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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Rocket engine diagnostics using neural networks

Two problems in applying neural networks to fault detection and identification are (1) the complexity of the sensor data to fault mapping and (2) the lack of sufficient training data. Here, methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Second, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented. These methods were tested on a task of fault detection and identification in the Space Shuttle Main Engine (SSME). Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.

Whitehead, Bruce A.↗
DOE OSTI2020

Chemical structure and genetic organization of the E. coli O6:K15 capsular polysaccharide

Capsular polysaccharides are important virulence factors in pathogenic bacteria. Characterizing the structural components and biosynthetic pathways for these polysaccharides is key to our ability to design vaccines and other preventative therapies that target encapsulated pathogens. Many gramnegative pathogens such as Neisseria meningitidis and Escherichia coli express acidic capsules. The E. coli K15 serotype has been identifed as both an enterotoxigenic and uropathogenic pathogen. Despite its relevance as a disease-causing serotype, the associated capsular polysaccharide remains poorly characterized. We describe in this report the chemical structure of the K15 polysaccharide, based on chemical analysis and nuclear magnetic resonance (NMR) data. The repeating structure of the K15 polysaccharide consists of 4)-α-GlcpNAc-(1→5)-α-KDOp-(2→partially O-acetylated at 3-hydroxyl of GlcNAc. We also report, the organization of the gene cluster responsible for capsule biosynthesis. We identify genes in this cluster that potentially encode an O-acetyltransferase, an N-acetylglucosamine transferase, and a KDO transferase consistent with the structure we report.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1989

Analysis of flight data from a High-Incidence Research Model by system identification methods

Data partitioning and modified stepwise regression were applied to recorded flight data from a Royal Aerospace Establishment high incidence research model. An aerodynamic model structure and corresponding stability and control derivatives were determined for angles of attack between 18 and 30 deg. Several nonlinearities in angles of attack and sideslip as well as a unique roll-dominated set of lateral modes were found. All flight estimated values were compared to available wind tunnel measurements.

Batterson, James G.↗
DOE OSTI2025

Enhancing Cluster Identification in Atom Probe Tomography Data Using Transfer Learning

Atom probe tomography (APT) has enabled the direct visualization of solute clusters, providing valuable insights into material structures. This clustering is crucial for understanding the nanoscale composition and behavior of materials, which can significantly influence their mechanical and physical properties. However, the widely used clustering methods in the APT community face challenges such as subjective parametric selection and limited applicability, particularly in dealing with overlapping clusters, nested clusters, and artifacts across different scales, such as precipitates and dislocations. To address these challenges, we present a framework based on density-based cluster analysis that aims to be less dependent on user input, reproducible, and robust.

Density-based clustering↗
NASA NTRS2002

System IDentification Programs for AirCraft (SIDPAC)

A collection of computer programs for aircraft system identification is described and demonstrated. The programs, collectively called System IDentification Programs for AirCraft, or SIDPAC, were developed in MATLAB as m-file functions. SIDPAC has been used successfully at NASA Langley Research Center with data from many different flight test programs and wind tunnel experiments. SIDPAC includes routines for experiment design, data conditioning, data compatibility analysis, model structure determination, equation-error and output-error parameter estimation in both the time and frequency domains, real-time and recursive parameter estimation, low order equivalent system identification, estimated parameter error calculation, linear and nonlinear simulation, plotting, and 3-D visualization. An overview of SIDPAC capabilities is provided, along with a demonstration of the use of SIDPAC with real flight test data from the NASA Glenn Twin Otter aircraft. The SIDPAC software is available without charge to U.S. citizens by request to the author, contingent on the requestor completing a NASA software usage agreement.

