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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Pioneer 10 and Voyager Observations of the Interstellar Medium in Scattered Emission of the He 584 a and H Lya 1216 a Lines

The combination of Pioneer photometric and Voyager spectrometric observations of EUV interstellar-interplanetary emissions in the region beyond 5 A was applied to a determination of atomic hydrogen and helium densities. These density estimates obtained from direct measurement of scattered radiation depend on absolute calibration of the instruments in the same way as other earlier determinations based on the same method. However, the spacecraft data were combined with daily ll sun averages of the H Lyman 1216 A line obtained by the Solar Mesospheric Explorer satellite to obtain a measure of atomic hydrogen density independent of instrument absolute calibration. The method depends on observations of long and short term temporal variability of the solar line over a one year period, and the fact that the ISM is optically thick. The density estimates from preliminary work on these observations are H = 0.12/cu cm and H = .016/cu cm, giving a density ratio close to the cosmic abundance value in contrast to some earlier results indicating a depletion of atomic hydrogen. Estimates were obtained of galactic background emissions in the signals of both spacecraft.

Shemansky, D. E.↗
NASA NTRSDate not supplied

Methods for Analytic Reflected Light Flux modelling in Observing Human Made Space Vehicles

During the Artemis I mission, the Orion Optical Navigation (OpNav) camera was used in a flight test to capture images of the Interim Cryogenic Propulsion Stage (ICPS) at long range. Novel methods in reflectance modelling were developed to estimate the reflected light flux as a function of range to inform the camera settings used to obtain the images. Postflight analysis was performed with the obtained images and telemetry. Techniques were developed using specialized star identification and attitude determination software to localize ICPS and positively identify it. The images were then used to verify the preflight reflected light flux as a function of range prediction and provide insight into the vehicle’s attitude dynamics.

Reflectance↗
NASA NTRS1984

Pioneer 10 and Voyager Observations of the Interstellar Medium in Scattered Emission of the H 584 A and H Lya 1216 A Lines

The combination of Pioneer photometric and Voyager spectrometric observations of EUV interstellar-interplanetary emissions in the region beyond 5 AU have been applied to a determination of atomic hydrogen and helium densities. These density estimates obtained from direct measurement of scattered radiation depend on absolute calibration of the instruments, in the same way as other earlier determinations based on the same method. However. we have combined the spacecraft data with daily full sun averages of the H Lya 1216 A line obtained by the Solar Mesospheric Explorer (SME) satellite, to obtain a measure of atomic hydrogen density independent of instrument absolute calibration. The method depends on observations of long and short term temporal variability of the solar line over a 1 year period, and the fact that the ISM is optically thick. The density estimates from preliminary work on these observations are (H) = 0.12 cm(sup 2) and (He) = .016 cm(sup 2), giving a density ratio close to the cosmic abundance value, in contrast to some earlier results indicating a depletion of atomic hydrogen. We have obtained estimates of galactic background emissions in the signals of both spacecraft.

Shemansky, D. E.↗
DOE OSTI2023

Revealing the Statistics of Extreme Events Hidden in Short Weather Forecast Data

Extreme weather events have significant consequences, dominating the impact of climate on society. While high-resolution weather models can forecast many types of extreme events on synoptic timescales, long-term climatological risk assessment is an altogether different problem. A once-in-a-century event takes, on average, 100 years of simulation time to appear just once, far beyond the typical integration length of a weather forecast model. Therefore, this task is left to cheaper, but less accurate, low-resolution or statistical models. But there is untapped potential in weather model output: despite being short in duration, weather forecast ensembles are produced multiple times a week. Integrations are launched with independent perturbations, causing them to spread apart over time and broadly sample phase space. Collectively, these integrations add up to thousands of years of data. We establish methods to extract climatological information from these short weather simulations. Using ensemble hindcasts by the European Center for Medium-range Weather Forecasting archived in the subseasonal-to-seasonal (S2S) database, we characterize sudden stratospheric warming (SSW) events with multi-centennial return times. Consistent results are found between alternative methods, including basic counting strategies and Markov state modeling. By carefully combining trajectories together, we obtain estimates of SSW frequencies and their seasonal distributions that are consistent with reanalysis-derived estimates for moderately rare events, but with much tighter uncertainty bounds, and which can be extended to events of unprecedented severity that have not yet been observed historically. These methods hold potential for assessing extreme events throughout the climate system, beyond this example of stratospheric extremes.

