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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Crystallography companion agent for high-throughput materials discovery

The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone and impossible to scale. With the advent of autonomous robotic scientists or self-driving laboratories, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which outputs probabilistic classifications—rather than absolutes—to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering substantial time savings. It is demonstrated on a diverse set of organic and inorganic materials characterization challenges. This method is directly applicable to inverse design approaches and robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

36 MATERIALS SCIENCE↗
DOE OSTI2021

bnl/pub-Maffettone_2020_08

The application of the XCA package as first demonstrated in aXiv:2008.00283. ABSTRACT: The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time consuming, error prone, and impossible to scale. With the advent of autonomous robotic scientists or self-driving labs, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which output probabilistic classifications --- rather than absolutes --- to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering unprecedented time savings, and is demonstrated on a diverse set of organic and inorganic materials challenges. This innovation is directly applicable to inverse design approaches, robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

Maffettone, PhillipM [Brookhaven National Lab. (BN↗
DOE OSTI2023

Parametric Study of the Vacuum Permeator for the Tritium Extraction eXperiment

Tritium breeding is a critical component of any self-sustaining future fusion reactor. The liquid metal eutectic, PbLi, is of particular interest as a tritium breeder material due to its favorable thermophysical and neutronic properties. One of the several remaining challenges facing PbLi breeder blankets is the need to design and validate a highly efficient tritium extraction system. The vacuum permeator is a promising extraction concept that utilizes tritium permeation through a highly permeable metal membrane. The Tritium Extraction eXperiment (TEX) is a forced-convection PbLi loop constructed to investigate tritium extraction from PbLi with vacuum permeators. Accurate thermal-hydraulic and tritium transport models are required to establish appropriate test matrices, predict experiment outcomes, and analyze data. However, the hydrogen transport properties of PbLi and permeator materials have large uncertainties. A database is collected, and a parametric analysis is conducted on the effect of hydrogen transport material properties: diffusivity of H in PbLi and permeator, solubility of H in PbLi and permeator, and permeator surface recombination constant on the expected tritium extraction efficiency for a vacuum permeator installed in TEX. As a result, we observe solubility of H in PbLi and the permeator and the recombination constant of the permeator have the largest effect on the extraction efficiency.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

An AI-accelerated pathway for reproducible and stable halide perovskites

Halide perovskites (HPs) have remarkable optoelectronic properties, and in the last decade their photovoltaic power conversion efficiency and light-emitting diode efficiency have skyrocketed. Despite the surge in research on these burgeoning materials, two key challenges in the field remain: material irreproducibility and instability. Their behavior is especially dynamic in response to environmental stressors, due to complex interactions with the perovskite crystal lattice. Here, in this review, we survey the latest achievements in HP materials research accomplished with the assistance of artificial intelligence (AI), through the implementation of automated experimentation and machine learning (ML) data analysis. Automated synthesis and characterization tackle problems with material irreproducibility by systematically controlling parameters with very high precision, creating massive datasets, and allowing methodical comparisons from which unbiased conclusions can be drawn. AI can reveal otherwise unnoticed trends, inform future experiments with the highest potential information gain, and forecast future performance. The review concludes with a forward viewpoint of how human-assisted closed-loop laboratories and shared databases allow halide perovskite materials’ processing, properties, and performance to be potentially optimized with AI, accelerating the development of highly reproducible and stable optoelectronic devices.

