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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

MIT's interferometer CST testbed

The MIT Space Engineering Research Center (SERC) has developed a controlled structures technology (CST) testbed based on one design for a space-based optical interferometer. The role of the testbed is to provide a versatile platform for experimental investigation and discovery of CST approaches. In particular, it will serve as the focus for experimental verification of CSI methodologies and control strategies at SERC. The testbed program has an emphasis on experimental CST--incorporating a broad suite of actuators and sensors, active struts, system identification, passive damping, active mirror mounts, and precision component characterization. The SERC testbed represents a one-tenth scaled version of an optical interferometer concept based on an inherently rigid tetrahedral configuration with collecting apertures on one face. The testbed consists of six 3.5 meter long truss legs joined at four vertices and is suspended with attachment points at three vertices. Each aluminum leg has a 0.2 m by 0.2 m by 0.25 m triangular cross-section. The structure has a first flexible mode at 31 Hz and has over 50 global modes below 200 Hz. The stiff tetrahedral design differs from similar testbeds (such as the JPL Phase B) in that the structural topology is closed. The tetrahedral design minimizes structural deflections at the vertices (site of optical components for maximum baseline) resulting in reduced stroke requirements for isolation and pointing of optics. Typical total light path length stability goals are on the order of lambda/20, with a wavelength of light, lambda, of roughly 500 nanometers. It is expected that active structural control will be necessary to achieve this goal in the presence of disturbances.

Hyde, Tupper↗
DOE OSTI2024

Multi-Level Structural Damage Characterization Using Sparse Acoustic Sensor Networks and Knowledge Transferred Deep Learning

Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material, or geometric complexity. This motivates the exploration of machine-learning-based structural health monitoring methods in complex structures. However, creating sufficient training data sets with various defects is an ongoing challenge for data-driven machine (deep) learning algorithms. The ability to transfer the knowledge of a trained neural network from one component to another or to other sections of the same component would drastically reduce the required training data set. Also, it would facilitate computationally inexpensive machine learning based inspection systems. In this work, a machine-learning-based multi-level damage characterization is demonstrated with the ability to transfer trained knowledge within the sparse sensor network. A novel network spatial assistance and an adaptive convolution technique are proposed for efficient knowledge transfer within the deep learning algorithm. Proposed structural health monitoring method is experimentally evaluated on an aluminum plate with artificially induced defects. It was observed that the method improves the performance of knowledge transferred damage characterization by 50% during localization and 24% during severity assessment. Further, experiments using time windows with and without multiple edge reflections are studied. Results reveal that multiply scattered waves contain rich and deterministic defect signatures that can be mined using deep learning neural networks, improving the accuracy of both identification and quantification. In the case of a fixed sensor network, using multiply scattered waves shows 100% prediction accuracy at all levels of damage characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2023

CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment

Abstract We introduce Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture to learn unique feature representations and perform classification of materials across multiple scales (from atomic to mesoscale) and diverse classes ranging from metals, oxides, non-metals to hierarchical materials such as zeolites and semi-ordered mesophases. CEGANN can classify based on a global, structure-level representation such as space group and dimensionality (e.g., bulk, 2D, clusters, etc.). Using representative materials such as polycrystals and zeolites, we demonstrate its transferability in performing local atom-level classification tasks, such as grain boundary identification and other heterointerfaces. CEGANN classifies in (thermal) noisy dynamical environments as demonstrated for representative zeolite nucleation and growth from an amorphous mixture. Finally, we use CEGANN to classify multicomponent systems with thermal noise and compositional diversity. Overall, our approach is material agnostic and allows for multiscale feature classification ranging from atomic-scale crystals to heterointerfaces to microscale grain boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2022

On the use of vibronic coherence to identify reaction coordinates for ultrafast excited-state dynamics of transition metal-based chromophores