58 GEOSCIENCES↗
DOE OSTI2019

Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids

We present a physics-informed Gaussian Process Regression (GPR) model to predict the phase angle, angular speed, and wind mechanical power from a limited number of measurements. In the traditional data-driven GPR method, the form of the Gaussian Process covariance matrix is assumed and its parameters are found from measurements. In the physics-informed GPR, we treat unknown variables (including wind speed and mechanical power) as a random process and compute the covariance matrix from the resulting stochastic power grid equations. We demonstrate that the physics-informed GPR method is significantly more accurate than the standard data-driven one for immediate forecasting of generators' angular velocity and phase angle. We also show that the physics-informed GPR provides accurate predictions of the unobserved wind mechanical power, phase angle, or angular velocity when measurements from only one of these variables are available. The immediate forecast of observed variables and predictions of unobserved variables can be used for effectively managing power grids (electricity market clearing, regulation actions) and early detection of abnormal behavior and faults. The physics-based GPR forecast time horizon depends on the combination of input (wind power, load, etc.) correlation time and characteristic (relaxation) time of the power grid and can be extended to short and medium-range times.

machine learning, power grid, forecasting, Gaussia↗
DOE OSTI2014

Development of a testing method for asbestos fibers in treated materials of asbestos containing wastes by transmission electron microscopy

Highlights: • A high sensitive and selective testing method for asbestos in treated materials of asbestos containing wastes was developed. • Asbestos can be determined at a limits are a few million fibers per gram and a few μg g{sup −1}. • High temperature melting treatment samples were determined by this method. Asbestos fiber concentration were below the quantitation limit in all samples, and total fiber concentrations were determined as 47–170 × 10{sup 6} g{sup −1}. - Abstract: Appropriate treatment of asbestos-containing wastes is a significant problem. In Japan, the inertization of asbestos-containing wastes based on new treatment processes approved by the Minister of the Environment is promoted. A highly sensitive method for testing asbestos fibers in inertized materials is required so that these processes can be approved. We developed a method in which fibers from milled treated materials are extracted in water by shaking, and are counted and identified by transmission electron microscopy. Evaluation of this method by using asbestos standards and simulated slag samples confirmed that the quantitation limits are a few million fibers per gram and a few μg/g in a sample of 50 mg per filter. We used this method to assay asbestos fibers in slag samples produced by high-temperature melting of asbestos-containing wastes. Fiber concentrations were below the quantitation limit in all samples, and total fiber concentrations were determined as 47–170 × 10{sup −6} f/g. Because the evaluation of treated materials by TEM is difficult owing to the limited amount of sample observable, this testing method should be used in conjunction with bulk analytical methods for sure evaluation of treated materials.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Dislocation-pipe diffusion in nitride superlattices observed in direct atomic resolution

Device failure from diffusion short circuits in microelectronic components occurs via thermally induced migration of atoms along high-diffusivity paths: dislocations, grain boundaries, and free surfaces. Even well-annealed single-grain metallic films contain dislocation densities of about 10 14 m -2 ; hence dislocation-pipe diffusion (DPD) becomes a major contribution at working temperatures. While its theoretical concept was established already in the 1950s and its contribution is commonly measured using indirect tracer, spectroscopy, or electrical methods, no direct observation of DPD at the atomic level has been reported. We present atomically-resolved electron microscopy images of the onset and progression of diffusion along threading dislocations in sequentially annealed nitride metal/semiconductor superlattices, and show that this type of diffusion can be independent of concentration gradients in the system but governed by the reduction of strain fields in the lattice.