Morelli, Eugene A.↗
DOE OSTI2020

Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction

The discovery of high-performing and stable materials for sustainable energy applications is a pressing goal in catalysis and materials science. Understanding the relationship between a material’s structure and functionality is an important step in the process, such that viable polymorphs for a given chemical composition need to be identified. Machine-learning-based surrogate models have the potential to accelerate the search for polymorphs that target specific applications. Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium oxide polymorphs of IrO 2 and IrO 3 . The search is coupled to a subsequent analysis of the electrochemical stability of the discovered structures for the acidic oxygen evolution reaction (OER). Structural candidates are generated by identifying all 956 structurally unique AB2 and AB3 prototypes in existing materials databases (more than 38000). Next, using an active learning approach, we find 196 IrO 2 polymorphs within the thermodynamic amorphous synthesizability limit and reaffirm the global stability of the rutile structure. We find 75 synthesizable IrO 3 polymorphs and report a previously unknown FeF 3 -type structure as the most stable, termed α-IrO 3 . To test the algorithms performance, we compare to a random search of the candidate space and report at least a 2-fold increase in the rate of discovery. Additionally, the AL approach can acquire the most stable polymorphs of IrO 2 and IrO 3 with fewer than 30 density functional theory optimizations. Analysis of the structural properties of the discovered polymorphs reveals that octahedral local coordination environments are preferred for nearly all low-energy structures. Subsequent Pourbaix Ir–H 2 O analysis shows that α-IrO3 is the globally stable solid phase under acidic OER conditions and supersedes the stability of rutile IrO 2 . Calculation of theoretical OER surface activities reveal ideal weaker binding of the OER intermediates on α-IrO 3 than on any other considered iridium oxide. We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal oxide structure with a defined stoichiometry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Phase Identification in Synchrotron X-ray Diffraction Patterns of Ti–6Al–4V Using Computer Vision and Deep Learning

X-ray diffraction patterns contain information about the atomistic structure and microstructure (defect population) of materials, extracting detailed information from diffraction patterns is complex, demanding and relies on prior knowledge. Here, we hypothesize that deep-learning techniques can help to perform an effective and accurate analysis with high throughput rates. To demonstrate this concept, we applied a novel deep learning framework to determine the evolution of the β-phase volume fraction in a Ti–6Al–4V alloy during heat-treatment from video sequences of 2D diffraction patterns recorded in transmission and with highly monochromatic radiation in a synchrotron beamline. In particular, we studied the impact of network design on prediction reliability and computational performance. Networks of different architectures were trained using 3008 experimental 2D patterns. A well-tuned model was found to reproduce the phase fractions of another experimental data set, consisting of 1100 diffraction patterns, with a mean-square error as small as 2.6 x 10 -4 . The average prediction error of β-phase volume fraction was within 1.6 x 10 -2 (in each diffraction pattern) of the values obtained by conventional methods. Our work demonstrates that convolutional neural networks can evaluate high energy X-ray diffraction patterns with a remarkable level of reliability. Furthermore, it demonstrates the significance of network design on the reliability of predictions and computational performance. The most complex models do not necessarily result in highest accuracy and may even fail to learn from the data.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Identification of a second-order mechanical system model from a state space realization

Many system identification algorithms produce models in modern state variable form. When a mechanical system is identified one knows that there must be a model in second order form. In structural dynamics one is interested in either a modal model of the system, or in a model including a mass matrix, stiffness matrix and a damping matrix. In this paper, algorithms are developed that convert a modern state space realization into the above two representations of interest in mechanical systems. The algorithm can be used with any identification methods that produce a modern state variable representation such as the Eigensystem Realization Algorithm (ERA), its modified version by data correlation (ERA/DC), and combinations of these with Observer/Kalman Filter Identification (OKID). An algorithm is developed that allows one to identify the damping matrix in a model representation, so that one determines how near to modal the damping is in the system, and can therefore understand the degree to which the modes are coupled by the damping in the system. A second algorithm produced the mass, damping, and stiffness matrices, given the input and output matrices. It is assumed that the number of sensors (or the number of actuators) is greater than or equal to the number of modes in the system, and it is proved that this is a necessary condition to be able to uniquely identify these matrices. Experience with examples suggests that a singular value truncation involved in these algorithms can help one determine the true system order. This truncation has the benefit of knowledge of the correct form for a mechanical system model, which is not present in the modern control identification algorithm.

Tseng, Dong-Huei↗
DOE OSTI2021

Identification of a low-energy metastable 1 T -type phase for monolayer VSe 2

Elucidating the polymorphism of transition metal dichalcogenide layers and the interplay between structure and properties is a key challenge for the application of these materials. We identify a low-energy metastable phase of monolayer VSe 2 and elucidate its magnetic and electronic properties. This structure is distinct from the previously identified charge density wave (CDW) phase. However, while having rather distinct properties it is very close in energy to the CDW phase and is likely to be realized in experiments. Importantly, local bonding instabilities, as characterized by reconstruction of the electronic structure over a wide energy range, are important for this distortion, which includes both V off-centering in the octahedral coordination cages and a partial disproportionation into two distinct types of V. The phase does not have a ferromagnetic ground state. Furthermore, the results show that the physics of 1T–VSe 2 are richer than previously known with an interplay of Fermi surface instabilities and local bonding effects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Highly accelerated life testing (HALT): A review from a statistical perspective

