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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles

Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42,400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

Algorithmic Classification of Raman Spectra Biosignatures: Improving Life Detection Confidence

“Agnostic” biosignatures – indicators of life (or the absence of life), independent of a particular biochemistry – are increasingly considered a high standard for life detection. The Ladder of Life Detection (2018) called for investigating how combinations of independent and different potential biosignatures affect confidence. To address this gap, statistical classification of elemental abundances, isotopic fractionation, and reflectance spectroscopy (VNIR) has been implemented. Raman spectroscopy, highly desirable due to its wide availability, has the potential to improve this predictive power. This work implemented biosignature classification algorithms on Raman data alone, in preparation for combination with the other data types. Raman spectroscopy data was collected from published databases and papers as part of a manually curated dataset of “indicative” and “non-indicative of life” samples. These currently include 61 non-indicative samples (meteorites, magnetite); 3 indicative living samples (bacteria); 20 indicative non-living samples (chalk, bone); and 12 indicative mixed (with non-indicative material) samples (soil, microbial mats). Laboratory work is ongoing to characterize additional samples, particularly a greater breadth of mixed systems. Spectra were interpolated, filtered with the Savitzsky-Golay filter, and de-noised. For a preliminary examination, agnostic features were manually extracted including mean intensity, number of peaks, and mean peak width. Different peak prominences and filtering polynomials were used to refine features. Classification algorithms were implemented: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), random forest (RF), Gaussian naïve bayes (GNB). Lastly, Monte Carlo simulations on 1,000 50%-train-test-splits were used to validate classification performance and feature significance. The preliminary feature set achieved its highest AUC of 0.52 with LR, with no strongly discriminatory features. Work to improve feature extraction, such as through deep learning with back propagation, is planned. In future work, the Raman data will be combined with the other data types, and potentially new data types such as enantiomeric excess. This project was partially supported through the NASA Ames Project EXcellence (APEX) incubator program.

Astrobiology↗
DOE OSTI2020

Tunable valleytronics with symmetry-retaining high polarization degree in SnS x Se 1–x model system

SnS has recently been shown to possess unique valleytronic capability with a large polarization degree, where non-degenerate valleys can be accessed using linearly polarized light, bestowed upon by the unique anisotropy and wavefunction symmetry. It is thus of utmost importance to demonstrate the extension of such effects for the IV–VI system in general, thereby elucidating the generality and tunability of such valleytronics. In this study, we show the highly tunable valleytronics via gradual compositional control of the tin(II) sulfo-selenide (SnS x Se 1–x ) alloy system with excellent retainment of symmetry-determined selection rules. We show the presence of both ΓY and ΓX valleys in all alloy compositions via selectivity in absorption and emission of linearly polarized light by optical reflection (R)/transmission (T) and photoluminescence measurements and tuned the bandgaps of the valleys within a range of 1.28 eV–1.05 eV and 1.48 eV–1.24 eV, respectively. This simultaneous tuning of non-degenerate valleys agrees well with theoretical calculations. We then fitted the bandgap values in compositional space, obtaining bowing parameters as a useful database. We further demonstrated the feasibility of using IV–VI valleytronics systems in general by elucidating the retainment of strong polarization degrees of as high as 91% across all compositions. The generalization of such purely symmetry-dependent valleytronics also opens up opportunities for the discovery of more multi-functional materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Two-Phase Equation of State for Al 2 O 3

Two-phase equation of state (EOS) for AI 2 O 3 (alumina) is calibrated using the experimental/simulation calibration data uploaded to the caldata repository. The two phases are solid α-Al 2 O 3 (corundum, sapphire, ruby) and its melt. The enthalpy of formation is set to that available from the NIST-JANAF database at T = 0K, P =1 bar. The expected applicability range of the EOS is from the ambient conditions to temperatures of ~ 5000 K and pressures of ~ 50-100 GPa.

36 MATERIALS SCIENCE↗
DOE OSTI2020

FY20 Progress Report on FIPD Development and User Support

The DOE Advanced Reactor Technologies program has supported efforts to recover and preserve metallic fuel data generated throughout the past fast reactor R&D programs. Those efforts have focused on establishing databases for information from the experiments conducted during the Integral Fast Reactor program including data generated at EBR-II, FFTF, and TREAT reactors, as well as out of pile transient testing data. These databases are essential for future-licensing activities of metallic fuel based advanced fast reactors. This report describes the EBR-II Fuels Irradiation & Physics Database (FIPD) development, content updates, and user support during the current fiscal year (FY20). Future plans are also provided.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2004