Hering, Abigail R. [Univ. of California, Davis, CA↗
NASA NTRS2004

Information Technology Support in the 8000 Directorate

My summer internship was spent supporting various projects within the Environmental Management Office and Glenn Safety Office. Mentored by Eli Abumeri, I was trained in areas of Information Technology such as: Servers, printers, scanners, CAD systems, Web, Programming, and Database Management, ODIN (networking, computers, and phones). I worked closely with the Chemical Sampling and Analysis Team (CSAT) to redesign a database to more efficiently manage and maintain data collected for the Drinking Water Program. This Program has been established for over fifteen years here at the Glenn Research Center. It involves the continued testing and retesting of all drinking water dispensers. The quality of the drinking water is of great importance and is determined by comparing the concentration of contaminants in the water with specifications set forth by the Environmental Protection Agency (EPA) in the Safe Drinking Water Act (SDWA) and its 1986 and 1991 amendments. The Drinking Water Program consists of periodic testing of all drinking water fountains and sinks. Each is tested at least once every 2 years for contaminants and naturally occurring species. The EPA's protocol is to collect an initial and a 5 minute draw from each dispenser. The 5 minute draw is what is used for the maximum contaminant level. However, the CS&AT has added a 30 second draw since most individuals do not run the water 5 minutes prior to drinking. This data is then entered into a relational Microsoft Access database. The database allows for the quick retrieval of any test@) done on any dispenser. The data can be queried by building number, date or test type, and test results are documented in an analytical report for employees to read. To aid with the tracking of recycled materials within the lab, my help was enlisted to create a database that could make this process less cumbersome and more efficient. The date of pickup, type of material, weight received, and unit cost per recyclable. This information could then calculate the dollar amount generated by the recycling of certain materials. This database will ultimately prove useful in determining the amounts of materials consumed by the lab and will help serve as an indicator potential overuse.

FROM↗
DOE OSTI2025

Assessing the hygrothermal performance of bio-based materials in building wall systems

Building envelope systems are crucial in regulating thermal and moisture exchange between interior and exterior environments, accounting for approximately 28 % of building energy consumption in the United States with walls being the primary contributors. Improper selection of building envelope materials can lead to moisture-related issues, reduced resilience, and compromised durability. Hygrothermal performance assessment is a key factor in efficient building design. As such, improving the energy and hygrothermal performance of opaque wall materials, through careful assessment of material choices, is essential to enhancing building resilience, lowering energy costs, and improving occupant comfort. As the building industry seeks new strategies to reduce material energy intensity, bio-based materials emerge as a promising solution. However, their long-term hygrothermal performance in building envelope systems remains underexplored. To fill this gap, this study evaluates the hygrothermal behavior of 13 bio-based materials in residential wall systems across three U.S. climate zones. Laboratory experiments were performed to measure material properties such as density, thermal conductivity, moisture transmission, and sorption isotherms. These data were integrated into the WUFI® simulation tool to assess wall hygrothermal performance in Houston, Baltimore, and Chicago. A three-phase modeling approach was used: (1) baseline residential walls with oriented strand board (OSB) and gypsum board; (2) replacing OSB with bio-based materials; and (3) replacing drywall with bio-based materials. Results showed that the evaluated bio-based materials maintained acceptable moisture thresholds of ≤ 16 % across all climates, confirming their viability as an alternative for current sheathing materials. Furthermore, this study provides a foundation for future research and innovation in material science on the use of certain bio-based materials in high-performance, low energy use residential construction. Ultimately, providing critical data, offering a database of bio-based material properties, and supplying a simulation-based approach will help designers make informed decisions for future efficient building practices.

Bio-based materials↗
DOE OSTI2022

Schema Elements for Granta Annual Report: FY2022

Granta: Materials Intelligence, also known as Granta:MI or Granta, is a commercial database software by Ansys, Inc. that is utilized by the Nuclear Security Enterprise (NSE) to organize and store relevant materials data. For a complete discussion of the use of Granta:MI at NSE sites, see the FY21 annual report. Granta:MI is used by five NSE sites locally, and all NSE sites have access to an enterprise instance on the Enterprise Secure Network (ESN) as well as an unclassified development instance. It has been recognized by NNSA management that a shared repository for additive manufacturing (AM) data would not only ensure data and knowledge is not lost but would provide a pool of information relating AM inputs (build parameters, raw materials properties, post-processing information) to the as-built properties of AM parts. Such a pool of data would enable optimization of AM build design and help NNSA achieve the goals of shortening fielding times for new components.