Despite its use in one form or another for at least four decades, HALT and related techniques [e.g., highly accelerated-stress screening (HASS) and stress audits (HASA)] are not well understood within the statistical community and remain controversial. This largely reflects a conflict in motivation between engineers, testing under harsh conditions to discover and eliminate failure modes, and statisticians, taking a more cautious approach to develop quantitative estimates of parameters such as mean time between failures (MTBF). Here, this review article will clarify HALT concepts and methods and explain where it fits within the universe of methods that involve the application of accelerating factors to compress the time required to evaluate or enhance product reliability. A major distinction is between methods such as HALT, a high-stress test-analyze-fix-test iterative process directed at improving reliability by discovering and fixing weak points in a design, and quantitative accelerated life testing (QALT), whose goal is the estimation of product life for a fixed design. We discuss methods such as physics of failure that offer some hope of bridging the gap between the qualitative nature of HALT, and purely quantitative statistical methods. We present a variety of engineering applications of HALT including metal fatigue, piping and pressure vessels, structural damage, radiation damage, and rotating machinery. We also discuss potential synergies between HALT and QALT, such as rapid identification, through HALT, of failure modes requiring quantitative analysis. For further study, extensive references to the applicable literature are provided as well as an appendix that describes related methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Mapping Neutralizing and Immunodominant Sites on the SARS-CoV-2 Spike Receptor-Binding Domain by Structure-Guided High-Resolution Serology

We report analysis of the specificity and kinetics of neutralizing antibodies (nAbs) elicited by SARS-CoV-2 infection is crucial for understanding immune protection and identifying targets for vaccine design. In a cohort of 647 SARS-CoV-2-infected subjects, we found that both the magnitude of Ab responses to SARS-CoV-2 spike (S) and nucleoprotein and nAb titers correlate with clinical scores. The receptor-binding domain (RBD) is immunodominant and the target of 90% of the neutralizing activity present in SARS-CoV-2 immune sera. Whereas overall RBD-specific serum IgG titers waned with a half-life of 49 days, nAb titers and avidity increased over time for some individuals, consistent with affinity maturation. We structurally defined an RBD antigenic map and serologically quantified serum Abs specific for distinct RBD epitopes leading to the identification of two major receptor-binding motif antigenic sites. Our results explain the immunodominance of the receptor-binding motif and will guide the design of COVID-19 vaccines and therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Machine-Learning-Driven Discovery of Water Splitting BaFe 2 O 4 and Human-in-the-Loop Improvement via Al-Substitution for Increased Thermal Stability

Thermochemical hydrogen (TCH) production offers a promising method for converting thermal energy into hydrogen fuel through heat-driven redox cycles of metal oxides. Here, in this work a defect graph neural network (dGNN) was used to predict oxygen vacancy formation energies ΔH V O combined with Materials Project predictions of oxygen chemical potential stability to screen candidate oxides via high-throughput database analysis. BaFe 2 O 4 was identified as a promising material for experimental validation based on its predicted ΔH V O , oxygen chemical potential stability range, and potential for tunable substitutions to improve thermal properties. Experimental validation using thermogravimetric analysis (TGA), stagnation flow reactor (SFR), X-ray diffraction (XRD), and electron microscopy confirmed positive water-splitting behavior but also revealed limitations in thermal stability under aggressive reduction conditions. To address this, a human-in-the-loop modification strategy was employed introducing Al substitution in BaFe 2–x Al x O 4 ; this modification improves thermal stability, alters the crystal structure and enhances overall performance. These results demonstrate a combined computational and experimental workflow in which machine learning accelerates identification of promising candidates, while targeted experimental design enables optimization of functional performance. This approach advances the development of robust, cost-effective TCH materials and highlights the importance of integrating data-driven discovery with human-guided materials design in paving the way for scalable hydrogen production technologies.