Safety Aboard the International Space Station

As with any task that NASA takes on, safety is of utmost importaqce. There are pages of safety codes and procedures that must be followed before any idea can be brought to life. Unfortunately, the International Space Station s (ISS) safety regulations and procedures are based on lg standards rather than on Og. To aide in making this space age home away from home a less hazardous environment, I worked on several projects revolving around the dangers of flammable items in microgravity. The first task I was assigned was to track flames. This involves turning eight millimeter video recordings, of tests run in the five second drop tower, into avi format on the computer. The footage is then compressed and altered so that the flame can be seen more clearly. Using another program called Spotlight, line profiles were used to collect data describing the luminescence of the flame at different points. These raw data are saved as text files and run trough a macro so that a Matlab program can analyze it. By fitting the data to a curve and determining the areas of brightest luminescence, the behavior of the flame can be recorded numerically. After entering the data into a database, researchers can come back later and easily get information on flames resulting from different gas and liquid mixtures in microgravity. I also worked on phase two of the FATE project, which deals with safety aboard the ISS. This phase involves igniting projected droplets and determining how they react with secondary materials. Such simulations represent, on a small scale, the spread of onboard fires due to the effervescence of burning primary materials. I set up existing hardware to operate these experiments and ran tests with it, photographing the results. I also made CAD drawings of the apparatus and the area available on the (SF)2 rig for it to fit into. The experiment will later be performed on the KC-135, and the results gathered will be used to reanalyze current safety standards for the ISS, including the distance of required separation for flammable materials. Additional information is included in the original extended abstract.

Mintz, Shauna M.↗
NASA NTRS2011

Space Shuttle Systems Engineering Processes for Liftoff Debris Risk Mitigation

This slide presentation reviews the systems engineering process designed to reduce the risk from debris during Space Shuttle Launching. This process begins the day of launch from the tanking to the vehicle tower clearance. Other debris risks (i.e., Ascent, and micrometeoroid orbital debit) are mentioned) but are not the subject of this presentation. The Liftoff debris systems engineering process and an example of how it works are reviewed (i.e.,STS-119 revealed a bolt liberation trend on the Fixed Service Structure (FSS) 275 level elevator room). The process includes preparation of a Certification of Flight Readiness (CoFR) that includes (1) Lift-off debris from previous mission dispositioned, (2) Flight acceptance rationale has been provided for Lift-off debris sources/causes (3) Lift-off debris mission support documentation, processes and tools are in place for the up-coming mission. The process includes a liftoff debris data collection that occurs after each launch. This includes a post launch walkdown, that records each liftoff debris, and the entry of the debris into a database, it also includes a review of the imagery from the launch, and a review of the instrumentation data. There is also a review of the debris transport analysis process, that includes temporal and spatial framework and a computational fluid dynamics (CFD) analysis. which incorporates a debris transport analyses (DTA), debris materials and impact tests, and impact analyses.

Mitchell, Michael↗
NASA NTRS2012

Evaluation of Wall Boundary Conditions for Impedance Eduction Using a Dual-Source Method

The accuracy of the Ingard-Myers boundary condition and a recently proposed modified Ingard-Myers boundary condition is evaluated for use in impedance eduction under the assumption of uniform mean flow. The evaluation is performed at three centerline Mach numbers, using data acquired in a grazing flow impedance tube, using both upstream and downstream propagating sound sources, and on a database of test liners for which the expected behavior of the impedance spectra is known. The test liners are a hard-wall insert consisting of 12.6 mm thick aluminum, a linear liner without a facesheet consisting of a number of small diameter but long cylindrical channels embedded in a ceramic material, and two conventional nonlinear liners consisting of a perforated facesheet bonded to a honeycomb core. The study is restricted to a frequency range for which only plane waves are cut on in the hard-wall sections of the flow impedance tube. The metrics used to evaluate each boundary condition are 1) how well it educes the same impedance for upstream and downstream propagating sources, and 2) how well it predicts the expected behavior of the impedance spectra over the Mach number range. The primary conclusions of the study are that the same impedance is educed for upstream and downstream propagating sources except at the highest Mach number, that an effective impedance based on both the upstream and downstream measurements is more accurate than an impedance based on the upstream or downstream data alone, and that the Ingard-Myers boundary condition with an effective impedance produces results similar to that achieved with the modified Ingard-Myers boundary condition.

