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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Composition Measurements of the Environmental Management Headquarters (EMHQ) Low-Activity Waste (LAW) Glasses

This report provides the results from the chemical analyses of glass compositions for the Environmental Management Headquarters Low-Activity Waste study glasses, a series of simulated nuclear waste glasses fabricated at Pacific Northwest National Laboratory. These data will be used in the development, validation, and implementation of enhanced property/composition models for waste glass vitrification at Hanford. Chemical analyses were performed on a representative sample of each of the quenched glasses to allow for comparisons with targeted compositions. The relative differences between the targeted and measured concentrations of Na 2 O, Li 2 O, and SO 3 for several of the glasses and B 2 O 3 in one of the glasses were greater than 10%. These results can be used in further characterization of this series of glasses, including the normalization of Product Consistency Test results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Composition Measurements of the LAW ML1 Glasses

This report provides the results from the chemical analyses of the glass compositions of the quenched Low Activity Waste Machine Learning study glasses, a series of simulated nuclear waste glasses designed and fabricated at Pacific Northwest National Laboratory. These data will be used in the development, validation, and implementation of enhanced property/composition models for waste glass vitrification at Hanford. Chemical analyses were performed on a representative sample of each of the quenched glasses to allow for comparisons with target compositions. The relative differences between the target and measured concentrations of F - for one glass, P 2 O 5 for two glasses, SO 3 for three glasses, and ZrO 2 for two of the glasses were greater than 10%. These results can be used in further characterization of this series of glasses, including the normalization of Product Consistency Test results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1993

Particle sightings by the infrared telescope on Spacelab 2

Part of the objective of the cooled infrared telescope on Spacelab 2 was to determine the particle environment around the Space Shuttle. The telescope scanned the sky in six wavelength bands ranging from 2 to 100 microns with high time resolution. Dust particles could be identified from their particular signature in the data stream. Particle data from about 4 h early in the mission were analyzed in terms of size, color temperature, velocity, and time. The 1100 particles that were seen were slow moving and ranged in color temperature from 190 to 350 K with a few much hotter. The minimum detectable diameter is between 5 and 13 microns, depending on temperature. The size distribution resembles sample distributions collected at a Shuttle preparation facility. Although particle detection rates varied widely with time, no specific events were identified to be associated with particle production. It was not possible to determine the particle composition, although it was probably not ice.

Simpson, J. P.↗
NASA NTRS2013

Microchemical and Structural Evidence for Space Weathering in Soils from Asteroid Itokawa

The chemistry, microstructure and optical properties of grains on the surfaces of airless bodies are continu-ously modified due to their interactions predominantly with solar energetic ions and micrometeorite impacts. Collectively known as space weathering, this phenomenon results in a discrepancy between remotely sensed spectra from asteroids and those ac-quired directly from meteorites. The return of pristine samples from the asteroid Itokawa provides insight into surface processes on airless bodies and will help in correlating remote sensing data with laboratory analysis of meteorites. Samples and Methods: We examined Itokawa samples RA-QD02-0042-01 and RA-QD-02-0042-02, ultramicrotomed sec-tions of a singular grain prepared by the Hayabusa sample cura-tion team. We analyzed these slices using a 200 keV JEOL 2010F transmission electron microscope (TEM) at Arizona State Uni-versity and a 200 keV JEOL 2500SE TEM at NASA JSC. Both field emission TEMs are equipped with energy-dispersive X-ray spectrometers (EDS) and scanning TEM (STEM) detectors. Results and Discussion: TEM observations reveal that the sectioned grain predominantly consists of a single crystal of low-Ca orthopyroxene, with subsidiary smaller regions of olivine, Fe-Ni sulfide, and Fe-Ni metal. EDS-spectrum imaging and high-resolution TEM (HRTEM) show local, nanocrystalline regions of the outermost 2 to 5 nm of the pyroxene are composed of an Fe-Mg-S-rich and Si- and O-depleted layer that is underlain by a 2- to 5-nm thick amorphous zone enriched in Si. These layers occur in multiple microtome slices and have uniform thicknesses. We also observe localized 'islands' of material on the surface of the pyroxene which HRTEM imaging indicates are amorphous and EDS measurements show are compositionally heterogeneous. A 10- to 60-nm thick partially amorphous zone occurs below the compositionally distinct rim. While this this zone is associated with the compositionally heterogeneous outer layer, it also occurs as a local stand-alone feature on the exterior rim of the grain. Ar-eas of the pyroxene grain rim also exhibit a vesicular texture. The TEM data indicate a complex history of space weather-ing for samples RA-QD02-0042-01 and -02. The outermost layer of nanocrystalline material with varied composition is consistent with previously suggested [3-4] chemical and structural pro-cessing by solar wind ions, with a possible additional role for im-pact vapor deposition [3-4]. The amorphous and compositionally distinct islands on the surface of this grain, similar to lunar glasses, suggest formation through vapor deposition via micrometeor-ite impact events. In comparison, the amorphization and vesicula-tion textures are likely a product of radiation damage from the solar wind. The depth and degree of amorphization, in conjunction with model calculations, will help provide an upper limit on exposure time for these particles.

