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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Categorical dimensions of human odor descriptor space revealed by non-negative matrix factorization

In contrast to most other sensory modalities, the basic perceptual dimensions of olfaction remain un- clear. Here, we use non-negative matrix factorization (NMF) - a dimensionality reduction technique - to uncover structure in a panel of odor profiles, with each odor defined as a point in multi-dimensional descriptor space. The properties of NMF are favorable for the analysis of such lexical and perceptual data, and lead to a high-dimensional account of odor space. We further provide evidence that odor di- mensions apply categorically. That is, odor space is not occupied homogenously, but rather in a discrete and intrinsically clustered manner. We discuss the potential implications of these results for the neural coding of odors, as well as for developing classifiers on larger datasets that may be useful for predicting perceptual qualities from chemical structures.

Chennubhotla, Chakra↗
DOE OSTI2025

Systematic characterization of unknown compounds via dimensionality reduction of time series

Analysis of ambient aerosols provides valuable insight into particle sources and formation chemistry. However, due to the complexity of atmospheric data and the dynamic nature of aerosol composition, a substantial fraction of data often become discarded by conventional analysis methods. Furthermore, a large fraction of chemical species within those data are unidentifiable due to a lack of matching spectral information, resulting in suboptimal characterization of chemical composition. Previous work has demonstrated techniques for cataloging analytes in a chromatographic dataset by deconvolution of mass spectra, but integration of these analytes throughout a large dataset remains time consuming. Here, we present a method to automatically identify an ion for quantitation for single-ion chromatogram based peak fitting and integration, enabling comprehensive integration of analytes with minimal user interaction. The resulting time series are clustered with a machine-learning based dimensionality reduction technique to systematically investigate the underlying characteristics of the categorized analytes and gain new insights into the chemical composition and physicochemical properties of the unidentifiable analytes. We apply these methods to existing atmospheric datasets collected in Manacapuru, Brazil during the GoAmazon2014/5 campaign to identify new analytes and interpret their variability and transformations in the atmosphere. The analysis results generate 408 time series from cataloged analytes of interest, and the clustering of those time series with spherical k-means results in 8 distinct clusters. We find the analytes form clusters based on their distinct physicochemical properties, demonstrating the method’s ability to systematically identify and selectively filter contaminants and instrumental analytes and characterize the unidentifiable analytes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A study of in situ reduction of MoO 3 to MoO 2 by X-ray Photoelectron Spectroscopy

Results from X-ray Photoelectron Spectroscopy (XPS) of molybdenum oxide samples are presented to elucidate how Mo (VI) oxide evolves to Mo (IV) oxide upon heating of a MoO 3 sample. XPS data analysis techniques based on manipulation of spectra treated as vectors are shown and allow insights into intermediate phases of Mo oxide which suggest how the original oxide changes under the influence of heat but also supports an interpretation of as-received tetravalent Mo powders as multivalent materials. In particular, several new spectral components were observed and assigned to the Magnéli phase as well as MoO 3 domains that have been modified by the presence of X-rays or temperature. Finally, these assignments comprise a new method of data processing where sample modification is used to inform XPS data interpretation and differ from previous work where the linear relationship of the binding energy of molybdenum oxides has been used.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Reducing Soft-error Vulnerability of Caches using Data Compression

With ongoing chip miniaturization and voltage scaling, particle strike-induced soft errors present increasingly severe threat to the reliability of on-chip caches. In this paper, we present a technique to reduce the vulnerability of caches to soft-errors. Our technique uses data compression to reduce the number of vulnerable data bits in the cache and performs selective duplication of more critical data-bits to provide extra protection to them. Microarchitectural simulations have shown that our technique is effective in reducing architectural vulnerability factor (AVF) of the cache and outperforms another technique. For single and dual-core system configuration, the average reduction in AVF is 5.59X and 8.44X, respectively. Also, the implementation and performance overheads of our technique are minimal and it is useful for a broad range of workloads.