36 MATERIALS SCIENCE↗
NASA NTRS2015

Upper Stage Engine Composite Nozzle Extensions

Carbon-carbon (C-C) composite nozzle extensions are of interest for use on a variety of launch vehicle upper stage engines and in-space propulsion systems. The C-C nozzle extension technology and test capabilities being developed are intended to support National Aeronautics and Space Administration (NASA) and United States Air Force (USAF) requirements, as well as broader industry needs. Recent and on-going efforts at the Marshall Space Flight Center (MSFC) are aimed at both (a) further developing the technology and databases for nozzle extensions fabricated from specific CC materials, and (b) developing and demonstrating low-cost capabilities for testing composite nozzle extensions. At present, materials development work is concentrating on developing a database for lyocell-based C-C that can be used for upper stage engine nozzle extension design, modeling, and analysis efforts. Lyocell-based C-C behaves in a manner similar to rayon-based CC, but does not have the environmental issues associated with the use of rayon. Future work will also further investigate technology and database gaps and needs for more-established polyacrylonitrile- (PAN-) based C-C's. As a low-cost means of being able to rapidly test and screen nozzle extension materials and structures, MSFC has recently established and demonstrated a test rig at MSFC's Test Stand (TS) 115 for testing subscale nozzle extensions with 3.5-inch inside diameters at the attachment plane. Test durations of up to 120 seconds have been demonstrated using oxygen/hydrogen propellants. Other propellant combinations, including the use of hydrocarbon fuels, can be used if desired. Another test capability being developed will allow the testing of larger nozzle extensions (13.5- inch inside diameters at the attachment plane) in environments more similar to those of actual oxygen/hydrogen upper stage engines. Two C-C nozzle extensions (one lyocell-based, one PAN-based) have been fabricated for testing with the larger-scale facility.

Valentine, Peter G.↗
DOE OSTI2022

Machine Learning Prediction of the Critical Cooling Rate for Metallic Glasses from Expanded Datasets and Elemental Features

In this study, we use a random forest (RF) model to predict the critical cooling rate (R C ) for glass formation of various alloys from features of their constituent elements. The RF model was trained on a database that integrates multiple sources of direct and indirect R C data for metallic glasses to expand the directly measured R C database of less than 100 values to a training set of over 2000 values. The model error on 5-fold cross-validation (CV) is 0.66 orders of magnitude in K/s. The error on leave-out-one-group CV on alloy system groups is 0.59 log units in K/s when the target alloy constituents appear more than 500 times in training data. Using this model, we make predictions for the set of compositions with melt-spun glasses in the database and for the full set of quaternary alloys that have constituents which appear more than 500 times in training data. These predictions identify a number of potential new bulk metallic glass systems for future study, but the model is most useful for the identification of alloy systems likely to contain good glass formers rather than detailed discovery of bulk glass composition regions within known glassy systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity

Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Auto-generating databases of Yield Strength and Grain Size using ChemDataExtractor

Abstract The emerging field of material-based data science requires information-rich databases to generate useful results which are currently sparse in the stress engineering domain. To this end, this study uses the’materials-aware’ text-mining toolkit, ChemDataExtractor, to auto-generate databases of yield-strength and grain-size values by extracting such information from the literature. The precision of the extracted data is 83.0% for yield strength and 78.8% for grain size. The automatically-extracted data were organised into four databases: a Yield Strength, Grain Size, Engineering-Ready Yield Strength and Combined database. For further validation of the databases, the Combined database was used to plot the Hall-Petch relationship for, the alloy, AZ31, and similar results to the literature were found, demonstrating how one can make use of these automatically-extracted datasets.