Thompson, M. S.↗
NASA NTRSDate not supplied

Overview of Lightning Science at NASA Marshall Space Flight Center

NASA Marshall Space Flight Center (MSFC) is a recognized world leader in the science of lightning. To date, MSFC has led three space-based global lightning observing missions and has helped lead multiple suborbital field campaigns involving lightning observations. Recently, the MSFC Lightning Team is closing out the recently completed International Space Station Lightning Imaging Sensor (ISS LIS) mission, including developing a nearly three-decade global climatology of lightning from space. This work also includes integrating lightning observations with data from NASA precipitation missions. The Team is also busy analyzing data from a recent airborne field campaign that observed dozens of terrestrial gamma-ray flashes (TGFs) from intense tropical thunderstorms. Lightning Team members are also leaders in validation of the Geostationary Lightning Mapper (GLM) operated by NOAA, and in developing lightning safety applications and studying the relationship between lightning and wildfires. The Lightning Team also studies chemical production by lightning and contributes to the National Climate Assessment (NCA). Finally, the Lightning Team is busy developing the next generation of spaceborne lightning sensors to broaden our understanding of the relationships between lightning, weather, climate, and atmospheric composition.

Timothy Lang↗
DOE OSTI2025

Unsupervised Clustering and Supervised Regression Learning to Select High Temperature Oxidation-Resistant Materials

High temperature oxidation and corrosion degradation mechanisms dictate the lifetime of materials critical to energy production. The combination of modeling and experimental approaches such as machine learning (ML) and data analytics, with sufficient experimental data, can accelerate the development of new materials while limiting its cost. In the present work, ML will be applied to two high temperature oxidation data libraries (Oak Ridge National Laboratory and National Air and Space Administration) that comprised of about 5000 mass change sample datasheets for a variety of materials and temperatures in dry air and air + 10 % H2O. A python code was developed to prepare the data for machine learning by collecting and formatting oxidation rate constants, alloy compositions and environment of exposure into a single data frame. Scikit-learn library and Statistics and Machine Learning Toolbox within MathWorks were then used to perform unsupervised clustering and supervised regression learning. The impact of dataset distribution on the performance of the developed ML models was evaluated. Potential strategies to improve the predictions and enhance extrapolative capability of the previously trained model were investigated.

Romedenne, Marie [ORNL] (ORCID:0000000317936561)↗
DOE OSTI2021

Landscape-scale Characterization of Arctic Tundra Vegetation Composition, Structure, and Function with a Multi-sensor Unoccupied Aerial System: Supporting Data.

Airborne remote sensing data collected using the Brookhaven National Laboratory's (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform, the Osprey, operated by the Terrestrial Ecosystem Science and Technology (TEST) group (https://www.bnl.gov/testgroup). This package includes data from three flights flown over the NGEE-Arctic Council, Kougarok and Teller sites in July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface 'skin' temperature imagery, as well as surface reflectance at 1 nm intervals in the visible to near-infrared spectral range 350 - 1000 nm measured at regular intervals along each flight path. Derived image products include ortho-mosaiced RGB and TIR images, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, digital terrain model (DTM), and a canopy height model (CHM). In addition, a VNIR surface reflectance file is provided for the trigger locations collected during each flight campaign. Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Unprocessed and processed data products are included in this package (processing levels 0-3). Data and metadata are provided as text (*.txt, *.json, *.kml, *hdr, *.enp), tabular (*.dat, *.csv, *.waypoint, ENVI format (no extension)), point cloud (*.laz) and image (*.jpg, *.tif, *png) formats. This metadata document contains flight campaign, instrument and file metadata, along with a description of data processing levels, data products and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy?s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

GPM, TRMM, and Other Global Precipitation Products and Services at NASA GES DISC

Precipitation is a key environmental variable. For example, in agriculture, precipitation, temperature, water (soil moisture), solar radiation, NDVI, etc., are key variables.Rainfed agriculture – major farming practices that rely on rainfall for water.Rainfed agriculture: >95% of farmed land (sub-Saharan Africa); 90% (Latin America); 75% (Near East and North Africa); 65% (East Asia); 60% (South Asia).Droughts and floods can cause severe crop loss. The Goddard Earth Sciences (GES) Data and Information Services Center (DISC), one of 12 NASA data centers, is located in Greenbelt, Maryland, USA. The NASA GES DISC is a major data archive center for global precipitation, water & energy cycles, atmospheric composition, and climate variability.