In this study, the question of whether one can use information from quantum coherence as a means of identifying vibrational degrees of freedom that are active along an excited-state reaction coordinate is discussed. Specifically, we are exploring the notion of whether quantum oscillations observed in single-wavelength kinetics data exhibiting coherence dephasing times that are intermediate between that expected for either pure electronic or pure vibrational dephasing are vibronic in nature and therefore may be coupled to electronic state-to-state evolution. In the case of a previously published Fe(ιι) polypyridyl complex, coherences observed subsequent to 1 A 1 → 1 MLCT excitation were linked to large-amplitude motion of a portion of the ligand framework; dephasing times on the order of 200–300 fs suggested that these degrees of freedom could be associated with ultrafast (~100 fs) conversion from the initially formed MLCT excited state to lower-energy, metal-centered ligand-field excited state(s) of the compound. Incorporation of an electronically benign but sterically restrictive Cu(ι) ion into the superstructure designed to interfere with this motion yielded a compound exhibiting a ~25-fold increase in the compound’s MLCT lifetime, a result that was interpreted as confirmation of the initial hypothesis. However, new data acquired on a different chemical system – Cr(acac') 3 (where acac' represents various derivatives of acetylacetonate) – yielded results that call into question this same hypothesis. Coherences observed subsequent to 4 A 2 → 4 T 2 ligand-field excitation on a series of molecules implicated similar vibrational degrees of freedom across the series, but exhibited dephasing times ranging from 340 fs to 2.5 ps without any clear correlation to the dynamics of excited-state evolution in the system. Taken together, the results obtained on both of these chemical platforms suggest that while identification of coherences can indeed point to degrees of freedom that should be considered as candidate modes for defining reaction trajectories, our understanding of the factors that determine the interplay across coherences, dephasing times, and electronic and geometric structure is insufficient at the present time to view this parameter as a robust metric for differentiating active versus spectator modes for ultrafast dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗
DOE OSTI2022

MINE 2.0: enhanced biochemical coverage for peak identification in untargeted metabolomics

Abstract Summary Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource. Availability and implementation The MINE 2.0 website can be accessed at https://minedatabase.ci.northwestern.edu. The MINE 2.0 web API documentation can be accessed at https://mine-api.readthedocs.io/en/latest/. The data and code underlying this article are available in the MINE-2.0-Paper repository at https://github.com/tyo-nu/MINE-2.0-Paper. MINE 2.0 source code can be accessed at https://github.com/tyo-nu/MINE-Database (MINE construction), https://github.com/tyo-nu/MINE-Server (backend web API) and https://github.com/tyo-nu/MINE-app (web app). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A Transductive Graph Neural Network learning for Grid Resilience Analysis

Power grids are critical infrastructures that require robust resilience analysis to ensure reliable and uninterrupted electricity supply. Traditional simulation-based methods for grid resilience analysis suffer from computational complexity and limited ability to capture the full spectrum of potential disruptions. This paper presents a novel approach to enhance grid resilience by leveraging transductive graph neural network (GNN) learning to identify critical nodes and links. By leveraging the graph structure and system features, GNNs effectively learn resilience metrics and accurately identify critical nodes based on actual grid operational behavior. The efficacy of the proposed approach is demonstrated through case studies on node criticality scoring and critical node/line identification in cascading outage scenarios. The results highlight the advantages of learning-based methods over traditional simulation-based approaches and their potential to revolutionize grid resilience analysis. The contributions of this paper include a graph-based scalable approach for fast cascading analysis, an inductive formulation for training GNN models, and a transfer learning-based approach to scale the model to largescale power systems.

grid resilience, graph neural networks, transducti↗
NASA NTRS1975

Evidence for detection of a moving magnetic monopole

A very heavy particle passed through a balloon-borne stack of Cherenkov film, emulsion, and Lexan sheets. In 33 Lexan sheets it produced tracks expected of either a nucleus with Z ranging between 125 and 137 and with beta less than about 0.92 or a magnetic monopole with g = 137e. Its track structure in emulsion indicated it was moving downward with beta equal to 0.5 (plus 0.1 or minus 0.05) and was either a nucleus with Z equal to 80 or a monopole with g = 137e. These facts strongly favor identification of the particle as a magnetic monopole of strength g = 137e and mass greater than 200 times proton mass.

