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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Continuous polyamorphic transition in high-entropy metallic glass

Polyamorphic transition (PT) is a compelling and pivotal physical phenomenon in the field of glass and materials science. Understanding this transition is of scientific and technological significance, as it offers an important pathway for effectively tuning the structure and property of glasses. In contrast to the PT observed in conventional metallic glasses (MGs), which typically exhibit a pronounced first-order nature, herein we report a continuous PT (CPT) without first-order characteristics in high-entropy MGs (HEMGs) upon heating. This CPT behavior is featured by the continuous structural evolution at the atomic level and an increasing chemical concentration gradient with temperature, but no abrupt reduction in volume and energy. The continuous transformation is associated with the absence of local favorable structures and chemical heterogeneity caused by the high configurational entropy, which limits the distance and frequency of atomic diffusion. As a result of the CPT, numerous glass states can be generated, which provides an opportunity to understand the nature, atomic packing, formability, and properties of MGs. Moreover, this discovery highlights the implication of configurational entropy in exploring polyamorphic glasses with an identical composition but highly tunable structures and properties.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Optical conductivity of partially oxidized graphene from first principles

We investigate the geometry, electronic structure, and optical properties of partially oxidized graphene using density functional theory. Our calculations show that oxygen atoms are chemisorbed on graphene plane and distort carbon atoms vertically, with almost no change in the in-plane structure. The ground state configurations for different oxygen coverages ranging from 2% to 50% (O/C ratio) are calculated and show the strong tendency of oxygen adatoms to aggregate and form discrete islands on graphene plane. It is found that the opened band gap due to oxygen functionalization depends on the oxygen density and the adsorption configuration. The gap is not significant for oxygen densities lower than 8%. The optical conductivities are calculated in the infrared, visible, and ultraviolet regions and show different characteristic features depending on the degree of oxidation. These results imply that optical measurement techniques can be employed to monitor oxidation (or reduction) process as contact-free methods.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Solitary and shock structures in a strongly coupled cryogenic quantum plasma

The quantum ion-acoustic (QIA) solitary and shock structures formed in a strongly coupled cryogenic quantum plasma (containing strongly coupled positively charged inertial cold ions and Fermi electrons as well as positrons) have been theoretically investigated. The generalized quantum hydrodynamic model and the reductive perturbation method have been employed to derive the Korteweg-de Vries (K-dV) and Burgers equations. The basic features of the QIA solitary and shock structures are identified by analyzing the stationary solitary and shock wave solutions of the K-dV and Burgers equations. It is found that the basic characteristics (e.g., phase speed, amplitude, and width) of the QIA solitary and shock structures are significantly modified by the effects of the Fermi pressures of electrons and positrons, the ratio of Fermi temperature of positrons to that of electrons, the ratio of effective ion temperature to electron Fermi temperature, etc. It is also observed that the effect of strong correlation among extremely cold ions acts as a source of dissipation, and is responsible for the formation of the QIA shock structures. The results of this theoretical investigation should be useful for understanding the nonlinear features of the localized electrostatic disturbances in laboratory electron-positron-ion plasmas (viz., super-intense laser-dense matter experiments)

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Low Space Harmonic Content Windings (LSHWs) Applied to Improve the Pareto Front in Design Optimization of Electric Machines

In design optimization of high performance electric machines where the power density is increasingly a key metric, it is often desirable to maximize the average torque output within a given volume. However, if a smooth output is also required, the torque ripple may become a hurdle as it tends to increase with the average torque for conventional windings. A direct benefit of low space harmonic content windings (LSHWs), featuring the suppression or elimination of targeted non-working orders in the winding magnetomotive force (MMF), is the torque ripple reduction. This paper provides a detailed analysis of torque ripple pairs and the space-time harmonic order mapping, with concise results that applies to both distributed windings and fractionalslot concentrated windings (FSCWs) of any slot/pole combination. Metamodel-based optimizations are performed to compare two LSHWs with a conventional winding. Improved Pareto fronts are validated, which confirms higher torque producing capabilities at various torque ripple thresholds for LSHWs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Efficient Clustering of Software Vulnerabilities using Self Organizing Map (SOM)

