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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

(abstract) Venus Gravity Data Reduction

The Magellan spacecraft has provided high resolution gravity data to its very end, October 13, 1994, when it was consumed by the Venusian atmosphere. After aerobraking in August of 1993 to attain a near circular orbit, excellent high latitude data were acquired which previously were very weak during the elliptical orbit coverage. There are 1500 orbits during the near circular orbit, supplying redundant coverage at different geometries over many features. This allowed the relaxation of apriori constraints, so true amplitudes are being extracted from the data. In this paper we present the results of a 75(sup th) degree and order field that incorporates all the old Pioneer Venus Orbiter data as well as all the Magellan data to September 1994. The new results reflect even higher correlation with topography, higher amplitude values for the highs and lows, and global results that have essentially very little apriori constraint on the solution parameters. We also correlate our new model with the earlier ones based on 60(sup th) and 40(sup th) degree and order presentations.

Magellan Venus gravity orbits topography surface f↗
DOE OSTI2021

Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data

The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer

Neural style transfer (NST) has evolved significantly in recent years. Yet, despite its rapid progress and advancement, existing NST methods either struggle to transfer aesthetic information from a style effectively or suffer from high computational costs and inefficiencies in feature disentanglement due to using pre-trained models. This work proposes a lightweight but effective model, AesFA---Aesthetic Feature-Aware NST. The primary idea is to decompose the image via its frequencies to better disentangle aesthetic styles from the reference image while training the entire model in an end-to-end manner to exclude pre-trained models at inference completely. Finally, to improve the network's ability to extract more distinct representations and further enhance the stylization quality, this work introduces a new aesthetic feature: contrastive loss. Extensive experiments and ablations show the approach not only outperforms recent NST methods in terms of stylization quality, but it also achieves faster inference. Codes are available at https://github.com/Sooyyoungg/AesFA.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Deep clustering of protein folding simulations

Background: We investigate the problem of clustering biomolecular simulations using deep learning techniques. Since biomolecular simulation datasets are inherently high dimensional, it is often necessary to build low dimensional representations that can be used to extract quantitative insights into the atomistic mechanisms that underlie complex biological processes. Results: We use a convolutional variational autoencoder (CVAE) to learn low dimensional, biophysically relevant latent features from long time-scale protein folding simulations in an unsupervised manner. We demonstrate our approach on three model protein folding systems, namely Fs-peptide (14 μs aggregate sampling), villin head piece (single trajectory of 125 μs) and β- β- α (BBA) protein (223 + 102 μs sampling across two independent trajectories). In these systems, we show that the CVAE latent features learned correspond to distinct conformational substates along the protein folding pathways. The CVAE model predicts, on average, nearly 89% of all contacts within the folding trajectories correctly, while being able to extract folded, unfolded and potentially misfolded states in an unsupervised manner. Further, the CVAE model can be used to learn latent features of protein folding that can be applied to other independent trajectories, making it particularly attractive for identifying intrinsic features that correspond to conformational substates that share similar structural features. Conclusions: Together, we demonstrate that the CVAE model can quantitatively describe complex biophysical processes such as protein folding.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Estimation and Visualization of Isosurface Uncertainty from Linear and High-Order Interpolation Methods

Isosurface visualization is fundamental for exploring and analyzing 3D volumetric data. Marching cubes (MC) algorithms with linear interpolation are commonly used for isosurface extraction and visualization. Although linear interpolation is easy to implement, it has limitations when the underlying data is complex and high-order, which is the case for most real-world data. Linear interpolation can output vertices at the wrong location. Its inability to deal with sharp features and features smaller than grid cells can lead to an incorrect isosurface with holes and broken pieces. Despite these limitations, isosurface visualizations typically do not include insight into the spatial location and the magnitude of these errors. We utilize high-order interpolation methods with MC algorithms and interactive visualization to highlight these uncertainties. Our visualization tool helps identify the regions of high interpolation errors. It also allows users to query local areas for details and compare the differences between isosurfaces from different interpolation methods. In addition, we employ high-order methods to identify and reconstruct possible features that linear methods cannot detect. We showcase how our visualization tool helps explore and understand the extracted isosurface errors through synthetic and real-world data.