36 MATERIALS SCIENCE↗
NASA NTRS1999

NASA Langley Teacher Resource Center at the Virginia Air and Space Center

Nation's education goals through expanding and enhancing the scientific an technological competence of students and educators. To help disseminate NASA instructional materials and educational information, NASA's Education Division has established the Educator Resource Center Network. Through this network (ERCN), educators are provided the opportunity to receive free instructional information, materials, consultation, and training workshops on NASA educational products. The Office of Education at NASA Langley Research Center offers an extension of its Precollege Education program by supporting the NASA LARC Educator Resource Center at the Virginia Air & Space Center, the official visitor center for NASA LARC. This facility is the principal distribution point for educators in the five state service region that includes Virginia, West Virginia, Kentucky, North Carolina and South Carolina. The primary goal, to provide expertise and facilities to help educators access and utilize science, mathematics, and technology instructional products aligned with national standards and appropriate state frameworks and based on NASA's unique mission and results, has been accomplished. This ERC had 15,200 contacts and disseminated over 190,000 instructional items during the period of performance. In addition the manager attended 35 conferences, workshops, and educational meetings as an GR, presenter, or participant. The objective to demonstrate and facilitate the use of educational technologies has been accomplished through the following: The ERC's web page has been developed as a cyber-gateway to a multitude of NASA and other educational resources as well as to Our own database of current resource materials. NASA CORE CD-ROM technology is regularly demonstrated and promoted using the center's computers. NASA TV is available, demonstrated to educators, and used to facilitate the downlinking of NASA educational programming.

Maher, Kim L.↗
DOE OSTI2024

Schema Elements for Granta Annual Report: FY2024

Granta: Materials Intelligence (Granta: MI) is a commercial database software distributed by Ansys, Inc. that is utilized by the Nuclear Security Enterprise (NSE) to organize and store relevant materials data. Lack of standard and well-documented database schema is the primary obstacle to an NSE materials data management solution, so the objective of this project is to create and document such a schema. In FY21, an approach for designing, documenting, and managing a standard database schema was described based on the creation of schema elements (collections of attributes used to describe particular aspects of the data) to be used as building blocks for creating various database tables without duplication. In FY22, these methods were applied through a multi-site collaboration to create and document the schema elements necessary to build a thermogravimetric analysis (TGA) testing table. In FY23 the schema was expanded to include elements for a differential scanning calorimetry (DSC) table, along with schema for supporting metadata tables including Instruments, Projects, Documents, and Testing Series. In FY24 the following progress was made, again through multi-site collaboration: • The existing schema elements were modified to accommodate thermomechanical analysis (TMA) data, and a table, Test Data: TMA, was created for managing TMA data. • The elements necessary for the following additive manufacturing (AM) data tables (directed at data specific to selective laser sintering AM technology) were created: • AM Builds • AM Processes • AM Part Designs • Built AM Parts • AM Feedstock Materials • AM Feedstock Material Batches • The elements necessary for creating a Calibrated Material Models table were created, and the Calibrated Material Models table was created. In FY25 the existing schema will be deployed on the production enterprise Granta instance on the enterprise secure network. Schema elements will be appended, and new elements created as necessary, to allow the creation of tables specifically to support materials testing, AM process development, and design and analysis for modernization programs.

36 MATERIALS SCIENCE↗
NASA NTRS2022

High-Vacuum Triboelectric Charging of Space Materials

In the high-vacuum environment of space and the surface of the moon, static electricity on surfaces lacks the atmospheric dissipation mechanisms found on earth. As a result, the ubiquitous triboelectric charging mechanism can lead to high levels of charge on surfaces. This static charge can result in damage to sensitive devices, interfere with communications, and electrostatic levitation of lunar dust. At the NASA Electrostatics and Surface Physics Laboratory (ESPL), we use different apparatuses and techniques to tribo-charge materials in high-vacuum (10-5 torr), including a tribo-robot and a triboelectric regolith-material stage (TRMS). The tribo-robot is used to rub two materials together, and fieldmeters and electrometers can be used to determine how much charge is on the materials. The TRMS is used to drag different materials onto a bed of lunar simulant. This system is used to characterize how materials may interact with lunar dust using fieldmeters, electrometers, and laser scattering. Using these methods, we are building a database of how different space materials charge when contacting each other at high-vacuum, and how materials triboelectrically interact with the lunar surface.