Liu, Z.↗
DOE OSTI2023

A reduced order sulfuric acid decomposition model for a nuclear-powered hybrid sulfur cycle

A reduced order model of sulfuric acid decomposition within a bayonet chemical reactor was developed to support the U. S. Department of Energy Integrated Energy System program, and address the lack of knowledge in scaling and integration for joint chemical and nuclear processes. Sulfuric acid decomposition within a bayonet reactor was modeled to provide chemical and thermodynamic data relevant to advanced nuclear reactor-driven integrated energy systems based on desired operational scale and operational conditions. The temperature range required for high-temperature advanced nuclear reactor integrated energy systems, 750-850 °C, was shown to produce reasonable agreement (within a few percent relative error) with past models and experimental data, and yielded good efficiency results for bayonet reactor operations. The results of the reduced order model agreed with previous work from Savannah River National Labs within a maximum of 3.4% error on the decomposition of sulfur trioxide, and on previous Hybrid Sulfur flowsheets from Gorensek and Summers that showed operational temperature, pressure, and composition ranges for efficiency which made the Hybrid Sulfur cycle competitive with water electrolysis. Here, the agreement with previous high-fidelity models provided a framework for future Integrated Energy System grid evaluations with an advanced nuclear reactor and large-scale hydrogen production using a mathematical model to represent chemical operations.

08 HYDROGEN↗
NASA NTRS1984

STS-9 BET products

The final products generated for the STS-9, which landed on December 8, 1983 are reported. The trajectory reconstruction utilized an anchor epoch of GMT corresponding to an initial altitude of h 356 kft, selected in view of the limited tracking coverage available. The final state utilized IMU2 measurements and was based on processing radar tracking from six C-bands and a single S-band station, plus six photo-theodolite cameras in the vicinity of Runway 17 at Edwards Air Force Base. The final atmosphere (FLAIR9/UN=581199C) was based on a composite of the remote measured data and the 1978 Air Force Reference Atmosphere model. The Extended BET is available as STS9BET/UN=274885C. The AEROBET and MMLE input files created are discussed. Plots of the more relevant parameters from the AEROBET (reel number NL0624) are included. Input parameters, final residual plots, a trajectory listing, and data archival information are defined.

Findlay, J. T.↗
NASA NTRS1982

Advanced manufacturing development of a composite empennage component for L-1011 aircraft

This is the final report of technical work conducted during the fourth phase of a multiphase program having the objective of the design, development and flight evaluation of an advanced composite empennage component manufactured in a production environment at a cost competitive with those of its metal counterpart, and at a weight savings of at least 20 percent. The empennage component selected for this program is the vertical fin box of the L-1011 aircraft. The box structure extends from the fuselage production joint to the tip rib and includes front and rear spars. During Phase 4 of the program, production quality tooling was designed and manufactured to produce three sets of covers, ribs, spars, miscellaneous parts, and subassemblies to assemble three complete ACVF units. Recurring and nonrecurring cost data were compiled and documented in the updated producibility/design to cost plan. Nondestruct inspections, quality control tests, and quality acceptance tests were performed in accordance with the quality assurance plan and the structural integrity control plan. Records were maintained to provide traceability of material and parts throughout the manufacturing development phase. It was also determined that additional tooling would not be required to support the current and projected L-1011 production rate.

Alva, T.↗
NASA NTRS1999

Fragmentation of 200 and 244 MeV/u Carbon Beams in Thick Tissue-Like Absorbers

Stacks consisting of thin CR-39 sheets sandwiched between thick Lucite and water absorbers were perpendicularly bombarded by C-12 ions at 200 and 244 MeV/u. Track radius distributions representing the charge composition of the fragmented beams were automatically measured by a particle track analysis system. After analysis of the nuclear charge distributions, the total charge removal cross sections and elemental production cross sections of fragments with atomic numbers from 5 to 3, were obtained down to the lower energies (approximately 50 and 100 MeV/u, respectively). It has been found that the measured total charge removal cross section agrees with theoretical predictions within approximately 10% and very well with previous experiments in corresponding energy regions. Two model calculations for production of B fragment are in good agreement with our measured data while a third model overestimates it by approximately 12%. Theoretical cross sections for Be and Li fragments differ strongly among the different models and from measured values.