organic↗
DOE OSTI2025

Survey of Thirteen Novel Pseudomonas putida Bacteriophages

Bacteriophages have been widely investigated as a promising treatment of food, medical equipment, and humans colonized by antibiotic-resistant bacteria. Phages pose particular interest in combating those bacteria which form biofilms, such as the medically important human pathogen Pseudomonas aeruginosa and several plant pathogens, including P. syringae . In an undergraduate lab course, P. putida was used as the host to isolate novel anti-pseudomonal bacteriophages. Environmental samples of soil and water were collected, and purified phage isolates were obtained. After Illumina sequencing, genomes of these phages were assembled de novo and annotated. Assembled genomes were compared with known genomes in the literature and GenBank to identify taxonomic relations and to refine their functional annotations. The thirteen phages described are sipho-, myo-, and podoviruses in several families of Caudoviricetes , spanning several novel genera, with genomes ranging from 40,000 to 96,000 bp. One phage (DDSR119) is unique and is the first reported P. putida siphovirus. The remaining 12 can be clustered into four distinct groups. Six are highly related to each other and to previously described Autotranscriptaviridae phages: Waldo5, PlaquesPlease, and Laces98 all belong to the Waldovirus genus, whereas Stalingrad, Bosely, and Stamos belong to the Troedvirus genus. Zuri was previously classified as the founding member of a new genus Zurivirus within the family Schitoviridae . Ebordelon and Holyagarpour each represent different species within Zurivirus , whereas Meara is a more distantly related member of the Schitoviridae . Dolphis and Jeremy are similar enough to form a genus but have only a few distant relatives among sequenced phages and are notable for being temperate. We identified the lysis cassettes in all 13 phages, compared tail spike structures, and found auxiliary metabolic genes in several. Studies like these, which isolate and characterize infectious virions, enable the identification of novel proteins and molecular systems and also provide the raw materials for further study, evaluation, and manipulation of phage proteins and their hosts.

Pseudomonas putida↗
NASA NTRS1983

Control - Demands mushroom as station grows

The NASA space station, which is presently in the planning stage, is to be composed of both rigid and nonrigid modules, rotating elements, and flexible appendages subjected to environmental disturbances from the earth's atmospheric gravity gradient, and magnetic field, as well as solar radiation and self-generated disturbances. Control functions, which will originally include attitude control, docking and berthing control, and system monitoring and management, will with evolving mission objectives come to encompass such control functions as articulation control, autonomous navigation, space traffic control, and large space structure control. Attention is given to the advancements in modular, distributed, and adaptive control methods, as well as system identification and hardware fault tolerance techniques, which will be required.

Szirmay, S. Z.↗
NASA NTRS1985

Operating characteristics of a prototype high energy gamma-ray telescope

The field of gamma-ray astronomy in the energy range from ten to several hundred MeV is severely limited by the angular resolution that can be achieved by present instruments. The identification of some of the point sources found by the COS-B mission and the resolution of detailed structure existing in those sources may depend on the development of a new class of instrument. The coded aperture mask telescope, used successfully at X-ray energies hold the promise of being such an instrument. A prototype coded aperture telescope was operated in a tagged photon beam ranging in energy from 23 to 123 MeV. The purpose of the experiment was to demonstrate the feasibility of operating a coded aperture mask telescope in this energy region. Some preliminary results and conclusions drawn from some of the data resulting from this experiment are presented.

Jenkins, T. L.↗
NASA NTRS1987

Solar events and their influence on the interplanetary medium

Aspects of a workshop on Solar events and their influence on the interplanetary medium, held in September 1986, are reviewed, the goal of which was to foster interactions among colleagues, leading to an improved understanding of the unified relationship between solar events and interplanetary disturbances. The workshop consisted of three working groups: (1) flares, eruptives, and other near-Sun activity; (2) coronal mass ejections; and (3) interplanetary events. Each group discussed topics distributed in advance. The flares-eruptives group members agreed that pre-event energy is stored in stressed/sheared magnetic fields, but could not agree that flares and other eruptive events (e.g., eruptive solar prominences) are aspects of the same physical phenomenon. In the coronal mass ejection group, general agreement was reached on the presence of prominences in CMEs, and that they have a significant three-dimensional structure. Some topics identified for further research were the aftermath of CMEs (streamer deflections, transient coronal holes, possible disconnections), identification of the leading edge of CMEs, and studies of the range and prevalence of CME mass sizes and energies.

Joselyn, Joann↗
NASA NTRS1990

The submillimeter-wave spectrum of (O-16)(O-18)

In order to aid in the interstellar identification of molecular oxygen via its (O-16)(O-18) isotopic species, the frequencies of four rotational fine-structure lines of (O-16)(O-18) have been measured at frequencies ranging from 298 to 464 GHz. The line with the highest intensity and consequent best chance of astronomical detection lies at 401739.823 MHz. Its magnetic dipole strength is somewhat more than half that of the previously measured strong line at 233946.179 MHz.

Crownover, Richard L.↗