Mittal, Sparsh↗
DOE OSTI2015

Characterizing Structural Controls of EGS-Candidate and Conventional Geothermal Reservoirs in the Great Basin: Developing Successful Exploration Strategies in Extended Terranes

We conducted a comprehensive analysis of the structural controls of geothermal systems within the Great Basin and adjacent regions. Our main objectives were to: 1) Produce a catalogue of favorable structural environments and models for geothermal systems. 2) Improve site-specific targeting of geothermal resources through detailed studies of representative sites, which included innovative techniques of slip tendency analysis of faults and 3D modeling. 3) Compare and contrast the structural controls and models in different tectonic settings. 4) Synthesize data and develop methodologies for enhancement of exploration strategies for conventional and EGS systems, reduction in the risk of drilling non-productive wells, and selecting the best EGS sites.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Impact of Multifunctional Bimetallic Materials on Lithium Battery Electrochemistry

Electric energy storage devices such as batteries are complex systems comprised of a variety of materials with each playing separate yet interactive roles, complicated by length scale interactions occurring from the molecular to the mesoscale. Thus, addressing specific battery issues such as functional capacity requires a comprehensive perspective initiating with atomic level concepts. For example, the electroactive materials which contribute to the functional capacity in a battery comprise approximately 30% or less of the total device mass. Thus, the design and implementation of multifunctional materials can conceptually reduce or eliminate the contribution of passive materials to the size and mass of the final system. Material multi-functionality can be achieved through appropriate material design on the atomic level resulting in bimetallic electroactive materials where one metal cation forms mesoscale conductive networks upon discharge while the other metal cation can contribute to atomic level structure and net functional secondary capacity, a device level issue. Specifically, this Account provides insight into the multi-mechanism electrochemical redox processes of bimetallic cathode materials based on transition metal oxides (MM’O) or phosphorous oxides (MM’PO) where M = Ag and M’ = V or Fe. One discharge process can be described as reduction-displacement where Ag + is reduced to Ag 0 and displaced from the parent structure. This reduction-displacement reaction in silver-containing bimetallic electrodes allows for the in-situ formation of a conductive network, enhancing the electrochemical performance of the electrode and reducing or eliminating the need for conductive additives. A second discharge process occurs through the reduction of the second transition metal, V or Fe, where the oxidation state of the metal center is reduced and lithium cations are inserted into the structure. As both metal centers contribute to the functional capacity, determining the kinetically and thermodynamically preferred reduction processes at various states of discharge is critical to elucidating the mechanism. Specific advanced in-situ and ex-situ characterization techniques are conducive to gaining insight regarding the electrochemical behavior of these multifunctional materials over multiple length scales. At the material level, optical microscopy, scanning electron microscopy, and local conductivity measurement via a nanoprobe can track the discharge mechanism of an isolated single particle. At the mesoscale electrode level, in-situ data from synchrotron based energy dispersive X-ray diffraction (EDXRD) within fully intact steel batteries can be used to spatially map the distribution of silver metal generated through reduction displacement as a function of discharge depth and discharge rate. As illustrated here, appropriate design of materials with multiple electrochemically active metal centers and properties tuned through strategically conceptualized materials synthesis may provide a path toward the next generation of high energy content electroactive materials and systems. In conclusion, full understanding of the multiple electrochemical mechanisms can be achieved only by utilizing advanced characterization tools over multiple length scales.

25 ENERGY STORAGE↗
DOE OSTI2019

Non-Intrusive Inference Reduced Order Model for Fluids Using Deep Multistep Neural Network

In this effort we propose a data-driven learning framework for reduced order modeling of fluid dynamics. Designing accurate and efficient reduced order models for nonlinear fluid dynamic problems is challenging for many practical engineering applications. Classical projection-based model reduction methods generate reduced systems by projecting full-order differential operators into low-dimensional subspaces. However, these techniques usually lead to severe instabilities in the presence of highly nonlinear dynamics, which dramatically deteriorates the accuracy of the reduced-order models. In contrast, our new framework exploits linear multistep networks, based on implicit Adams–Moulton schemes, to construct the reduced system. The advantage is that the method optimally approximates the full order model in the low-dimensional space with a given supervised learning task. Moreover, our approach is non-intrusive, such that it can be applied to other complex nonlinear dynamical systems with sophisticated legacy codes. We demonstrate the performance of our method through the numerical simulation of a two-dimensional flow past a circular cylinder with Reynolds number Re = 100. The results reveal that the new data-driven model is significantly more accurate than standard projection-based approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