Ouermi, Timbwaoga↗
DOE OSTI2018

Deciphering complex features in STM images of O adatoms on Ag(110)

Recently it was demonstrated that atomic oxygen can cause the extraction of substrate atoms off metal surfaces thus generating chemically different active sites. For Ag(110) this process occurs when O 2 is dosed at 175 K leading, at low coverage, to the formation of single Ag vacancies. Vacancy creation proceeds thereby via the formation of O-Ag-O complexes, which involve a local reconstruction of the surface and ignite the disruption of the Ag substrate. In this paper, we report on the details of such processes and on the isolated structures formed by the O adatoms in the limit of very low coverage. Specifically, we employ scanning tunneling microscopy and density functional theory to unravel the complex structures of O/Ag(110) which are transiently present under specific reaction conditions. A variety of features such as isolated gray dots, sombreros, shallow gray and white structures oriented along [001] and [1$\bar{1}$0], gray stripes, and lozenges were identified and assigned to O adatoms in different configurations. The zigzag chains interact strongly with the STM tip and are easily disrupted, giving rise to highly mobile, sombrero-shaped, isolated O adatoms also far away from the scanned area, i.e., from the current injection spot. Around 200 K, not only Ag vacancies, which are mobile with anisotropic migration, can merge together into rather complex features, but also the mobile Ag atoms are trapped by O adatoms, thus facilitating the formation of an oxygen-decorated Ag chain along [001] which ultimately induces the well-known added-row reconstruction.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2014

Method and Apparatus for Automated Isolation of Nucleic Acids from Small Cell Samples

RNA isolation is a ubiquitous need, driven by current emphasis on microarrays and miniaturization. With commercial systems requiring 100,000 to 1,000,000 cells for successful isolation, there is a growing need for a small-footprint, easy-to-use device that can harvest nucleic acids from much smaller cell samples (1,000 to 10,000 cells). The process of extraction of RNA from cell cultures is a complex, multi-step one, and requires timed, asynchronous operations with multiple reagents/buffers. An added complexity is the fragility of RNA (subject to degradation) and its reactivity to surface. A novel, microfluidics-based, integrated cartridge has been developed that can fully automate the complex process of RNA isolation (lyse, capture, and elute RNA) from small cell culture samples. On-cartridge cell lysis is achieved using either reagents or high-strength electric fields made possible by the miniaturized format. Traditionally, silica-based, porous-membrane formats have been used for RNA capture, requiring slow perfusion for effective capture. In this design, high efficiency capture/elution are achieved using a microsphere-based "microfluidized" format. Electrokinetic phenomena are harnessed to actively mix microspheres with the cell lysate and capture/elution buffer, providing important advantages in extraction efficiency, processing time, and operational flexibility. Successful RNA isolation was demonstrated using both suspension (HL-60) and adherent (BHK-21) cells. Novel features associated with this development are twofold. First, novel designs that execute needed processes with improved speed and efficiency were developed. These primarily encompass electric-field-driven lysis of cells. The configurations include electrode-containing constructs, or an "electrode-less" chip design, which is easy to fabricate and mitigates fouling at the electrode surface; and the "fluidized" extraction format based on electrokinetically assisted mixing and contacting of microbeads in a shape-optimized chamber. A secondary proprietary feature is in the particular layout integrating these components to perform the desired operation of RNA isolation. Apart from a novel functional capability, advantages of the innovation include reduced or eliminated use of toxic reagents, and operator-independent extraction of RNA.

Sundaram, Shivshankar↗
DOE OSTI2026

Collision Tracking in OpenMC: Methods and Applications in Neutron Noise, Neutron Imaging, Time-of-Flight, and Multiplicity Counting

We present the development and application of a collision tracking feature within the OpenMC Monte Carlo particle transport code, designed for diverse applications such as neutron spectroscopy, scatter camera system, neutron noise, and multiplicity counting simulations. This feature enables the tracking of individual particle collisions, with potential applications in nuclear nonproliferation, reactor physics, and nuclear security. Additionally, the feature holds potential for the calibration of neutron detectors, specifically in converting light output into energy deposited within the detectors. The implementation consists of a set of filters—such as reaction type, energy, cell, and material—that constrain the set of collisions that are tracked, extensions to the Python API to enable simple input specification, and support for writing either OpenMC’s native HDF5-based format or the Monte Carlo particle list format. This feature was added to the official OpenMC release in version 0.15.3. In this work, the feature will be applied to showcase scenarios such as time-of-flight simulations, scatter-camera imaging for neutron source localization, neutron-noise analysis to extract integral kinetic parameters such as the prompt decay constant α, and multiplicity counting to estimate the mass of special nuclear materials. Ultimately, this feature aims to expand the application scope of open-source Monte Carlo particle transport codes such as OpenMC.