The common vulnerabilities and exposures (CVE) database was created with a mission to ``identify, define, and catalog publicly disclosed cybersecurity vulnerabilities''. This rich body of information can be used to enable rapid and efficient response to secure and defend cyber operations and protect critical cyber infrastructure. The main goal of this paper is to develop a visual analytics tool to enable deep analysis of CVEs using unsupervised clustering techniques. We enhance our analysis by first mapping CVEs to hierarchical-classes in Common Weakness Enumeration (CWE) using information in the National Vulnerability Database (NVD). Both the mapping and the numerical representation of CVEs are enabled by V2W-BERT, which uses natural language processing of the extensive information in NVD to generate a large tabular database of 137,226 CVE entries from 1999 to 2020, where each CVE is represented by a vector of 768 numerical features. The vectorized data is processed by Self-Organizing Maps (SOM), which is an unsupervised machine learning technique for dimensionality reduction, visual representation and clustering. Using a Torus map of 6417 units, we achieve ~10-fold data compression of ~140k CVEs using SOM. The trained map is further clustered using standard K-means clustering into 138 clusters of CVEs. We conducted a brief investigation of the rich mapping of CVEs to best-matching-units to K-means clusters, as well as CVEs to CWEs. For example, this novel mapping provided insight into the role of CWE-59 and CWE-264 in several CVEs that is otherwise hard to explore in the original data. We conclude that our this novel approach will not only enable deep analysis of the complex relationships between CVEs and CWEs, but also a mechanism to quickly respond to and design mitigation actions for rapidly evolving vulnerabilities that have not been mapped to existing CWEs.

Panchal, Khyati↗
DOE OSTI2016

SU-F-R-28: Correction of FCh-PET Bladder Uptake Using Virtual Sinograms and Investigation of Its Impact On the Quantification of Prostate Textural Characteristics

Purpose: Textural biomarkers as a tool for quantifying intratumoral heterogeneity hold great promise for diagnosis and early assessment of treatment response in prostate cancer. However, spill-in counts from the bladder uptake are suspected to have an impact on the textural measurements of the prostate volume. This work proposes a correction method for the FCh-PET bladder uptake and investigates its impact on intraprostatic textural properties. Methods: Two patients with PC received pre-treatment dynamic FCh-PET scans reconstructed at four time points (interval: 2 min), for which prostate and bladder contours were obtained. Projection bins affected by bladder uptake were determined by forward-projection. For each time point and axial position, virtual sinograms were obtained and affected bins replaced by a weighted combination of original values and values interpolated using cubic spline from non-affected bins of the current and adjacent projection angles. The process was optimized using a genetic algorithm in terms of minimization of the root-mean-square error (RMSE) within the bladder between the corrected dynamic time point volume and a reference initial uptake volume. Finally, the impact of the bladder uptake correction on the prostate region was investigated using two standard SUV metrics (1) and three texture metrics (2): 1) SUVmax, SUVmean; 2) Contrast, Homogeneity, Coarseness. Results: Without bladder uptake correction, SUVmax and SUVmean were on average overestimated in the prostate by 0%, 0%, 33.2%, 51.2%, and 3.6%, 6.0%, 2.9%, 3.2%, for each time point respectively. Contrast varied by −9.1%, −6.7%, +40.4%, +107.7%, and Homogeneity and Coarseness by +4.5%, +1.8%, −8.8%, −14.8% and +1.0%, +0.5%, −9.5%, +0.9%. Conclusion: We proposed a method for FCh-PET bladder uptake correction and showed an impact on the quantification of the prostate signal. This method achieved a large reduction of intra-prostatic SUVmax while minimizing the impact on SUVmean. Further investigation is necessary to interpret changes in textural features. SL acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Characterization of an M-Cluster-Substituted Nitrogenase VFe Protein