HUBBLE SPACE TELESCOPE PROPER MOTION (HSTPROMO) CATALOGS OF GALACTIC GLOBULAR CLUSTERS. I. SAMPLE SELECTION, DATA REDUCTION, AND NGC 7078 RESULTS

We present the first study of high-precision internal proper motions (PMs) in a large sample of globular clusters, based on Hubble Space Telescope (HST) data obtained over the past decade with the ACS/WFC, ACS/HRC, and WFC3/UVIS instruments. We determine PMs for over 1.3 million stars in the central regions of 22 clusters, with a median number of ∼60,000 stars per cluster. These PMs have the potential to significantly advance our understanding of the internal kinematics of globular clusters by extending past line-of-sight (LOS) velocity measurements to two- or three-dimensional velocities, lower stellar masses, and larger sample sizes. We describe the reduction pipeline that we developed to derive homogeneous PMs from the very heterogeneous archival data. We demonstrate the quality of the measurements through extensive Monte Carlo simulations. We also discuss the PM errors introduced by various systematic effects and the techniques that we have developed to correct or remove them to the extent possible. We provide in electronic form the catalog for NGC 7078 (M 15), which consists of 77,837 stars in the central 2.'4. We validate the catalog by comparison with existing PM measurements and LOS velocities and use it to study the dependence of the velocity dispersion on radius, stellar magnitude (or mass) along the main sequence, and direction in the plane of the sky (radial or tangential). Subsequent papers in this series will explore a range of applications in globular-cluster science and will also present the PM catalogs for the other sample clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Neoadjuvant Chemotherapy does not Increase Complications in Oncoplastic Breast-Conserving Surgery

Background: Oncoplastic breast-conserving surgery (OBCS) broadens the indications for breast conservation. Neoadjuvant systemic chemotherapy (NAC) is used increasingly in the treatment of patients with early-stage and locally advanced breast cancer. This study aimed to evaluate the outcomes for patients who received NAC followed by OBCS. Methods: A retrospective chart review was performed for all patients who underwent OBCS involving the mastopexy/breast-reduction technique, including synchronous mastopexy/breast reduction for symmetry, at the University of Texas MD Anderson Cancer Center between January 2010 and January 2016. Patients who had received NAC were compared with those who had undergone surgery first. Demographic, treatment, and outcomes data were collected. Results: The study included 429 patients, corresponding to 713 breasts. Of these patients, 122, corresponding to 199 breasts, received NAC. The patients who received NAC were younger (p < 0.001) and had a more advanced cancer stage (p < 0.001). The overall complication rate per patient was 25.9%, with major complications occurring in 9.1% of the patients. After adjustment for risk factors, NAC was not shown to be associated with an increased risk of complications or delayed adjuvant radiation therapy (p = 0.37), irrespective of the chemotherapy regimen used or whether the interval between NAC and surgery was 4 weeks or longer. Conclusions: In a high-volume center, OBCS can be performed safely for carefully selected patients after NAC without an increased risk of complications or delayed adjuvant radiation therapy. An interval of at least 4 weeks between completion of NAC and surgery can be regarded as safe irrespective of the chemotherapy regimen used.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Containment at the Source during Waste Volume Reduction of Large Radioactive Components Using Oxylance High-Temperature Cutting Equipment - 13595

As a waste-volume reduction and management technique, highly contaminated Control Element Drive Mechanism (CEDM) housings were severed from the Reactor Pressure Vessel Head (RPVH) inside the San Onofre Unit 2 primary containment utilizing Oxylance high-temperature cutting equipment and techniques. Presented are relevant data concerning: - Radiological profiles of the RPVH and individual CEDMs; - Design overviews of the engineering controls and the specialized confinement housings; - Utilization of specialized shielding; - Observations of apparent metallurgical-contamination coalescence phenomena at high temperatures resulting in positive control over loose-surface contamination conditions; - General results of radiological and industrial hygiene air sampling and monitoring; - Collective dose and personnel contamination event statistics; - Lessons learned. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2011