42 ENGINEERING↗
DOE OSTI2019

Implementation of a Particle Accelerator Beam Dynamics Code on Multi-Node GPUs

Particle accelerators play an important role in a wide range of scientific discoveries and industrial applications. The self-consistent multi-particle simulation based on the particle-in-cell (PIC) method has been used to study charged particle beam dynamics inside those accelerators. However, the PIC simulation is time-consuming and needs to use modern parallel computers for high-resolution applications. In this paper, we implemented a parallel beam dynamics PIC code on multi-node hybrid architecture computers with multiple Graphics Processing Units (GPUs). We used two methods to parallelize the PIC code on multiple GPUs and observed that the replication method is a better choice for moderate problem size and current computer hardware while the domain decomposition method might be a better choice for large problem size and more advanced computer hardware that allows direct communications among multiple GPUs. Using the multi-node hybrid architectures at Oak Ridge Leadership Computing Facility (OLCF), the optimized GPU PIC code achieves a reasonable parallel performance and scales up to 64 GPUs with 16 million particles.

43 PARTICLE ACCELERATORS↗
NASA NTRS1998

Accelerometer Placement for the International Space Station Node Modal Test

Accelerometer location analysis for the modal survey test of the International Space Station Node is described. Three different approaches were utilized: (1) Guyan reduction; (2) Iterative Guyan reduction; and (3) The average driving point residue (ADPR) method. Both Guyan approaches worked well, but poor results were observed for the ADPR method. Although the iterative Guyan approach appears to provide the best set of sensor locations, it is intensive computationally, becoming impractical for large initial location sets. While this is computer dependent, it appears that initial sets larger than about 1500 degrees of freedom are impractical for the iterative technique.

Tinker, Michael L.↗
NASA NTRS2001

Statistical Short-Range Forecast Guidance for Cloud Ceilings Over the Shuttle Landing Facility

This report describes the results of the AMU's Short-Range Statistical Forecasting task. The cloud ceiling forecast over the Shuttle Landing Facility (SLF) is a critical element in determining whether a Shuttle should land. Spaceflight Meteorology Group (SMG) forecasters find that ceilings at the SLF are challenging to forecast. The AMU was tasked to develop ceiling forecast equations to minimize the challenge. Studies in the literature that showed success in improving short-term forecasts of ceiling provided the basis for the AMU task. A 20-year record of cool-season hourly surface observations from stations in east-central Florida was used for the equation development. Two methods were used: an observations-based (OBS) method that incorporated data from all stations, and a persistence climatology (PCL) method used as the benchmark. Equations were developed for 1-, 2-, and 3-hour lead times at each hour of the day. A comparison between the two methods indicated that the OBS equations performed well and produced an improvement over the PCL equations. Therefore, the conclusion of the AMU study is that OBS equations produced more accurate forecasts than the PCL equations, and can be used in operations. They provide another tool with which to make the ceiling forecasts that are critical to safe Shuttle landings at KSC.

Lambert, Winifred C.↗
NASA NTRS2006

CO Signatures in Subtropical Convective Clouds and Anvils during CRYSTAL-FACE: An Analysis of Convective Transport and Entrainment using Observations and a Cloud-Resolving Model

Convective systems are an important mechanism in the transport of boundary layer air into the upper troposphere. The Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment (CRYSTAL-FACE) campaign, in July 2002, was developed as a comprehensive atmospheric mission to improve knowledge of subtropical cirrus systems and their roles in regional and global climate. In situ measurements of carbon monoxide (CO), water vapor (H2Ov), and total water (H2Ot) aboard NASA's WB-57F aircraft and CO aboard the U.S. Navy's Twin Otter aircraft were obtained to study the role of convective transport. Three flights sampled convective outflow on 11, 16 and 29 July found varying degrees of CO enhancement relative to the free troposphere. A cloud-resolving model used the in situ observations and meteorological fields to study these three systems. Several methods of filtering the observations were devised here using ice water content, relative humidity with respect to ice, and particle number concentration as a means to statistically sample the model results to represent the flight tracks. A weighted histogram based on ice water content observations was then used to sample the simulations for the three flights. In addition, because the observations occurred in the convective outflow cirrus and not in the storm cores, the model was used to estimate the maximum CO within the convective systems. In general, anvil-level air parcels contained an estimated 20-40% boundary layer air in the analyzed storms.