Monte Carlo code↗
NASA NTRS1994

Laboratory Comparisons of Organic Materials to Those Observed in the Interstellar Medium

Spectra of objects which lie along several lines of sight through the diffuse interstellar medium (ISM) all contain an absorption feature near 3.4 Pm which has been attributed to saturated aliphatic hydrocarbons on interstellar grains. The similarity of the absorption bands near 3.4 microns (2950 /cm) along different lines of sight reveal that the carrier of this band lies in the diffuse dust. Several materials have been proposed as "fits" to the 3.4 microns feature over the years. A comparison of these identifications is presented. A remarkable similarity between the spectrum of the diffuse dust and an organic extract from the Murchison meteorite suggests that some of the interstellar organic material may be preserved in primitive solar system bodies. Recent detections of the hydrocarbon absorption feature in external galaxies are presented. These results indicate the widespread availability of the organic material for incorporation into planetary systems. Recent detections of the X-CN absorption feature in dense molecular clouds are also presented. The preliminary results suggest that the X-CN band is only observed toward embedded sources and is not distributed throughout the molecular cloud. If so, this has profound effects on the production mechanism of this component of the organic material in space.

Pendleton, Yvonne J.↗
DOE OSTI2020

Imaging of Calibrated Defects in Additively Manufactured Materials: Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components

Additive manufacturing (AM, or 3D printing) for commercial nuclear energy applications is an emerging method for cost-efficient manufacturing aimed at replacing aging nuclear reactor parts and reducing costs for new construction. Known material flaws in AM include low-density regions consisting of non-sintered powder, which have to be detected to ensure the safety of long-term performance nuclear reactor components. Currently, limited options are available for nondestructive evaluation (NDE), either during or post manufacturing. As a solution to NDE of AM, we are developing pulsed thermal imaging which is non-contact, one-sided, and scalable to arbitrary size and shape of the AM parts. Pulsed thermography (PT) system utilizes a high intensity flash lamp to rapidly heat surface of sample, and a high sensitivity fast-frame megapixel infrared (IR) camera to capture data of surface temperature variations. The acquired data cube consists of a stack of surface temperature images taken at different times. Information about material internal defects is extracted by analyzing the data cube. This report provides results of preliminary performance evaluation of pulsed thermal imaging capability in detection of imprinted flaws in AM metallic structures. The flaws were introduced into AM parts as imprinted hemispherical low density regions, consisting of trapped un-sintered metallic powder. Specimens for developed for this study consisted of AM stainless steel 316 and Inconel 718 plates. The diameters of imprinted defects varied from 1mm to 8mm, and their depths below the plate flat surface varied between 1mm and 6mm. Pulsed thermal tomography (PTT) processes the measured data cube to obtain 3D reconstructions of material effusivity using a unique inversion algorithm developed at Argonne. PTT has been previously used in imaging of similar size flat bottom hole (FBH) simulated defects in stainless steel 316 and Inconel 718 specimens. In the study involving AM specimens, PTT imaging results have shown that 1mm-diameter defects located 1mm and 2mm below the surface of specimens were detectable. Larger size defects were detectable at greater depth. We also explored an alternative approach to detection of material flaws in PT data cube, which is using neural learning-based approach to blind source separation. Detection of small material defects requires finding features in the data cube which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with phtotothermics, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Optimal vocabulary selection approaches for privacy-preserving deep NLP model training for information extraction and cancer epidemiology

With the use of artificial intelligence and machine learning techniques for biomedical informatics, security and privacy concerns over the data and subject identities have also become an important issue and essential research topic. Without intentional safeguards, machine learning models may find patterns and features to improve task performance that are associated with private personal information. The privacy vulnerability of deep learning models for information extraction from medical textural contents needs to be quantified since the models are exposed to private health information and personally identifiable information. The objective of the study is to quantify the privacy vulnerability of the deep learning models for natural language processing and explore a proper way of securing patients’ information to mitigate confidentiality breaches. The target model is the multitask convolutional neural network for information extraction from cancer pathology reports, where the data for training the model are from multiple state population-based cancer registries. This study proposes the following schemes to collect vocabularies from the cancer pathology reports; (a) words appearing in multiple registries, and (b) words that have higher mutual information. We performed membership inference attacks on the models in high-performance computing environments. The comparison outcomes suggest that the proposed vocabulary selection methods resulted in lower privacy vulnerability while maintaining the same level of clinical task performance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Interferometric SAR modelling of near surface data to improve geological model in the Surat Basin, Australia