THE GALACTIC CENSUS OF HIGH- AND MEDIUM-MASS PROTOSTARS. I. CATALOGS AND FIRST RESULTS FROM MOPRA HCO{sup +} MAPS

The Census of High- and Medium-mass Protostars (CHaMP) is the first large-scale, unbiased, uniform mapping survey at sub-parsec-scale resolution of 90 GHz line emission from massive molecular clumps in the Milky Way. We present the first Mopra (ATNF) maps of the CHaMP survey region (300{sup 0} > l > 280{sup 0}) in the HCO{sup +} J = 1 {yields} 0 line, which is usually thought to trace gas at densities up to 10{sup 11} m{sup -3}. In this paper, we introduce the survey and its strategy, describe the observational and data reduction procedures, and give a complete catalog of moment maps of the HCO{sup +} J = 1{yields}0 emission from the ensemble of 303 massive molecular clumps. From these maps we also derive the physical parameters of the clumps, using standard molecular spectral-line analysis techniques. This analysis yields the following range of properties: integrated line intensity 1-30 K km s{sup -1}, peak line brightness 1-7 K, linewidth 1-10 km s{sup -1}, integrated line luminosity 0.5-200 K km s{sup -1} pc{sup 2}, FWHM size 0.2-2.5 pc, mean projected axial ratio 2, optical depth 0.08-2, total surface density 30-3000 M{sub sun} pc{sup -2}, number density (0.2-30) x 10{sup 9} m{sup -3}, mass 15-8000 M{sub sun}, virial parameter 1-55, and total gas pressure 0.3-700 pPa. We find that the CHaMP clumps do not obey a Larson-type size-linewidth relation. Among the clumps, there exists a large population of subthermally excited, weakly emitting (but easily detectable) dense molecular clumps, confirming the prediction of Narayanan et al. These weakly emitting clumps comprise 95% of all massive clumps by number, and 87% of the molecular mass, in this portion of the Galaxy; their properties are distinct from the brighter massive star-forming regions that are more typically studied. If the clumps evolve by slow contraction, the 95% of fainter clumps may represent a long-lived stage of pressure-confined, gravitationally stable massive clump evolution, while the CHaMP clump population may not engage in vigorous massive star formation until the last 5% of their lifetimes. The brighter sources are smaller, denser, more highly pressurized, and closer to gravitational instability than the less bright sources. Our data suggest that massive clumps approach critical Bonnor-Ebert-like states at constant density, while others' suggest that lower-mass clumps reach such states at constant pressure. Evidence of global gravitational collapse of massive clumps is rare, suggesting that this phase lasts <1% of the clumps' lifetime.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Combining Deep Learning and scatterControl for High-Throughput X-ray CT Based Non-Destructive Characterization of Large-Scale Casted Metallic Components

X-ray computed tomography (XCT) is essential for nondestructive evaluation and quality control of large-scale metal components. XCT imaging, however, faces significant challenges from metal artifacts, particularly those caused by Compton scattering, which degrade image quality and obscure critical details. Hardware-based solutions (e.g. scatterControl) offer advancements by intercepting scattered photons and reducing artifacts, but they can be time-consuming and require additional processing. Here, we propose modifying and leveraging a novel deep learning (DL) framework, Simurgh, to enhance and accelerate scatter correction in XCT. By combining scatterControl with DL-based artifact removal, we demonstrate significant reduction in scan time while producing high-quality reconstructions. Through extensive evaluation on industrial XCT data, we show that our methods reduce scan time by up to more than 10 x while preserving flaw detectability. Quantitative analysis across multiple segmentation techniques confirms that Simurgh-based reconstructions consistently outperform traditional Feldkamp-Davis-Kress, model-based iterative reconstruction, and commercial DL models in both pixel-level and task-specific evaluations, enabling scalable, high-throughput XCT workflows for characterization of large scale components in applications such as casting and metal additive manufacturing.