Price, P. B.↗
NASA NTRS1983

Planetary atmospheres

During the period from 1979 to 1982, Pioneer and Voyager missions to Venus, Jupiter, and Saturn combined with ongoing analysis and data collection from the Viking-Mars mission have contributed greatly to an understanding of planetary atmospheres. Research concerning the atmosphere of Venus is discussed, taking into account the atmospheric composition, the isotopic ratios of noble gases, data concerning temperature and pressure, information regarding various atmospheric layers, atmospheric density data, and models regarding the dynamical properties of Venus' atmosphere. Information concerning the atmosphere of Mars is also examined. Isotopic ratios of carbon and oxygen are considered along with altitude profiles, diurnal heating and cooling, and thermal tides and their relationship to dust storms. Attention is given to the temperature structure of the Jovian atmosphere, the morphology of Jovian cloud features, features in the Saturnian atmosphere, information with respect to the atmosphere of Titan, the rotation rates of Uranus, masses and densities of Uranus and Neptune, and the identification of Pluto as a small icy body.

Beebe, R.↗
NASA NTRS1992

The 32nd CDC: System identification using interval dynamic models

Motivated by the recent explosive development of results in the area of parametric robust control, a new technique to identify a family of uncertain systems is identified. The new technique takes the frequency domain input and output data obtained from experimental test signals and produces an 'interval transfer function' that contains the complete frequency domain behavior with respect to the test signals. This interval transfer function is one of the key concepts in the parametric robust control approach and identification with such an interval model allows one to predict the worst case performance and stability margins using recent results on interval systems. The algorithm is illustrated by applying it to an 18 bay Mini-Mast truss structure.

Keel, L. H.↗
NASA NTRS2012

The Temperature and Distribution of Organic Molecules in the Inner Regions of T Tauri Disks

"High-resolution NIR spectroscopic observations of warm molecular gas emission from young circumstellar disks allow us to constrain the temperature and composition of material in the inner planet-forming region. By combining advanced data reduction algorithms with accurate modeling of the terrestrial atmospheric spectrum and a novel double-differencing data analysis technique, we have achieved very high-contrast measurements (S/N approx. 500-1000) of molecular emission at 3 microns. In disks around low-mass stars, we have achieved the first detections of emission from HCN and C2H2 at near-infrared wavelengths from several bright T Tauri stars using the CRIRES spectrograph on the Very Large Telescope and NIRSPEC spectrograph on the Keck Telescope. We spectrally resolve the line shape, showing that the emission has both a Keplerian and non-Keplerian component as observed previously for CO emission. We used a simplified single-temperature local thermal equilibrium (LTE) slab model with a Gaussian line profile to make line identifications and determine a best-fit temperature and initial abundance ratios, and we then compared these values with constraints derived from a detailed disk radiative transfer model assuming LTE excitation but utilizing a realistic temperature and density structure. Abundance ratios from both sets of models are consistent with each other and consistent with expected values from theoretical chemical models, and analysis of the line shapes suggests that the molecular emission originates from within a narrow region in the inner disk (R < 1 AU)."

Mandell, Avi↗
NASA NTRS2016

Post-Flight Assessment of Avcoat Thermal Protection System for the Exploration Flight Test-1

On December 5, 2014 NASA conducted the first flight test of its next generation human-class Orion spacecraft. The flight was called the Exploration Flight Test -1 (EFT-1) which lasted for 4 hours and culminated into a re-entry trajectory at 9 km/s. This flight test of the 5-meter Orion Crew Module demonstrated various sub-systems including the Avcoat ablative thermal protection system (TPS) on the heat shield. The Avcoat TPS had been developed from the Apollo-era recipe with a few key modifications. The engineering for thermal sizing was supported by modeling, analysis, and ground tests in arc jet facilities. This paper will describe a postlfight analysis plan and present results from post-recovery inspections, data analysis from embedded sensors, TPS sample extraction and characterization in the laboratory. After the recovery of the vehicle, a full photographic survey and surface scans of the TPS were performed. The recovered vehicle showed physical evidence of flow disturbances, varying degrees of surface roughness, and excessive recession downstream of compression pads. The TPS recession was measured at more than 200 locations of interest on the Avcoat surface. The heat shield was then processed for sample extraction prior to TPS removal using the 7-Axis Milling machine at Marshall Space Flight Center. Around 182 rectangular TPS samples were extracted for subsequent analysis and investigation. The final paper will also present results of sample analysis. The planned investigation includes sidewall imaging, followed by image analysis to characterize TPS response by quantifying different layers in the char and pyrolysis zones. A full postmortem of the instrumentation and sensor ports will also be performed to confirm no adverse effects due to the sensors themselves. A subset of the samples will undergo structural testing and perform detailed characterization of any cracks and integrity of gore seams. Finally, the material will be characterized with layer-by-layer density measurements and SEM investigations to evaluate material morphology at microstructural level including identification of elements and compounds.