Lopez, Jimena P.↗
NASA NTRS2006

CO Signatures in Subtropical Convective Clouds and Anvils During CRYSTAL-FACE: An Analysis of Convective Transport and Entertainment Using Observations and a Cloud-Resolving Model

Convective systems are an important mechanism in the transport of boundary layer air into the upper troposphere. The Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment (CRYSTAL-FACE) campaign, in July 2002, was developed as a comprehensive atmospheric mission to improve knowledge of subtropical cirrus systems and their roles in regional and global climate. In situ measurements of carbon monoxide (CO), water vapor (H20v), and total water (H20t) aboard NASA's . WB-57F aircraft and CO aboard the U.S. Navy's Twin Otter aircraft were obtained to study the role of convective transport. Three flights sampled convective outflow on 11, 16 and 29 July found varying degrees of CO enhancement relative to the fiee troposphere. A cloud-resolving model used the in situ observations and meteorological fields to study these three systems. Several methods of filtering the observations were devised here using ice water content, relative humidity with respect to ice, and particle number concentration as a means to statistically sample the model results to represent the flight tracks. A weighted histogram based on ice water content observations was then used to sample the simulations for the three flights. In addition, because the observations occurred in the convective outflow cirrus and not in the storm cores, the model was used to estimate the maximum CO within the convective systems. In general, anvil-level air parcels contained an estimated 20-40% boundary layer air in the analyzed storms.

Lopez, Jimena P.↗
DOE OSTI2023

Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability

Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data analysis plays a crucial role in scientific discovery, especially in the current AI era, by enabling Responsible AI development, FAIR, Reproducibility, and User Steering. However, the heterogeneous nature of science poses challenges such as dealing with multiple supporting tools, cross-facility environments, and efficient HPC execution. Building on data observability, adapter system design, and provenance, we propose MIDA: an approach for lightweight runtime Multi-workflow Integrated Data Analysis. MIDA defines data observability strategies and adaptability methods for various parallel systems and machine learning tools. With observability, it intercepts the dataflows in the background without requiring instrumentation while integrating domain, provenance, and telemetry data at runtime into a unified database ready for user steering queries. We conduct experiments showing end-to-end multi-workflow analysis integrating data from Dask and MLFlow in a real distributed deep learning use case for materials science that runs on multiple environments with up to 276 GPUs in parallel. We show near-zero overhead running up to 100,000 tasks on 1,680 CPU cores on the Summit supercomputer.

Santos Souza, Renan↗
DOE OSTI2019

Deep Transfer Learning With Field-Based Measurements for Large Area Classification

Classification of Electro-Optical (EO) datasets using Deep Neural Networks (DNNs) has lead to high-performance supervised learning algorithms. However, DNNs require a large amount of labeled data for training which often have limited availability in ecological studies. Up-to-date classification of vegetation in sensitive Arctic ecosystems continue to be a challenge. In an ecosystem undergoing rapid change, capturing the dynamics of vegetation requires the existing maps of vegetation (such as Alaska Existing Vegetation Type (AKEVT), circa 2000) to be updated based on frequent field based observation of vegetation. A method is needed to transfer the knowledge gained from field-collected observations to a larger area using DNNs. Transfer learning is a machine learning technique where a model trained on one task is re-purposed on another similar task. This paper seeks to train DNNs using field-collected observations and apply transfer learning to apply the knowledge gained to update existing vegetation map at larger scale. We test two DNN methods, (1) a deep multilayer perceptron (MLP) model and (2) siamese MLP network that uses a structure to rank similarity between inputs and can be used for training datasets with few samples and show good performance with limited datasets (e.g. few-shot learning). The results show ∼90% accuracy (using the field observations for evaluation) when transfer learning is applied to a siamese network, compared to ∼45% accurate when a MLP is trained on the AKEVT and evaluated on the field observations. The approach show promise for improving and update the existing vegetation maps over large areas using limited field-based observations.