Watson, W. R.↗
NASA NTRS2015

Using the Shuttle In Situ Window and Radiator Data for Meteoroid Measurements

Every time NASA's Space Shuttle flew in orbit, it was exposed to the natural meteoroid and artificial debris environment. NASA Johnson Space Center maintains a database of impact cratering data of 60 Shuttle missions flown since the mid‐1990's that were inspected after flight. These represent a total net exposure time to the space environment of 2 years. Impact damage was recorded on the windows and radiators, and in many cases information on the impactor material was determined by later analysis of the crater residue. This information was used to segregate damage caused by natural meteoroids and artificial space debris. The windows represent a total area of 3.565 sq m, and were capable of resolving craters down to about 10 micrometers in size. The radiators represent a total area of 119.26 sq m, and saw damage from objects up to approximately 1 mm in diameter. These data were used extensively in the development of NASA's ORDEM 3.0 Orbital Debris Environment Model, and gives a continuous picture of the orbital debris environment in material type and size ranging from about 10 micrometers to 1 mm. However, the meteoroid data from the Shuttles have never been fully analyzed. For the orbital debris work, special "as flown" files were created that tracked the pointing of the surface elements and their shadowing by structure (such as the ISS during docking). Unfortunately, such files for the meteoroid environment have not yet been created. This talk will introduce these unique impact data and describe how they were used for orbital debris measurements. We will then discuss some simple first‐order analyses of the meteoroid data, and point the way for future analyses.

Matney, Mark↗
DOE OSTI2025

Accurate segmentation of localized corrosion in structural alloys via deep learning

This study presents a deep learning-based approach for the automated segmentation of corrosion damage in scanning electron microscopy (SEM) images. The proposed method enables rapid and accurate segmentation of corrosion features in these SEM images, making it highly suitable for real-time applications such as automated microscopy. Specifically, a dedicated corrosion segmentation database tailored for this task is constructed. The newly constructed dataset, alongside data from two public databases, are employed to jointly train a deep learning-based model modified with a texture refinement module. Compared to the same model without the texture refinement module, the refined model substantially enhances the efficacy and efficiency of corrosion segmentation. Furthermore, the methodology developed here is extendable to segmentation tasks for other materials with similar resolution, texture, and contrast characteristics, thereby paving the way for accelerated and automated analysis in corrosion science and beyond.

Artificial Intelligence↗
NASA NTRS1993

X-ray properties of normal galaxies

In this paper I will first review briefly the results of the X-ray observations of early-type galaxies with the HEAO 2 (Eisnstein) satellite, published in the past ten years. I will then concentrate on more recent results stemming from a systematic re-analysis of the Einstein galaxy database, and I will also report on some very preliminary ROSAT results. Parts of this talk were given at the ESO Elba meeting on early-type galaxies in May 1992 and therefore there is a considerable overlap between this paper and that in the proceedings of that meeting. Some of this material was also reviewed by Fabbiano and Kim (1992) and Fabbiano (1992b).

Fabbiano, G.↗
DOE OSTI2021

Corrosion of rebar in concrete. Part III: Artificial Neural Network analysis of chloride threshold data

Active corrosion of carbon steel in reinforced concrete occurs when the chloride ion concentration exceeds the chloride threshold (CT). CT is affected by many physical and chemical parameters including primary variables (pH, corrosion potential, breakdown potential, and temperature) and secondary variables (cemenet composition, concreete porosity, and water/cement ratio). A Kohonen-self organized map (KSOM) and regression artificial neural network (ANN) coupled methodology was developed to find the missing values of independent variables in the sparse database and for quantitatively evaluating the effects of these variables on CT values that are expressed in %TotalCl/cem (or %FreeCl/cem), and [Cl - ]/[OH - ].

36 MATERIALS SCIENCE↗
DOE OSTI2024

Computational thermodynamics-guided alloy design and phase stability in CoCrFeMnNi-based medium-and high-entropy alloys: An experimental-theoretical study

A computational thermodynamics approach has been employed to design CoCrFeMnNi-based medium- and high-entropy alloys (M/HEAs) with systematically varied compositions (Co(( 80-X )/ 2 )Cr(( 80-X )/ 2 )Fe X Mn 10 Ni 10 with x = 30, 40, and 50 at.%) and phase stability. Since the formation of sigma phase, usually brittle and undesirable, is a common concern, when this class of alloys is subjected to elevated temperatures (600–1000 °C), predicting its formation becomes essential. Thus, its formation and the phase equilibria were studied using the CALPHAD method, and two empirical methods, namely, valence electron concentration (VEC) and paired sigma-forming element (PSFE). Isothermal aging treatments at 900–1100 °C for 20 h were performed, since CALPHAD and VEC/PSFE predictions diverged. Both prediction methods were compared with experimental characterization by a combination of scanning electron microscopy and high-energy synchrotron X-ray diffraction. In conclusion, the predictions from the VEC/PSFE and CALPHAD calculations (depending on the database used) were shown to be quite accurate.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Evaluating MetGlasTM Solderability with Tin-Silver-Copper and Tin-Silver-Bismuth Solder Alloys