ABSTRACT The Mo- and V-nitrogenases are two homologous members of the nitrogenase family that are distinguished mainly by the presence of different heterometals (Mo or V) at their respective cofactor sites (M- or V-cluster). However, the V-nitrogenase is ~600-fold more active than its Mo counterpart in reducing CO to hydrocarbons at ambient conditions. Here, we expressed an M-cluster-containing, hybrid V-nitrogenase in Azotobacter vinelandii and compared it to its native, V-cluster-containing counterpart in order to assess the impact of protein scaffold and cofactor species on the differential reactivities of Mo- and V-nitrogenases toward CO. Housed in the VFe protein component of V-nitrogenase, the M-cluster displayed electron paramagnetic resonance (EPR) features similar to those of the V-cluster and demonstrated an ~100-fold increase in hydrocarbon formation activity from CO reduction, suggesting a significant impact of protein environment on the overall CO-reducing activity of nitrogenase. On the other hand, the M-cluster was still ~6-fold less active than the V-cluster in the same protein scaffold, and it retained its inability to form detectable amounts of methane from CO reduction, illustrating a fine-tuning effect of the cofactor properties on this nitrogenase-catalyzed reaction. Together, these results provided important insights into the two major determinants for the enzymatic activity of CO reduction while establishing a useful framework for further elucidation of the essential catalytic elements for the CO reactivity of nitrogenase. IMPORTANCE This is the first report on the in vivo generation and in vitro characterization of an M-cluster-containing V-nitrogenase hybrid. The “normalization” of the protein scaffold to that of the V-nitrogenase permits a direct comparison between the cofactor species of the Mo- and V-nitrogenases (M- and V-clusters) in CO reduction, whereas the discrepancy between the protein scaffolds of the Mo- and V-nitrogenases (MoFe and VFe proteins) housing the same cofactor (M-cluster) allows for an effective assessment of the impact of the protein environment on the CO reactivity of nitrogenase. The results of this study provide a first look into the “weighted” contributions of protein environment and cofactor properties to the overall activity of CO reduction; more importantly, they establish a useful platform for further investigation of the structural elements attributing to the CO-reducing activity of nitrogenase.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Verification and validation of developed short-term forecasting models

Recent advancements in machine learning (ML) and artificial intelligence (AI) technologies provide an opportunity for leveraging data-driven algorithms to predict future nuclear power plant (NPP) operating conditions by using recorded plant process data. Successfully implementing these models can lead to cost-reducing, conditioned-based predictive maintenance through optimized maintenance schedules and a reduction of unnecessary maintenance activities. This report discusses the verification and validation of short-term forecasting processes (i.e., data cleaning, feature selection, model optimization, and forecasting) developed in previous reports. The verification and validation (V&V) process demonstrates the expected precision and accuracy when the ML model encounters new datasets from different systems. Shapley additive explanations were used as the primary means of feature selection across these different data set. Individual models were trained for each data set, then validated through a cross-validation procedure. In this report, two different ML models were tasked to predict variables from three different plant process data sets with varying prediction horizons. The results indicate that support vector regression (SVR) outperformed long short-term memory (LSTM) neural networks in regard to each data set and each prediction horizon in this study, but further tuning and optimization could improve long short-term memory results. However, each forecasting model showed reduced performance as the prediction horizon was extended from 1 hour to 1 day ahead. Research is ongoing to evaluate the optimal input variable space, which is based on a given set of process parameters, to further improve forecasting accuracy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Xcel Energy’s electric carbon emissions reduction trajectories in the context of 1.5°C and 2°C warming pathways

In 2015 the Paris Agreement established the goals of limiting global average warming to well below 2°C and pursuing efforts to limit warming to below 1.5°C. A large and growing number of scenarios have been developed by the climate research community that explore global energy and emissions pathways that would achieve those goals. We draw on the most recent database of such scenarios to update a previous analysis of Xcel Energy’s emissions reduction goals in light of evolving climate science. We assess the outlook for the role of the US electricity sector in current economy-wide and global emissions pathways and compare it to Xcel Energy’s near-term resource plans to 2030. We find that global scenarios that achieve the 1.5°C goal span a range of US/North America electricity sector emissions reductions by 2030 of about 65-85%. Xcel Energy’s emissions reductions to date have exceeded those of the US electricity sector as a whole, and its projected trajectory to 2030 under current approved resource plans falls within this range. Scenarios achieving the 2°C goal have a wider range of reductions (about 40-85%). In scenarios achieving either goal, electricity sector emissions fall faster than economy-wide emissions, a robust feature of mitigation scenarios, which typically rely on low carbon electricity to achieve climate targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1973

Classifying unresolved objects from simulated space data.

A multispectral scanner data set gathered at a flight altitude of 10,000 ft. over an agricultural area was modified to simulate the spatial resolution of the spacecraft scanners. Signatures were obtained for several major crops and their proportions were estimated over a large area. For each crop, a map was generated to show its approximate proportion in each resolution element, and hence its distribution over the area of interest. A statistical criterion was developed to identify data points that may not represent a mixture of the specified crops. This allows for great reduction in the effect of unknown or alien objects on the estimated proportions. This criterion can be used to locate special features, such as roads or farm houses. Preliminary analysis indicates a high level of consistency between estimated proportions and available ground truth. Large concentrations of major crops show up especially well on the maps.