Aerothermodynamics↗
NASA NTRSDate not supplied

A Microphone Phased Array for Launch Acoustics Application

A new, portable, phased array of microphones is built at NASA Ames Research Center specifically for the harsh environment encountered in launch pads of rocket vehicles. It uses 70 piezoresistive, dynamic pressure sensors, optimally distributed on a 10.5ft diameter open frame dome structure. The open frame is light yet robust to sustain the high wind load of typical seaside launch pads, and the blast and acoustic loads from the launch. A 200-ft long cable bundle carries the microphone signals to a weather-protected cabinet containing the data systems, and allows for the placement of the array tall structures. The array is equipped with an infra-red camera and a visible wavelength camera for imaging the launch pad. The beamformed noise maps will be superimposed on the video footages collected by the cameras for correct identification of the noise sources. The array is tested with very loud speaker sources to determine the beamforming ability using different schemes, such as the conventional beamforming, orthogonal-functional beamforming, and spectral element methods. A comparative study was conducted to determine the minimum attainable spatial resolution. Data from a validation test in an outdoor setup, where the array was placed on a Telehandler at several different heights and distances from either a single or a pair of speakers, is presented in this paper. The final goal is to use the array during NASA’s Artemis-II launch to determine the distribution of noise sources on the Mobile Launcher.

Acoustics↗
DOE OSTI2022

Pressure-induced creation and annihilation of Weyl points in T d - Mo 0.5 W 0.5 Te 2 and 1 T " - Mo 0.5 W 0.5 Te 2

By means of first-principles density-functional theory calculations, we investigate the role of hydrostatic pressure in the electronic structure of the T d (Pmn2 1 ) and 1T" (Pm) phases of the Weyl semimetal Mo 0.5 W 0.5 Te 2 , which is a promising material for phase-change memory technology and superconductivity. We particularly focus on changes occurring in the distribution of the gapless Weyl points (WPs) within the 0 to 45 GPa pressure range. Here we further investigate the structural phase transition and lattice dynamics of the T d and 1T" phases within the aforementioned pressure range. Our calculations suggest that both the T d and 1T" phases of Mo 0.5 W 0.5 Te 2 host four WPs in their full Brillouin zone at zero pressure. The total number of WPs increases to 44 (36) with increasing pressure via pair creation up to 20 (15) GPa for the T d (1T") phase, and beyond this pressure pair annihilation of WPs starts occurring, leaving only 16 WPs at 45 GPa in both phases. The enthalpy versus pressure data reveal that the 1T" phase is more favorable below the critical pressure of 7.5 GPa; however, beyond this critical pressure the T d phase becomes enthalpically favorable. We also provide the calculated x-ray diffraction spectra along with the calculated Raman- and infrared-active phonon frequencies to facilitate the experimental identification of the studied phases

36 MATERIALS SCIENCE↗
DOE OSTI2021

The Architecture of Area 12 Camp - Nevada's Atomic Ghost Town. An Architectural Survey of Area 12 Camp, Nevada National Security Site, Nye County, Nevada