The solderability of MetGlas TM (subsidiary of Hitachi Metals, America Ltd) 2826 MB, a rapidly solidified metallic foil, was evaluated by the meniscus height/wetting force method for tin-silver-copper (SnAgCu) and tin-silver-bismuth (SnAgBi) solders to understand the effects of the extreme non-equilibrium condition of the MetglasTM surface on solderability performance. Of the variables studied here (solder temperature, heat treatment, and solder composition), solder composition had the largest impact on contact angle. Flux and foil composition remained constant throughout; but, these factors would also be predicted to significantly affect solderability. A greater understanding of the manner whereby non-equilibrium cooling affects solderability of these foils will broaden the application of soldering technology of structures fabricated by rapid cooling process (i.e. additively manufactured coatings and parts). Developing a robust database for Pb-free solderability behavior is also necessary, as industry transitions from tin-lead (SnPb) to lead free (Pb-free) solders.

36 MATERIALS SCIENCE↗
DOE OSTI2025

OmniXAS: A universal deep-learning framework for materials x-ray absorption spectra

X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents significant challenges, often requiring extensive, computationally intensive simulations, as well as significant domain expertise. These limitations hinder the development of fast, robust XAS analysis pipelines that are essential in high-throughput studies and for autonomous experimentation. Here, we address these challenges with OmniXAS, a framework that contains a suite of transfer learning approaches for XAS prediction, each uniquely contributing to improved accuracy and efficiency, as demonstrated on the K-edge spectra database covering eight 3⁢d transition metals (Ti–Cu). The OmniXAS framework is built upon three distinct strategies. First, we use M3GNet [Nat. Comput. Sci. 2, 718 (2022)] to derive latent representations of the local chemical environment of absorption sites as input for XAS prediction, achieving significant improvements over conventional featurization techniques. Second, we employ a hierarchical transfer learning strategy, training a universal multitask model across elements before fine-tuning for element-specific predictions. Models based on this cascaded approach after elementwise fine-tuning outperform element-specific models by up to 69%. Third, we implement cross-fidelity transfer learning, adapting a universal model to predict spectra generated by simulation of a different fidelity with a much higher computational cost. This approach improves prediction accuracy by up to 11% over models trained on the target fidelity alone. Our approach significantly boosts the throughput of XAS modeling by orders of magnitude as compared to first-principles simulations and is extendable to XAS prediction for a broader range of elements. The proposed transfer learning framework is generalizable to enhance deep-learning models that target other properties in materials research.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Hypervelocity Impact Physics

All large spacecraft are susceptible to impacts by meteoroids and orbiting space debris. These impacts occur at extremely high speed and can damage flight-critical systems, which can in turn lead to a catastrophic failure of the spacecraft. Therefore, the design of a spacecraft for a long-duration mission must take into account the possibility of such impacts and their effects on the spacecraft structure and on all of its exposed subsystems components. The work performed under the contract consisted of applied research on the effects of meteoroid/space debris impacts on candidate materials, design configurations, and support mechanisms of long term space vehicles. Hypervelocity impact mechanics was used to analyze the damage that occurs when a space vehicle is impacted by a micrometeoroid or a space debris particle. An impact analysis of over 500 test specimens was performed to generate by a hypervelocity impact damage database.

William P Schonberg↗
DOE OSTI2023

Explainable machine learning for hydrogen diffusion in metals and random binary alloys

Hydrogen diffusion in metals and alloys plays an important role in the discovery of new materials for fuel cell and energy storage technology. While analytic models use hand-selected features that have clear physical ties to hydrogen diffusion, they often lack accuracy when making quantitative predictions. Machine learning models are capable of making accurate predictions, but their inner workings are obscured, rendering it unclear which physical features are truly important. To develop interpretable machine learning models to predict the activation energies of hydrogen diffusion in metals and random binary alloys, we create a database for physical and chemical properties of the species and use it to fit six machine learning models. Our models achieve root-mean-squared errors between 98–119 meV on the testing data and accurately predict that elemental Ru has a large activation energy, while elemental Cr and Fe have small activation energies. By analyzing the feature importances of these fitted models, we identify relevant physical properties for predicting hydrogen diffusivity. While metrics for measuring the individual feature importances for machine learning models exist, correlations between the features lead to disagreement between models and limit the conclusions that can be drawn. Instead grouped feature importance, formed by combining the features via their correlations, agree across the six models and reveal that the two groups containing the packing factor and electronic specific heat are particularly significant for predicting hydrogen diffusion in metals and random binary alloys. In conclusion, this framework allows us to interpret machine learning models and enables rapid screening of new materials with the desired rates of hydrogen diffusion.

36 MATERIALS SCIENCE↗