Joseph Toth↗
NASA NTRS2003

Thermal and Chemical Characterization of Composite Materials. MSFC Center Director's Discretionary Fund Final Report, Project No. ED36-18

The purpose of this research effort was to: (1) provide a concise and well-defined property profile of current and developing composite materials using thermal and chemical characterization techniques and (2) optimize analytical testing requirements of materials. This effort applied a diverse array of methodologies to ascertain composite material properties. Often, a single method of technique will provide useful, but nonetheless incomplete, information on material composition and/or behavior. To more completely understand and predict material properties, a broad-based analytical approach is required. By developing a database of information comprised of both thermal and chemical properties, material behavior under varying conditions may be better understood. THis is even more important in the aerospace community, where new composite materials and those in the development stage have little reference data. For example, Fourier transform infrared (FTIR) spectroscopy spectral databases available for identification of vapor phase spectra, such as those generated during experiments, generally refer to well-defined chemical compounds. Because this method renders a unique thermal decomposition spectral pattern, even larger, more diverse databases, such as those found in solid and liquid phase FTIR spectroscopy libraries, cannot be used. By combining this and other available methodologies, a database specifically for new materials and materials being developed at Marshall Space Flight Center can be generated . In addition, characterizing materials using this approach will be extremely useful in the verification of materials and identification of anomalies in NASA-wide investigations.

Stanley, D. C.↗
DOE OSTI2022

BISON fuel performance modeling optimization for experiment X447 and X447A using axial swelling and cladding strain measurements

With the recent need to qualify new reactor designs such as the Versatile Test Reactor (VTR), fuel performance calculations need to be performed to determine safety criteria of the proposed designs. In order to validate the fuel performance results obtained by a fuel performance code, BISON, for new reactor designs, legacy fuel from EBR-II and FFTF MFF with Post -Irradiation Examination (PIE) data need to be used as validation cases to benchmark models. Here in this work, BISON has been paired with the Fuels Irradiation & Physics Database (FIPD) and IFR Materials Information System (IMIS) to supply PIE data for comparison with simulations of EBR-II experiments X447/X447A. X447/X447A were assessed by implementing models for Fuel Cladding Chemical Interaction (FCCI) within BISON and optimizing the friction coefficient between the fuel surface and the cladding, the anisotropic swelling factor, and the HT9 first thermal creep scalar (which scales the first term in the HT9 creep equation) to best match the PIE axial fuel swelling height and cladding profilometry for all pins in X447/X447A. The optimal values were found using a generic algorithm developed to select different values for the three parameters until end criteria was met and error couldn’t be reduced further. The BISON-simulated cladding profilometry was evaluated using Standard Error of the Estimate (SEE) to account for the profile shape of the cladding profilometry. Optimal values for the friction coefficient, anisotropic fuel swelling factor, and HT9 first thermal creep scalar were found to best fit the BISON simulation results to the PIE measurements found in IMIS and FIPD. Improvements to current models are suggested to account for the underprediction of fuel swelling at low burnups and the overprediction of fuel swelling at higher burnups observed for the axial fuel swelling height. Although two pins in EBR-II X447/X447A (DP70 and DP75) were known to fail due to FCCI, none of the pins simulated in BISON reached a cumulative damage fraction (CDF) above 0.008 with FCCI correlations coupled in the BISON simulations. The error estimate generated for all pins in X447/X447A using optimal values was 209 µm, which is deemed acceptable.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Shock Hugoniot calculations using on-the-fly machine learned force fields with ab initio accuracy

We present a framework for computing the shock Hugoniot using on-the-fly machine learned force field (MLFF) molecular dynamics simulations. In particular, we employ an MLFF model based on the kernel method and Bayesian linear regression to compute the free energy, atomic forces, and pressure, in conjunction with a linear regression model between the internal and free energies to compute the internal energy, with all training data generated from Kohn–Sham density functional theory (DFT). We verify the accuracy of the formalism by comparing the Hugoniot for carbon with recent Kohn–Sham DFT results in the literature. In so doing, we demonstrate that Kohn–Sham calculations for the Hugoniot can be accelerated by up to two orders of magnitude, while retaining ab initio accuracy. We apply this framework to calculate the Hugoniots of 14 materials in the FPEOS database, comprising 9 single elements and 5 compounds, between temperatures of 10 kK and 2 MK. We find good agreement with first principles results in the literature while providing tighter error bars. In addition, we confirm that the inter-element interaction in compounds decreases with temperature.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