and resilient control framework that addresses the challenges posed by diverse devices of unknown trustworthiness connecting to modern power systems. Through this innovative approach to deep cyber-physical situational awareness, the CYPRES project not only advances the state-of-the-art in energy infrastructure protection but also establishes a new paradigm for how EMS can be designed, deployed, and operated in an increasingly complex threat landscape. The findings and developments from this project provide crucial insights for stakeholders across the energy sector, offering a blueprint for enhancing the reliability and resilience of our nation’s critical energy infrastructure in the face of evolving cyber threats.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Study of runaway electrons using the conditional average sampling method in the Damavand tokamak

Some experiments for studying the runaway electron (RE) effects have been performed using the poloidal magnetic probes system installed around the plasma column in the Damavand tokamak. In these experiments, the so-called runaway-dominated discharges were considered in which the main part of the plasma current is carried by REs. The induced magnetic effects on the poloidal pickup coils signals are observed simultaneously with the Parail–Pogutse instability moments for REs and hard X-ray bursts. The output signals of all diagnostic systems enter the data acquisition system with 2 Msample/(s channel) sampling rate. The temporal evolution of the diagnostic signals is analyzed by the conditional average sampling (CAS) technique. The CASed profiles indicate RE collisions with the high-field-side plasma facing components at the instability moments. The investigation has been carried out for two discharge modes—low-toroidal-field (LTF) and high-toroidal-field (HTF) ones—related to both up and down limits of the toroidal magnetic field in the Damavand tokamak and their comparison has shown that the RE confinement is better in HTF discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1974

Crystallization behavior of anorthite

The growth rate of anorthite crystals from the melt is studied as a function of temperature with undercooling in the ranges 52-152 and 402-652 degrees C. The triclinic form is invariably observed as the crystallization product, growth is preferentially in the c direction, and the interface morphology is faceted. Significant growth rate anisotropy is indicated. The maximum growth rate of anorthite from the melt is higher than for anorthite-rich lunar compositions. Recent computer studies are combined with experimental data to estimate the heat of fusion of anorthite as 28000-45000 cal/mol; the corresponding range for entropy of fusion is (7.8-12)R (where R is the gas constant). The observations and kinetic data support Jackson's predictions concerning materials with large entropies of fusion and his suggestion that entropy of fusion is an important parameter for characterizing the crystal-liquid interface and the nature of the crystallization process.

Klein, L.↗
DOE OSTI2015

Midplane neutral density profiles in the National Spherical Torus Experiment

Atomic and molecular density data in the outer midplane of NSTX [Ono et al., Nucl. Fusion 40, 557 (2000)] are inferred from tangential camera data via a forward modeling procedure using the DEGAS 2 Monte Carlo neutral transport code. The observed Balmer-β light emission data from 17 shots during the 2010 NSTX campaign display no obvious trends with discharge parameters such as the divertor Balmer-α emission level or edge deuterium ion density. Simulations of 12 time slices in 7 of these discharges produce molecular densities near the vacuum vessel wall of 2–8 × 10{sup 17 }m{sup −3} and atomic densities ranging from 1 to 7 × 10{sup 16 }m{sup −3}; neither has a clear correlation with other parameters. Validation of the technique, begun in an earlier publication, is continued with an assessment of the sensitivity of the simulated camera image and neutral densities to uncertainties in the data input to the model. The simulated camera image is sensitive to the plasma profiles and virtually nothing else. The neutral densities at the vessel wall depend most strongly on the spatial distribution of the source; simulations with a localized neutral source yield densities within a factor of two of the baseline, uniform source, case. The uncertainties in the neutral densities associated with other model inputs and assumptions are ≤50%.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