Nalepka, R. F.↗
NASA NTRS2004

Variable Shadow Screens for Imaging Optical Devices

Variable shadow screens have been proposed for reducing the apparent brightnesses of very bright light sources relative to other sources within the fields of view of diverse imaging optical devices, including video and film cameras and optical devices for imaging directly into the human eye. In other words, variable shadow screens would increase the effective dynamic ranges of such devices. Traditionally, imaging sensors are protected against excessive brightness by use of dark filters and/or reduction of iris diameters. These traditional means do not increase dynamic range; they reduce the ability to view or image dimmer features of an image because they reduce the brightness of all parts of an image by the same factor. On the other hand, a variable shadow screen would darken only the excessively bright parts of an image. For example, dim objects in a field of view that included the setting Sun or bright headlights could be seen more readily in a picture taken through a variable shadow screen than in a picture of the same scene taken through a dark filter or a narrowed iris. The figure depicts one of many potential variations of the basic concept of the variable shadow screen. The shadow screen would be a normally transparent liquid-crystal matrix placed in front of a focal-plane array of photodetectors in a charge-coupled-device video camera. The shadow screen would be placed far enough from the focal plane so as not to disrupt the focal-plane image to an unacceptable degree, yet close enough so that the out-of-focus shadows cast by the screen would still be effective in darkening the brightest parts of the image. The image detected by the photodetector array itself would be used as feedback to drive the variable shadow screen: The video output of the camera would be processed by suitable analog and/or digital electronic circuitry to generate a negative partial version of the image to be impressed on the shadow screen. The parts of the shadow screen in front of those parts of the image with brightness below a specified threshold would be left transparent; the parts of the shadow screen in front of those parts of the image where the brightness exceeded the threshold would be darkened by an amount that would increase with the excess above the threshold.

Lu, Ed↗
NASA NTRS2015

RACORO Continental Boundary Layer Cloud Investigations: 1. Case Study Development and Ensemble Large-Scale Forcings

Observation-based modeling case studies of continental boundary layer clouds have been developed to study cloudy boundary layers, aerosol influences upon them, and their representation in cloud- and global-scale models. Three 60 h case study periods span the temporal evolution of cumulus, stratiform, and drizzling boundary layer cloud systems, representing mixed and transitional states rather than idealized or canonical cases. Based on in situ measurements from the Routine AAF (Atmospheric Radiation Measurement (ARM) Aerial Facility) CLOWD (Clouds with Low Optical Water Depth) Optical Radiative Observations (RACORO) field campaign and remote sensing observations, the cases are designed with a modular configuration to simplify use in large-eddy simulations (LES) and single-column models. Aircraft measurements of aerosol number size distribution are fit to lognormal functions for concise representation in models. Values of the aerosol hygroscopicity parameter, kappa, are derived from observations to be approximately 0.10, which are lower than the 0.3 typical over continents and suggestive of a large aerosol organic fraction. Ensemble large-scale forcing data sets are derived from the ARM variational analysis, European Centre for Medium-Range Weather Forecasts, and a multiscale data assimilation system. The forcings are assessed through comparison of measured bulk atmospheric and cloud properties to those computed in "trial" large-eddy simulations, where more efficient run times are enabled through modest reductions in grid resolution and domain size compared to the full-sized LES grid. Simulations capture many of the general features observed, but the state-of-the-art forcings were limited at representing details of cloud onset, and tight gradients and high-resolution transients of importance. Methods for improving the initial conditions and forcings are discussed. The cases developed are available to the general modeling community for studying continental boundary clouds.

Boundary layers↗
NASA NTRS2018

The Path Towards High-Contrast Imaging with the VLTI: The Hi-5 Project

The development of high-contrast capabilities has long been recognized as one of the top priorities for the VLTI (Very Large Telescope Interferometer). As of today, the VLTI routinely achieves contrasts of a few 103 in the near-infrared with PIONIER (Precision Integrated-Optics Near-infrared Imaging ExpeRiment) (H band) and GRAVITY (K band). Nulling interferometers in the northern hemisphere and non-redundant aperture masking experiments have, however, demonstrated that contrasts of at least a few 10 (sup -3) are within reach using specific beam combination and data acquisition techniques. In this paper, we explore the possibility to reach similar or higher contrasts on the VLTI. After reviewing the state-of-the-art in high-contrast infrared interferometry, we discuss key features that made the success of other high-contrast interferometric instruments (e.g., integrated optics, nulling, closure phase, and statistical data reduction) and address possible avenues to improve the contrast of the VLTI by at least one order of magnitude. In particular, we discuss the possibility to use integrated optics, proven in the near-infrared, in the thermal near-infrared (L and M bands, 3-5 microns), a sweet spot to image and characterize young extra-solar planetary systems. Finally, we address the science cases of a high-contrast VLTI imaging instrument and focus particularly on exoplanet science (young exoplanets, planet formation, and exozodiacal disks), stellar physics (fundamental parameters and multiplicity), and extragalactic astrophysics (active galactic nuclei and fundamental constants). Synergies and scientific preparation for other potential future instruments such as the Planet Formation Imager are also briefly discussed. This project is called Hi-5 for High-contrast Interferometry up to 5 microns.