Complex metal parts↗
DOE OSTI2024

Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization

X-ray absorption spectroscopy (XAS) is a commonly employed technique for characterizing functional materials. In particular, X-ray absorption near edge spectra (XANES) encode local coordination and electronic information, and machine learning approaches to extract this information are of significant interest. To date, most ML approaches for XANES have primarily focused on using the raw spectral intensities as input, overlooking the potential benefits of incorporating spectral transformations and dimensionality reduction techniques into ML predictions. Here, in this work, we focused on systematically comparing the impact of different featurization methods on the performance of ML models for XAS analysis. We evaluated the classification and regression capabilities of these models on computed data sets and validated their performance on previously unseen experimental data sets. Our analysis revealed an intriguing discovery: the cumulative distribution function feature achieves both high prediction accuracy and exceptional transferability. This remarkably robust performance can be attributed to its tolerance to horizontal shifts in the spectra, which is crucial when validating models using experimental data. While this work exclusively focuses on XANES analysis, we anticipate that the methodology presented here will hold promise as a versatile asset to the broader spectroscopy community.

36 MATERIALS SCIENCE↗
DOE OSTI2016

SFU-Driven Transparent Approximation Acceleration on GPUs

Approximate computing, the technique that sacrifices certain amount of accuracy in exchange for substantial performance boost or power reduction, is one of the most promising solutions to enable power control and performance scaling towards exascale. Although most existing approximation designs target the emerging data-intensive applications that are comparatively more error-tolerable, there is still high demand for the acceleration of traditional scientific applications (e.g., weather and nuclear simulation), which often comprise intensive transcendental function calls and are very sensitive to accuracy loss. To address this challenge, we focus on a very important but often ignored approximation unit on GPUs.

Approximate computing↗
DOE OSTI2016

Changes in Spatiotemporal Precipitation Patterns in Changing Climate Conditions

Climate models robustly imply that some significant change in precipitation patterns will occur. Models consistently project that the intensity of individual precipitation events increases by approximately 6-7%/K, following the increase in atmospheric water content, but that total precipitation increases by a lesser amount (2-3%/K in the global average). Some other aspect of precipitation events must then change to compensate for this difference. We develop here a new methodology for identifying individual rainstorms and studying their physical characteristics - including starting location, intensity, spatial extent, duration, and trajectory - that allows identifying that compensating mechanism. We apply this technique to precipitation over the contiguous U.S. from both radar-based data products and high-resolution model runs simulating 100 years of business-as-usual warming. In model studies, we find that the dominant compensating mechanism is a reduction of storm size. In summer, rainstorms become more intense but smaller; in winter, rainstorm shrinkage still dominates, but storms also become less numerous and shorter duration. These results imply that flood impacts from climate change will be less severe than would be expected from changes in precipitation intensity alone. We show also that projected changes are smaller than model-observation biases, implying that the best means of incorporating them into impact assessments is via "data-driven simulations" that apply model-projected changes to observational data. We therefore develop a simulation algorithm that statistically describes model changes in precipitation characteristics and adjusts data accordingly, and show that, especially for summertime precipitation, it outperforms simulation approaches that do not include spatial information.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Unlocking hidden information in sparse small-angle neutron scattering measurements

Hypothesis Small-Angle Neutron Scattering (SANS) is a powerful technique for studying soft matter systems such as colloids, polymers, and lyotropic phases, providing nanoscale structural insights. However, its effectiveness is limited by low neutron flux, leading to long acquisition times and noisy data. Here, we hypothesize that Bayesian statistical inference using Gaussian Process Regression (GPR) can reconstruct high-fidelity scattering data from sparse measurements by leveraging intensity smoothness and continuity. Experiments and Simulations The method was benchmarked computationally and validated through SANS experiments on various soft matter systems, including wormlike micelles, colloidal suspensions, polymeric structures, and lyotropic phases. GPR-based inference was applied to both experimental and synthetic data to evaluate its effectiveness in noise reduction and intensity reconstruction. Findings GPR significantly enhances SANS data quality and therefore reducing measurement times by up to two orders of magnitude. This cost-effective approach maximizes experimental efficiency, enabling high-throughput studies and real-time monitoring of dynamic systems. It is particularly beneficial for weakly scattering and time-sensitive studies. Beyond SANS, this framework applies to other low-SNR techniques, including laboratory-based small-angle X-ray scattering and various dynamical scattering methods. Furthermore, it offers transformative potential for compact neutron sources, enhancing their viability for structural analysis in resource-limited settings.