The U.S. Department of Energy, National Nuclear Security Administration Nevada Field Office (NNSA/NFO) plans to demolish 10 buildings and one structure in Area 12 Camp at the Nevada National Security Site (NNSS) in Nye County, Nevada, to meet current and future National Weapons Science, Global and Homeland Security Program, and Environmental Management mission requirements and to reduce dangers to site workers from some of these resources. These demolitions constitute an undertaking subject to review under Title 54 U.S.C. § 306108, commonly known as Section 106 of the National Historic Preservation Act, Title 54 U.S.C. § 300101, et seq., and its implementing regulations, 36 C.F.R. Part 800. These resources were recorded and evaluated as individually ineligible for the National Register of Historic Places (National Register or NRHP) by Menocal and Shaw (2019). Upon review by the Nevada State Historic Preservation Office (SHPO) and internal review by Desert Research Institute (DRI) and NNSA/NFO staff, it was determined that these resources needed to be rerecorded and evaluated in relation to what appeared to be a National Register-eligible Historic District made up of the entirety of Area 12 Camp. Based on preliminary information, it appeared that all 11 resources would likely be contributing elements of that historic district. With that assumption in mind, recording of the entire proposed Area 12 Camp Historic District was done for three purposes. First, it would result in a definitive significance evaluation of the 11 resources in question. Second, recording of the entire camp would be offered as an appropriate mitigation measure for the demolition of these resources. Third, it would present an essential background for future management of resources within the district boundaries. Prior to the present survey, Area 12 Camp had not yet been systematically recorded; therefore, DRI surveyed an area of approximately 229 acres for architectural resources. This effort resulted in the identification, recording, and evaluation of the Area 12 Camp Historic District (SHPO Resource No. D372), including the identification of its contributing components. This district is recommended eligible for the National Register of Historic Places under Criteria A and C. It is unevaluated under Criteria B and D. The Area 12 Historic District contains 71 Principal Resources, which include landscapes, buildings, and structures. Of these resources, 69 (including 10 of the 11 resources to be removed) are recommended as contributing elements of the historic district during its period of significance corresponding to nuclear testing from 1960 through 1992. During most of the Cold War, the government reservation now called the NNSS was named the Nevada Test Site (NTS).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Identification and analysis of sugar transporters capable of co–transporting glucose and xylose simultaneously

Simultaneous co-fermentation of glucose and xylose is a key desired trait of engineered Saccharomyces cerevisiae for efficient and rapid production of biofuels and chemicals. However, glucose strongly inhibits xylose transport by endogenous hexose transporters of S. cerevisiae. We identified structurally distant sugar transporters (Lipomyces starkeyi LST1_205437 and Arabidopsis thaliana AtSWEET7) capable of co-transporting glucose and xylose from previously unexplored oleaginous yeasts and plants. Kinetic analysis showed that LST1_205437 had lenient glucose inhibition on xylose transport and AtSWEET7 transported glucose and xylose simultaneously with no inhibition. Modelling studies of LST1_205437 revealed that Ala335 residue at sugar binding site can accommodates both glucose and xylose. Docking studies with AtSWEET7 revealed that Trp59, Trp183, Asn145, and Asn179 residues stabilized the interactions with sugars, allowing both xylose and glucose to be co-transported. In addition, we altered sugar preference of LST1_205437 by single amino acid mutation at Asn365. Furthermore, our findings provide a new mechanistic insight on glucose and xylose transport mechanism of sugar transporters and the identified sugar transporters can be employed to develop engineered yeast strains for producing cellulosic biofuels and chemicals.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Reduced-Order Aerodynamic Modeling Based on CFD Frequency Responses from Multisine Inputs

A system identification analysis was performed to determine reduced-order models of a computational fluid dynamics (CFD) solver for linear aeroelastic analysis and control design. The application was to the FUN3D code and the flexible half-span wind tunnel test article, in transonic flow conditions, used in the NASA-Boeing collaboration called the Integrated Adaptive Wing Technology Maturation (IAWTM) project. Multiple inputs (structural mode displacements and control surface deflections) were simultaneously excited with orthogonal phase-optimized multisines and multiple outputs (generalized aerodynamic forces) were recorded, from which the matrix of frequency responses were computed using a single CFD run. A state-space model was then fit to the frequency response data using a maximum-likelihood estimator.

System identification↗
DOE OSTI2022

Quantum machine learning for chemistry and physics

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