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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Test Plan: Enhanced HLW Glass Property-Composition Models - Phase I, VSL-13T3080-1 (May 2013) (Rev. 0)

In the present work, previously compiled glass property-composition databases for WTP HLW glasses will be updated with any new data that have become available. The previous evaluation of the baseline WTP models against the larger data sets in terms of range of validity and prediction performance will be used together with a review of the HLW glass property-composition data to identify gaps in the composition space that will need to be filled to support the development of expanded models. The results will be used to identify the composition spaces where gaps exist that need to be filled by new compositions to be tested. Composition matrices will be developed for this purpose. Crucible melts of the glass compositions identified in these matrices will be prepared and tested for the properties of interest. Finally, the entire composition space and glass property data available will be used in the development of improved and extended models. Review of the WTP HLW glass property-composition models against the extended datasets and initial identification of the data gaps will also be used to judge whether to develop one set of models for the entire HLW glass composition space or to develop separate models for discrete composition spaces. The longer-term objective of this multi-year task is to develop new glass property-composition models for PCT release, spinel crystallization (T1%), nepheline formation on CCC, TCLP Cd response, viscosity and electrical conductivity to control glass compositions for HLW vitrification at Hanford.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1981

Determination of in vivo mechanical properties of long bones from their impedance response curves

A mathematical model consisting of a uniform, linear, visco-elastic, Euler-Bernoulli beam to represent the ulna or tibia of the vibrating forearm or leg system is developed. The skin and tissue compressed between the probe and bone is represented by a spring in series with the beam. The remaining skin and tissue surrounding the bone is represented by a visco-elastic foundation with mass. An extensive parametric study is carried out to determine the effect of each parameter of the mathematical model on its impedance response. A system identification algorithm is developed and programmed on a digital computer to determine the parametric values of the model which best simulate the data obtained from an impedance test.

Borders, S. G.↗
NASA NTRS1986

A procedure for calculating the damping in multi-element space structures

A procedure for analyzing the damping in a multielement space structure connected by joints is described, in which distributed material damping and discrete nonlinear joint properties are incorporated into a linear analysis. The procedure involves four steps: (1) creation of a linear undamped finite element model; (2) experimental measurements of the transient response of a truss member in free fall tests to obtain material damping properties; with these properties incorporated into a linear damped finite element model of the structure; (3) the identification of the nonlinear joint properties using the force-state mapping technique; and (4) linearization of the identified nonlinear components, which are then incorporated into the linear damped model to create the linearized damped finite element model.

Crawley, E. F.↗
DOE OSTI2024

Weak-Form Latent Space Dynamics Identification

This software showcases the enhanced capabilities of the Latent Space Dynamics Identification (LaSDI) algorithm through the application of the weak form, resulting in WLaSDI. WLaSDI first compresses the data, then projects it onto test functions, and subsequently learns the local latent space models. Notably, WLaSDI demonstrates significantly improved robustness to noise. Using weak-form equation learning techniques, WLaSDI achieves local latent space modeling. Compared to the standard sparse identification of nonlinear dynamics (SINDy) used in LaSDI, the variance reduction of the weak form ensures robust and precise latent space recovery, enabling fast, robust, and accurate simulations. We demonstrate the efficacy of WLaSDI against LaSDI using several common benchmark examples, including viscid and inviscid Burgers', radial advection, and heat conduction. For instance, in 1D inviscid Burgers' simulations with up to 100% Gaussian white noise, WLaSDI maintains relative errors consistently below 6%, whereas LaSDI errors can exceed 10,000%. Similarly, in radial advection simulations, WLaSDI keeps relative errors below 16%, compared to potential errors of up to 10,000% with LaSDI. Additionally, WLaSDI achieves significant speedups, such as a 140X speedup in 1D Burgers' simulations compared to the corresponding full order model.

Choi, Youngsoo↗
DOE OSTI2021

Biomass supply chain equipment for renewable fuels production: A review

The production of renewable fuels is a critical component of global strategies to reduce greenhouse gas (GHG) emissions. Moreover, the collection of raw materials for its production can provide added benefits such as reduction of wildfire risk, additional income for farmers, and decreased disposal costs. Although there is substantial literature on design and modeling of supply chains, the authors were unable to find a single reference with the information needed for the selection and cost estimation of each type of equipment involved in the supply chain. Therefore, the goal of this research is to gather information necessary for the construction and utilization of models that might drive the identification of a feasible supply chain to produce renewable fuels at a commercial scale. The primary objectives are to 1) understand the supply chain of critical feedstocks for renewable fuels production; 2) identify the equipment commercially available for collection and adequation of feedstock; and 3) consolidate information regarding equipment cost, energy consumption, and efficiency, as well as feedstock storage and transportation systems. This paper provides a compilation for five feedstock types studied for sustainable aviation fuel production: 1) agricultural residues and grasses, 2) forest residues, 3) urban wood waste, 4) oilseeds, 5) fats, oils & greases. All the technologies involved from the field to the gate of the preprocessing or conversion unit were reviewed. The information on fats, oils & greases supply chains and equipment purposely designed for forest thinning and pruning was very limited.