Small angle neutron scattering↗
DOE OSTI2014

Tomographic inversion techniques incorporating physical constraints for line integrated spectroscopy in stellarators and tokamaks

Accurate tomographic inversion is important for diagnostic systems on stellarators and tokamaks which rely on measurements of line integrated emission spectra. A tomographic inversion technique based on spline optimization with enforcement of constraints is described that can produce unique and physically relevant inversions even in situations with noisy or incomplete input data. This inversion technique is routinely used in the analysis of data from the x-ray imaging crystal spectrometer (XICS) installed at the Large Helical Device. The XICS diagnostic records a 1D image of line integrated emission spectra from impurities in the plasma. Through the use of Doppler spectroscopy and tomographic inversion, XICS can provide profile measurements of the local emissivity, temperature, and plasma flow. Tomographic inversion requires the assumption that these measured quantities are flux surface functions, and that a known plasma equilibrium reconstruction is available. In the case of low signal levels or partial spatial coverage of the plasma cross-section, standard inversion techniques utilizing matrix inversion and linear-regularization often cannot produce unique and physically relevant solutions. The addition of physical constraints, such as parameter ranges, derivative directions, and boundary conditions, allow for unique solutions to be reliably found. The constrained inversion technique described here utilizes a modified Levenberg-Marquardt optimization scheme, which introduces a condition avoidance mechanism by selective reduction of search directions. The constrained inversion technique also allows for the addition of more complicated parameter dependencies, for example, geometrical dependence of the emissivity due to asymmetries in the plasma density arising from fast rotation. The accuracy of this constrained inversion technique is discussed, with an emphasis on its applicability to systems with limited plasma coverage.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Comparison of a new noncoplanar intensity-modulated radiation therapy technique for craniospinal irradiation with 3 coplanar techniques

When standard conformal x-ray technique for craniospinal irradiation is used, it is a challenge to achieve satisfactory dose coverage of the target including the area of the cribriform plate, while sparing organs at risk. We present a new intensity-modulated radiation therapy (IMRT), noncoplanar technique, for delivering irradiation to the cranial part and compare it with 3 other techniques and previously published results. A total of 13 patients who had previously received craniospinal irradiation with standard conformal x-ray technique were reviewed. New treatment plans were generated for each patient using the noncoplanar IMRT-based technique, a coplanar IMRT-based technique, and a coplanar volumetric-modulated arch therapy (VMAT) technique. Dosimetry data for all patients were compared with the corresponding data from the conventional treatment plans. The new noncoplanar IMRT technique substantially reduced the mean dose to organs at risk compared with the standard radiation technique. The 2 other coplanar techniques also reduced the mean dose to some of the critical organs. However, this reduction was not as substantial as the reduction obtained by the noncoplanar technique. Furthermore, compared with the standard technique, the IMRT techniques reduced the total calculated radiation dose that was delivered to the normal tissue, whereas the VMAT technique increased this dose. Additionally, the coverage of the target was significantly improved by the noncoplanar IMRT technique. Compared with the standard technique, the coplanar IMRT and the VMAT technique did not improve the coverage of the target significantly. All the new planning techniques increased the number of monitor units (MU) used—the noncoplanar IMRT technique by 99%, the coplanar IMRT technique by 122%, and the VMAT technique by 26%—causing concern for leak radiation. The noncoplanar IMRT technique covered the target better and decreased doses to organs at risk compared with the other techniques. All the new techniques increased the number of MU compared with the standard technique.

61 RADIATION PROTECTION AND DOSIMETRY↗