Langford, Zach↗
DOE OSTI2024

Indirect Liftoff Mechanism for High‐Throughput, Single‐Source Laser Scribing for Perovskite Solar Modules

A high‐throughput, single‐source laser scribing method exploiting a transparent conducting oxide (TCO) indirect liftoff mechanism is developed to produce serially interconnected perovskite solar modules. The TCO‐based indirect liftoff mechanism relies solely on laser absorption in the front transparent electrode material reducing thermal damage to the overlying layers and allowing for fast scribing speeds with low‐cost μs‐pulse duration fiber laser systems. Minimal resistive power losses are observed with the method compared to conventional ablative laser scribes, maintaining the power conversion efficiencies of small‐area devices (≈0.2 cm 2 ) across significantly larger deposition areas (≈1 cm 2 ). Finally, demonstrating > 3 m s −1 processing speeds, TCO‐based liftoff provides the highest throughput laser scribing method for thin‐film photovoltaic devices produced on glass/TCO substrates, capable of processing large‐area perovskite solar modules at a manufacturing scale.

14 SOLAR ENERGY↗
DOE OSTI2013

Correlation between human observer performance and model observer performance in differential phase contrast CT

Purpose: With the recently expanding interest and developments in x-ray differential phase contrast CT (DPC-CT), the evaluation of its task-specific detection performance and comparison with the corresponding absorption CT under a given radiation dose constraint become increasingly important. Mathematical model observers are often used to quantify the performance of imaging systems, but their correlations with actual human observers need to be confirmed for each new imaging method. This work is an investigation of the effects of stochastic DPC-CT noise on the correlation of detection performance between model and human observers with signal-known-exactly (SKE) detection tasks.Methods: The detectabilities of different objects (five disks with different diameters and two breast lesion masses) embedded in an experimental DPC-CT noise background were assessed using both model and human observers. The detectability of the disk and lesion signals was then measured using five types of model observers including the prewhitening ideal observer, the nonprewhitening (NPW) observer, the nonprewhitening observer with eye filter and internal noise (NPWEi), the prewhitening observer with eye filter and internal noise (PWEi), and the channelized Hotelling observer (CHO). The same objects were also evaluated by four human observers using the two-alternative forced choice method. The results from the model observer experiment were quantitatively compared to the human observer results to assess the correlation between the two techniques.Results: The contrast-to-detail (CD) curve generated by the human observers for the disk-detection experiments shows that the required contrast to detect a disk is inversely proportional to the square root of the disk size. Based on the CD curves, the ideal and NPW observers tend to systematically overestimate the performance of the human observers. The NPWEi and PWEi observers did not predict human performance well either, as the slopes of their CD curves tended to be steeper. The CHO generated the best quantitative agreement with human observers with its CD curve overlapping with that of human observer. Statistical equivalence between CHO and humans can be claimed within 11% of the human observer results, including both the disk and lesion detection experiments.Conclusions: The model observer method can be used to accurately represent human observer performance with the stochastic DPC-CT noise for SKE tasks with sizes ranging from 8 to 128 pixels. The incorporation of the anatomical noise remains to be studied.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Mesoscale Cellular Convection Detection and Classification Using Convolutional Neural Networks: Insights From Long-Term Observations at ARM Eastern North Atlantic Site

Marine boundary layer clouds are crucial in Earth's climate system. They frequently manifest as closed or open cell mesoscale cellular convection (MCC). MCC clouds are challenging to represent accurately in current climate models, highlighting the need for detailed observational data sets and in-depth analyses. This study utilizes over 8 years of observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility Eastern North Atlantic (ENA) site at Graciosa Island, Azores, to investigate these clouds. We first apply a convolutional neural network with a U-Net architecture to classify open and closed cells, marking the first application of such an approach for automatically detecting MCC patterns from ground-based radar measurements. This method addresses some observational gaps in satellite data related to low temporal resolution, nighttime challenges, and limited vertical structure capture. The analysis of the MCC cases shows clear differences between closed and open MCCs: Closed MCC clouds are characterized by lower cloud tops and bases, shallower cloud geometrical depth, weaker horizontal wind speeds, stronger atmospheric stability, and a more homogeneous liquid water path than open MCCs. Finally, we demonstrate two potential applications of our radar-based MCC classifications: (a) facilitating the investigation of aerosol-cloud interactions and (b) exploring meteorological factors along with MCC's evolution by integrating satellite imagery and back-trajectory analysis. The identified MCC cases offer a valuable resource for the scientific community to study MCC processes further and improve climate model accuracy.

54 ENVIRONMENTAL SCIENCES↗