: Feedstock, Collection and Adequation, Renewable ↗
DOE OSTI2018

Atom probe tomography analysis of the reference Zircon GJ-1: An interlaboratory study

In recent years, atom probe tomography (APT) has been increasingly used to study minerals, and in particular the mineral zircon. Zircon (ZrSiO 4 ) is ideally suited for geochronology by utilising the U-Th-Pb isotope systems, and trace element compositions are also widely used to constrain petrogenetic processes. However, while standard geoanalytical techniques provide information at micron scale lengths, the unique combination of chemical/isotopic sensitivity and spatial resolution of APT allows compositional and textural measurements at the nanoscale. This round robin study aims to define the reproducibility of APT data across research facilities and assess the role of different aspects of the atom probe workflow on reproducibility. This is essential to allow correct evaluation of APT results and full utilization of this emerging technique within the geoscience community. In this study, nine samples from the same homogemeous, GJ-1/87 zircon reference grain were sent to nine APT institutes in Europe, USA, Canada and Australia. Each institute conducted three different rounds of APT analyses: using (i) unconstrained analysis parameters, [1] pre-defined analysis parameters, and (iii) a data analysis exercise. Data such as the measured composition, acquisition parameters, or mass spectrum peak identifications, were recorded and analyzed. We observe a significant variation in the measured composition across this round robin study as well as the number of trace elements identified. These differences are thought to directly result from the user's choice of peak identification, ranging and background correction model. The type of instrument does not seem to be a critical factor. Consequently, comparison of absolute trace element data on zircon using APT between laboratories is only valid if the same workflow has been ensured.

round robin, atom probe tomography, reference zirc↗
DOE OSTI2021

MolDiscovery: learning mass spectrometry fragmentation of small molecules

Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗
DOE OSTI2021

Cryo-electron tomography provides topological insights into mutant huntingtin exon 1 and polyQ aggregates

Huntington disease (HD) is a neurodegenerative trinucleotide repeat disorder caused by an expanded poly-glutamine (polyQ) tract in the mutant huntingtin (mHTT) protein. The formation and topology of filamentous mHTT inclusions in the brain (hallmarks of HD implicated in neurotoxicity) remain elusive. Using cryo-electron tomography and subtomogram averaging, here we show that mHTT exon 1 and polyQ-only aggregates in vitro are structurally heterogenous and filamentous, similar to prior observations with other methods. Yet, we find filaments in both types of aggregates under ~2 nm in width, thinner than previously reported, and regions forming large sheets. In addition, our data show a prevalent subpopulation of filaments exhibiting a lumpy slab morphology in both aggregates, supportive of the polyQ core model. This provides a basis for future cryoET studies of various aggregated mHTT and polyQ constructs to improve their structure-based modeling as well as their identification in cells without fusion tags.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

The non-random walk of chiral magnetic charge carriers in artificial spin ice

The flow of magnetic charge carriers (dubbed magnetic monopoles) through frustrated spin ice lattices, governed simply by Coulombic forces, represents a new direction in electromagnetism. Artificial spin ice nanoarrays realise this effect at room temperature, where the magnetic charge is carried by domain walls. Control of domain wall path is one important element of utilizing this new medium. By imaging the transit of domain walls across different connected 2D honeycomb structures we contribute an important aspect which will enable that control to be realized. Although apparently equivalent paths are presented to a domain wall as it approaches a Y-shaped vertex from a bar parallel to the field, we observe a stark non-random path distribution, which we attribute to the chirality of the magnetic charges. These observations are supported by detailed statistical modelling and micromagnetic simulations. The identification of chiral control to magnetic charge path selectivity invites analogy with spintronics.