Here we present a study on geophysical inverse modelling for subsurface structural properties of an unconventional hydrocarbon site that was monitored previously by Interferometric Synthetic Aperture Radar (InSAR) technology for surface deformation. A static three-dimensional geomodel along with extracted property maps replicates the depth of each underlying stratigraphic unit and structural feature with the density of each geological layer. We examine the hypothesis that integration of elastic properties of each formation layer with InSAR observations in a stratified elastic medium will lead to a viscoelastic geophysical inverse problem that can be solved to estimate fractional volume change at the reservoir level. Moreover, we examine synthetic scenarios in which the elastic properties of the formations are perturbed before determining the resulting impact on the rate of surface deformation. The results show that although the slope of underlying formations, their density and depth can define the extent and pattern of a deformation signal, their properties have a marginal impact on volumetric change compared to the dense network of shallow depth Coal Seam Gas (CSG) mining wells. Besides, it is also demonstrated that the inversion of InSAR deformation maps can resolve the uncertainties associated with low-resolution seismic interpretation as well as filling the data gaps within seismic acquisitions. A significant contribution of this investigation to the geological basin modelling involves a) introducing a remote and non-invasive technology such as InSAR to improve geophysical mapping of subsurface structures such as faults in areas with sparse or no reflective seismic information, and b) applying a multi-layer viscoelastic geophysical source model for an unconventional hydrocarbon reservoir such as CSG.

58 GEOSCIENCES↗
DOE OSTI2025

Recovering high-purity uranyl nitrate from simulated used nuclear fuel dissolver solutions by crystallization: rejecting technetium

The separation of U from Tc and other problematic fission product elements like Mo and Ru, along with Sr, Zr, Cs, and Nd, has been achieved via the crystallization of uranyl nitrate hexahydrate (UNH). Rejection of technetium as pertechnetate anion ( 99 TcO 4 – ) is an especially important feature of this system, as it otherwise tends to follow U (VI) in extractive separations. It also raises the salient question regarding why this oxoanion cannot replace nitrate within the crystalline lattice of UNH. Results showed high-yield (>90 %), high-purity (>99 %) recovery of U as UNH from solutions containing 99 TcO 4 – by simple reduction of temperature from 60°C to 20°C. There was no observable interaction of 99 TcO 4 – with UO 2 2+ . The addition of other cations like, Sr 2+ , Zr 4+ , Cs + , and Nd 3+ , also did not form secondary, contaminant solid phases, leaving the > 99 % of the fission product elements in the mother liquor, while the U was recovered at > 90 %. Similarly, Mo and Ru, when added to the mixture, were shown to behave as the other fission-product elements, remaining in the mother liquor during crystallization. As a result, DFT calculations showed that, despite the higher binding strength of TcO 4 – , HMoO 4 – , and BiO 3 – with the UO 2 2+ cation compared to NO 3 – , the hydrogen-bonding network of the two coordinated ions and four waters of hydration in the UNH crystal structure is the driving force for the high specificity of this separation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Nickel(II) Metal Complexes as Optically Addressable Qubit Candidates

The inherent atomic level structural control of synthetic chemistry enables the creation of qubits, the base units of a quantum information science system, designed for a target application. For quantum sensing applications, enabling optical read-out of spin in tunable molecular systems, akin to defect-based systems, would be transformative. This approach would bring together molecular tunability with optical read-out technology. In theory, nickel ions in octahedral symmetry meet all the criteria for optical readout of spin. Yet, to the best of our knowledge, there are no pulse EPR studies on Ni 2+ molecules. We identified two compounds featuring highly symmetric Ni 2+ centers, thereby engendering weak zero-field splitting to enable EPR addressability:[Ni(phen) 3 ](BF 4 ) 2 (1) and [Ni(pyr 3 ) 2 ](BF 4 ) 2 (2) (phen = 1,10-phenanthroline; pyr 3 = tris-2-pyridyl-methane). Crucially, these complexes feature the requisite strong field ligands to enable emission for optical addressability. We extracted axial zero-field splitting parameters of D = +0.9 cm –1 and +2.7 cm –1 for 1 and 2, respectively, enabling pulse EPR measurements. Both compounds produce emission at λ max = 938–944 nm. Furthermore, the aggregate of these results expands the catalogue of qubit materials to Ni 2+ -based compounds and offers a future pathway for optical readout of these molecules.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Scale-Dependent Bedform Migration and Deformation in the Physical and Spectral Domains