Golovchenko, A. N.↗
DOE OSTI2023

Gas-Phase Composition as a Predictive Metric for Calendar Life Behavior of Next-Generation Silicon Anodes

The expansion of renewable technologies and electrification of the transportation sector is driving increased demand for next-generation battery materials that provide higher power and energy density with superior cycling and calendar life stability. Silicon (Si) has a theoretical capacity nearly 10x that of graphite, and is therefore a promising anode material candidate to meet these rigorous performance demands. While leading Si anode battery demonstrations are approaching target metrics for cycle life, a series of complex and interrelated modes of reactivity lead to reduced calendar life and therefore challenge practical adoption of these materials. Deconvoluting the degradation processes that impact Si calendar life is critical to informing the rational and accelerated design of improved Si materials. In the present work, we employ novel sampling techniques and GC-MS-FID characterization to measure gas-phase composition during initial Si cycling, which we tie to selective mechanisms of Si passivation. We utilize a tiered analysis approach to identify and quantify the gas-phase reaction products associated with three advanced Si material candidates under practical operating conditions. Ex situ analysis of Si powders (pure chemical reactivity) is coupled with nondestructive in situ sampling of Si electrodes in a practical pouch-cell format (coupled chemical and electrochemical reactivity). We link the observed gas-phase species evolution to electrochemical behavior and measured calendar life of the three Si materials. Further, we evaluate the voltage-resolved evolution of gas-phase species for one such Si nanomaterial, where nonmonotonic gas generation implies competition between passivating reaction pathways. The measured gas-phase compositional data serves as a critical input for our advanced electrochemical SEI models to identify favorable vs unfavorable reaction pathways to stabilize Si. In addition to bolstering a fundamental understanding of Si reactivity, the present approach informs specific and quantifiable gas-phase metrics tied to calendar life improvements in Si, which can streamline and accelerate the process of next-generation material development.

DIRECT ENERGY CONVERSION,ENERGY STORAGE↗
DOE OSTI2024

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Ion correlations explain kinetic selectivity in diffusion-limited solid-state synthesis reactions

Establishing viable solid-state synthesis pathways for novel inorganic materials remains a major challenge in materials science. Previous pathway design methods using pairwise reaction approaches have navigated the thermodynamic landscape with first-principles data but lack kinetic information, limiting their effectiveness. This gap leads to suboptimal precursor selection and predictions, especially for reactions forming competing phases with similar formation energies, where ion diffusion is a critical influence. Here we demonstrate an inorganic synthesis framework by incorporating machine learning-derived transport properties through ‘liquid-like’ product layers into a thermodynamic cellular reaction model. In the Ba–Ti–O system, known for its competitive polymorphism, we obtain accurate predictions of phase formation with varying BaO:TiO2 ratios as a function of time and temperature. We find that diffusion–thermodynamics interplay governs phase compositions, with cross-ion transport coefficients critical for predicting diffusion-limited selectivity. This work bridges length scales and timescales by integrating solid-state reaction kinetics with first-principles thermodynamics and spatial reactivity.

Atomistic models↗
DOE OSTI2021

Composition Measurements of the HFG1 Glasses

This report provides results from the analyses of glass compositions for the High Fluoride Glasses-1, a series of simulated nuclear waste glasses fabricated at the Pacific Northwest National Laboratory. The series included the quenched version of the glasses. These data will be used to validate or identify areas of potential characterization improvements in the current glass property/composition models. Chemical analyses were performed on a representative sample of each of the quenched glasses to allow for comparisons with the targeted compositions. The relative differences between the targeted and measured concentrations of F, K 2 0, and Li 2 O for some of the glasses were greater than 10%. Overall, there were no indications of errors in batching of the simulated waste glasses. These results can be used in further characterization of this series of glasses, including the normalization of Product Consistency Test results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1983

The Ne-21 production rate in stony meteorites estimated from Be-10 and other radionuclides

The Be-10 contents of 28 stony meteorites with known Ne-21 contents range from 0.97 to 23 dpm/kg and give an average Ne-21 production rate (P sub 21) of (0.28 + or - 0.02) x 10 to the -8th cu cm STP/g-My for shielding conditions corresponding to Ne-22/Ne-21 = 1.114 in an H-chondrite. This P sub 21 (Be) agrees with others' P sub 21 based on Na-22, Kr-81, and Mn-53, but not an Al-26. Temporal variations in the cosmic ray flux do not explain the disagreement satisfactorily; major errors in the radionuclide half-lives are not indicated. The discrepancy seems rooted in the data selection and the difficulties of making accurate corrections for shielding, chemical composition and other sources of variability.

Moniot, R. K.↗