36 MATERIALS SCIENCE↗
DOE OSTI2015

SYSTEMATIC CALCULATIONS OF ENERGY LEVELS AND TRANSITION RATES OF C-LIKE IONS WITH Z = 13-36

Based on systematic calculations using a combined relativistic configuration interaction and a many-body perturbation theory (MBPT) approach, we provide a complete and consistent data set for 46 levels belonging to the 2s {sup 2}2p {sup 2}, 2s2p {sup 3}, 2p {sup 4}, 2s {sup 2}2p3s, 2s {sup 2}2p3p, and 2s {sup 2}2p3d configurations in C-like ions with 13 ≤ Z ≤ 36. The data set includes energy levels as well as electric dipole, magnetic dipole, electric quadrupole, and magnetic quadrupole transition properties. Extensive comparisons with available observed and calculated results are made and indicate that the present MBPT calculations are highly accurate. The present data set can be used reliably for many purposes, such as the line identification of observed spectra, and modeling and diagnostics of astrophysical and fusion plasmas.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Direct spectroscopic evidence for mixed-valence Tl in the low carrier-density superconductor Pb 1 - x Tl x Te

Upon doping with Tl the narrow band-gap semiconductor PbTe exhibits anomalously high-temperature superconductivity despite a very low carrier-density as well as signatures of the Kondo effect despite an absence of magnetic moments. These phenomena have been explained by invoking 2e fluctuations of the valence of the Tl dopants, but a direct measurement of the mixed valency implied by such a mechanism has not been reported to date. Here in this work we present the unambiguous observation of multiple valences of Tl in Tl-doped PbTe via photoemission spectroscopy measurements. It is shown via a quantitative analysis that the suppression of the carrier density in compositions exhibiting superconductivity and Kondo-like behavior can be accounted for by mixed valency, thus arguing against a self-compensation scenario proposed elsewhere for this material and strengthening the case for valence fluctuation models. In addition to the identification of Tl + and Tl 3+ a possible third intermediate local charge-density is suggested by full fits to the data, the origins of which are also discussed but remain unclear.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

A Cyber-Physical Anomaly Detection for Wide-Area Protection Using Machine Learning

Wide-area protection scheme (WAPS) provides system-wide protection by detecting and mitigating small and large-scale disturbances that are difficult to resolve using local protection schemes. As this protection scheme is evolving from a substation-based distributed remedial action scheme (DRAS) to the control center-based centralized RAS (CRAS), it presents severe challenges to their cybersecurity because of its heavy reliance on an insecure grid communication, and its compromise would lead to system failure. This article presents an architecture and methodology for developing a cyber-physical anomaly detection system (CPADS) that utilizes synchrophasor measurements and properties of network packets to detect data integrity and communication failure attacks on measurement and control signals in CRAS. The proposed machine leaning-based methodology applies a rules-based approach to select relevant input features, utilizes variational mode decomposition (VMD) and decision tree (DT) algorithms to develop multiple classification models, and performs final event identification using a rules-based decision logic. Here, we have evaluated the proposed methodology of CPADS using the IEEE 39 bus system for several performance measures (accuracy, recall, precision, and F-measure) in a cyber-physical testbed environment. Furthermore, our experimental results reveal that the proposed algorithm (VMD-DT) of CPADS outperforms the existing machine learning classifiers during noisy and noise-free measurements while incurring an acceptable processing overhead.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Directional Recoil Detection

Searches for dark matter–induced recoils have made impressive advances in the last few years. Yet the field is confronted by several outstanding problems. First, the inevitable background of solar neutrinos will soon inhibit the conclusive identification of many dark matter models. Second, and more fundamentally, current experiments have no practical way of confirming a detected signal's Galactic origin. The concept of directional detection addresses both of these issues while offering opportunities to study novel dark matter– and neutrino-related physics. The concept remains experimentally challenging, but gas time projection chambers are an increasingly attractive option and, when properly configured, would allow directional measurements of both nuclear and electron recoils. In this review, we reassess the required detector performance and survey relevant technologies. Fortuitously, the highly segmented detectors required to achieve good directionality also enable several fundamental and applied physics measurements. As a result, we comment on near-term challenges and how the field could be advanced.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Cyber-Attack Detection and Accommodation for the Energy Delivery System

The goals of this project were to create a software system with a suite of key algorithms for cyber-attack detection and accommodation providing domain layer protection for critical power generation assets. Example assets included gas and steam turbines, heat recovery steam generators, and electrical generators. The aggressive algorithm goals were aimed at reducing the false positive rates in threat detection to <1% using learnings from many evolving disciplines (power turbine and generator physics, power system modeling, modern control theory, system identification, machine learning, deep learning, mathematics and data science). Additional goals for the algorithms involved localizing threats on-the-fly to know in which monitoring node the effects of attacks are present, and then providing accommodation to keep the system running uninterrupted much of the time in the presence of the attack. Accommodation had a performance goal of providing resiliency when up to 50% of monitoring nodes are in an attack state.

cybersecurity, cyber-physical↗
NASA NTRS1970

System identification - A survey

System identification, considering input signals classification, model structure, linear/nonlinear systems identifying and on-line/real time techniques

Astroem, K.-J.↗