Multi-scale bathymetries observed in laboratory channels and natural rivers have posed several challenges to the description of bedform geometry and kinematics. To better quantify scale-dependent migration velocity, high resolution spatio-temporal bed evolution data are analyzed using three independent methods: (i) a bedform tracking method that identifies individual bedforms in longitudinal bed elevation profiles, (ii) a cross-correlation based, image matching technique in consecutive bathymetry scans similar to Particle Image Velocimetry, and (iii) two-dimensional frequency-wavenumber spectra marking a dispersion relation between bedform period and length, allowing estimation of Fourier-based, scale-dependent migration velocities. The comparative studies show that the spectral and bedform tracking methods present small differences in the migration velocity of medium-large bedforms. However, significant deviations occur in the small bedforms, mostly due to bedform sheltering or amplified exposure to the flow, which also contribute to their enhanced scale-dependent deformation. The analysis also shows that some form of scale separation is needed to extract the large and slowly migrating bedforms as they are masked by secondary features when several bedform orders coexist. Bathymetric Image Velocimetry reasonably estimates the averaged migration velocity and exhibits potential to study relations between spanwise bed surface velocity and local bedform kinematics.

58 GEOSCIENCES↗
DOE OSTI2022

Observation and diagnostic application of Kr K-shell emission in magnetized liner inertial fusion experiments at Z

In a series of Magnetized Liner Inertial Fusion (MagLIF) experiments performed at the Z pulsed power accelerator of Sandia National Laboratories, beryllium liners filled with deuterium gas pressures in the 4–8 atm range and a tracer amount of krypton were imploded. At the collapse of the cylindrical implosion, temperatures in the 1–3 keV range and atom number densities of ∼1023 cm−3 were expected. The plasma was magnetized with a 10 T axial magnetic field. Krypton was added to the fuel for diagnosing implosion plasma conditions. Krypton K-shell line emission was recorded with the CRITR time-integrated transmission crystal x-ray spectrometer. The observation shows n = 2 to n = 1 line emissions in B-, Be-, Li-, and He-like Kr ions and is characteristic of the highest electron temperatures achieved in the thermonuclear plasma. Detailed modeling of the krypton atomic kinetics and radiation physics permits us to interpret the composite spectral feature, and it demonstrates that the spectrum is temperature sensitive. We discuss temperatures extracted from the krypton data analysis for experiments performed with several filling pressures.

Clapp, J. T. (ORCID:0000000219928146)↗
DOE OSTI2021

Statistical Characterization of Temperature and Pressure Vertical Profiles for the Analysis of Laser Heterodyne Radiometry Data

The statistical analysis of historic pressure and temperature profiles from radiosonde launches for use in the fitting of molecular oxygen line shapes is presented. As the O 2 mixing ratio is nearly constant throughout the lower atmosphere, only variations in pressure and temperature profiles will affect the fit of observed O 2 features in Laser Heterodyne Radiometry (LHR) spectra. Radiosonde temperature and pressure data are extracted from the Integrated Global Radiosonde Archive (IGRA) for a given station, date, and launch time. Data may be extracted for a single launch, for the same date over several years, and/or within a window centered on a target date. The temperature and pressure profiles are further characterized by the statistical variation in coefficients of polynomial fits in altitude. The properties of the probability distributions for each coefficient are used to constrain fits of O 2 line shapes through Nelder–Mead optimization. The refined temperature and pressure profiles are then used in the retrieval of vertically resolved mixing ratios for greenhouse gases (GHGs) measured in the same instrument. In continuous collections, each vertical profile determination may be treated as a Bayesian prior to inform subsequent measurements and provide an estimate of uncertainties.

47 OTHER INSTRUMENTATION↗
NASA NTRS1988

Knowledge-based low-level image analysis for computer vision systems

Two algorithms for entry-level image analysis and preliminary segmentation are proposed which are flexible enough to incorporate local properties of the image. The first algorithm involves pyramid-based multiresolution processing and a strategy to define and use interlevel and intralevel link strengths. The second algorithm, which is designed for selected window processing, extracts regions adaptively using local histograms. The preliminary segmentation and a set of features are employed as the input to an efficient rule-based low-level analysis system, resulting in suboptimal meaningful segmentation.

Dhawan, Atam P.↗