PIONIER (H band)↗
NASA NTRSDate not supplied

Overview of the Acoustic Improvement Program for the 9- by 15-Foot Low Speed Wind Tunnel

This report gives an overview of a recent improvement project to reduce the background noise in the NASA Glenn Research Center 9-by 15-Foot Low Speed Wind Tunnel. Noise surveys of the original tunnel configuration indicated that flow surface noise in the test section and noise propagating from other tunnel regions were the primary background noise contributors. Several novel features were incorporated in the design of the upgraded tunnel, including a low noise flow surface and serpentine baffles for noise reduction, along with acoustically treated turning vanes and a new diffuser. Three companion papers present implementation details including the acoustic design and the aerodynamic quality/design CFD. The upgrades reduced background noise in the test section by about 10dBA over a wide range of flow speeds and frequencies. It is hoped this will be a useful summary to aid future users of the facility and wind tunnel engineers everywhere.

Wind Tunnel↗
NASA NTRS2022

Microstructure Segmentation with Deep Learning Encoders Pre-Trained on a Large Microscopy Dataset

This study examined the improvement of microscopy segmentation accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures, including VGG, Inception, and ResNet, were trained on over 100,000 labelled microscopy images from 54 classes. These pre-trained encoders were then embedded into multiple segmentation architectures including U-Net and DeepLabV3+ to evaluate segmentation performance on newly created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2 percent reduction in relative segmentation error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.

machine learning↗
DOE OSTI2016

Evaluating lossy data compression on climate simulation data within a large ensemble

High-resolution Earth system model simulations generate enormous data volumes, and retaining the data from these simulations often strains institutional storage resources. Further, these exceedingly large storage requirements negatively impact science objectives, for example, by forcing reductions in data output frequency, simulation length, or ensemble size. To lessen data volumes from the Community Earth System Model (CESM), we advocate the use of lossy data compression techniques. While lossy data compression does not exactly preserve the original data (as lossless compression does), lossy techniques have an advantage in terms of smaller storage requirements. To preserve the integrity of the scientific simulation data, the effects of lossy data compression on the original data should, at a minimum, not be statistically distinguishable from the natural variability of the climate system, and previous preliminary work with data from CESM has shown this goal to be attainable. However, to ultimately convince climate scientists that it is acceptable to use lossy data compression, we provide climate scientists with access to publicly available climate data that have undergone lossy data compression. In particular, we report on the results of a lossy data compression experiment with output from the CESM Large Ensemble (CESM-LE) Community Project, in which we challenge climate scientists to examine features of the data relevant to their interests, and attempt to identify which of the ensemble members have been compressed and reconstructed. We find that while detecting distinguishing features is certainly possible, the compression effects noticeable in these features are often unimportant or disappear in post-processing analyses. In addition, we perform several analyses that directly compare the original data to the reconstructed data to investigate the preservation, or lack thereof, of specific features critical to climate science. Overall, we conclude that applying lossy data compression to climate simulation data is both advantageous in terms of data reduction and generally acceptable in terms of effects on scientific results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multiresolution classification of turbulence features in image data through machine learning

During large-scale simulations, intermediate data products such as image databases have become popular due to their low relative storage cost and fast in-situ analysis. Serving as a form of data reduction, these image databases have become more acceptable to perform data analysis on. In this work, we present an image-space detection and classification system for extracting vortices at multiple scales through wavelet-based filtering. A custom image-space descriptor is used to encode a large variety of vortex-types and a machine learning system is trained for fast classification of vortex regions. By combining a radial-based histogram descriptor, a bag of visual words feature descriptor, and a support vector machine, our results show that we are able to detect and classify vortex features at various sizes at multiple scales. Once trained, our framework enables the fast extraction of vortices on new, unknown image datasets for flow analysis.

97 MATHEMATICS AND COMPUTING↗