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At least 37 records · Page 2

Development of a Griffin model of the advanced test reactor

In the pursuit of a higher fidelity deterministic simulation capability of the Advanced Test Reactor, it is important to have a fast yet accurate deterministic neutronics model. Here, to achieve this, we employed an advanced two-step method. The first step involves generating homogenized cross sections using OpenMC, a cutting-edge Monte Carlo neutron transport code. OpenMC offers excellent modular capabilities, allowing for easy component integration and flexibility in incorporating new designs into the model. The second step involves deterministic transport calculations, which are performed using Griffin, a reactor physics application based on the Multiphysics Object-Oriented Simulation Environment (MOOSE). To ensure the accurate spatial resolution and assignment of material cross sections, a Cubit-generated mesh for the Advanced Test Reactor is utilized as an intermediate step between the OpenMC and Griffin models; Griffin utilizes the mesh for its finite element solution, while OpenMC material identifications are written to the mesh file to be used in Griffin material assignments. Additionally, a Python-based script converts the cross sections generated by OpenMC into the ISOXML format required by Griffin. Initial comparisons using the Griffin diffusion solver indicated good agreement between the neutron multiplication factors obtained from the standalone OpenMC model and the Griffin model, with differences of less than 10 pcm in the 2D geometry configuration; it was later determined that this agreement was likely due to compensating effect and was more likely on the order of –700 pcm relative to the OpenMC solution. However, in three-dimensional calculations, an unacceptably large error (almost 8,000 pcm) was found in the Griffin solution with the diffusion solver. Subsequent calculations using Griffin’s discrete ordinates solver demonstrated substantially improved agreement, within 116 pcm of the OpenMC solution used to generate the cross sections for Griffin. Building on this capability, future work will seek to perform more detailed validation calculations. The ultimate goal is to evaluate both transient and multiphysics simulations of the reactor.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS

Mapping Rare Earths and Toxics in E-Waste via Hyperspectral Imaging and Machine Learning

Electronic waste (e-waste) presents a mounting challenge to environmental sustainability due to its complex composition, which includes high-value rare earth elements, hazardous organic compounds, and non-recyclable plastics. Accurate and scalable material classification is essential for enabling efficient resource recovery and safe recycling practices. This study introduces a confidence-aware classification pipeline that combines mid-infrared hyperspectral imaging (HSI), spectral angle mapping (SAM), and iterative machine learning to perform pixel-level material identification across e-waste devices. A curated spectral library encompassing artificial materials (e.g., plastic iron oxide, galvanized metals), minerals (e.g., allanite, hematite), and organic compounds (e.g., benzanthracene, toluene) was used to generate pseudo-labels, each assigned a confidence score based on SAM-derived spectral similarity. High-confidence samples from seven consumer electronics—digital cameras, keyboards, laptop fans, modems, motherboards, TV remotes, and speakers—were iteratively expanded and classified using models such as Support Vector Machine (SVM), Random Forest, Gradient Boosting Classifier, Partial Least Squares Discriminant Analysis (PLSDA) and Logistic Regression. The best-performing classifiers achieved macro F1 scores approaching 1.0. Results revealed widespread plastic content (dominated by plastic iron oxide), the presence of rare earth-bearing minerals like cerium-containing allanite, and pervasive detection of hazardous organics such as benzanthracene. Principal Component Analysis (PCA) visualizations and confusion matrices confirmed high separability and robust classification performance. This methodology enables precise, non-destructive, and scalable classification of heterogeneous e-waste streams. It supports automated, hazard-aware sorting in recycling workflows, facilitating selective recovery of critical materials and compliance with circular economy goals. The confidence-aware framework provides a foundation for real-time deployment in industrial settings, offering significant implications for smart e-recycling infrastructure and policy-driven material stewardship.

Circular economy

Conservation of strategic metals

A long-range program in support of the aerospace industry aimed at reducing the use of strategic materials in gas turbine engines is discussed. The program, which is called COSAM (Conservation of Strategic Aerospace Materials), has three general objectives. The first objective is to contribute basic scientific understanding to the turbine engine technology bank so that our national security is not jeopardized if our strategic material supply lines are disrupted. The second objective is to help reduce the dependence of United States military and civilian gas turbine engines on worldwide supply and price fluctuations in regard to strategic materials. The third objective is, through research, to contribute to the United States position of preeminence in the world gas turbine engine markets by minimizing the acquisition costs and optimizing the performance of gas turbine engines. Three major research thrusts are planned: strategic element substitution; advanced processing concepts; and alternate material identification. Results from research and any required supporting technology will give industry the materials technology options it needs to make tradeoffs in material properties for critical components against the cost and availability impacts related to their strategic metal content.

Joseph R. Stephens

Geobotanical remote sensing for determination of aggregate source material

Aggregate source material suitable for facility and roadbed construction is often a very limited and highly valuable resource. The location of suitable source material is crucial to construction operations once facility requirements are established. The application of airborne and spaceborne remote sensing to terrain information requirements has proved attractive because of the rapid processing time and extensive spatial coverage associated with remotely gathered imagery. Source material identification may be improved by the remote sensing of vegetation associated with the material, particularly in areas of high vegetative cover. A research study employing remote sensing techniques was initiated to determine if vegetation could be used to discriminate parent materials for suitability as aggregate source material. Two test sites representing potential alluvial and residual source areas were selected in a semiarid region of Central California. Methods developed for the study included field observations of vegetation characteristics associated with the two parent material types along with the analysis of Thematic Mapper Simulator data flown over the test sites. The most useful images were those composites that included bands from two of the techniques (i.e., a Perpendicular Vegetation Index (PVI) band combined with principal components bands).

Minor, Timothy

Oxygen production by electrolysis of molten lunar regolith

The goal of this study was threefold. First, the theoretical energy requirements of the process were to be defined. This includes studies of the relevant oxidation-reduction reactions in the melt, their kinetics and energies of reaction, and experimental determination of production efficiencies and melt resistivities as functions of melt composition and applied potential. Second, the product(s) of silicate electrolysis were to be characterized. This includes: (1) evaluating the phase relationships in the systems SiO2-TiO2-Al2O3-MgO-FeO-CaO and Fe-Si; (2) estimating the compositions of the metal products as a function of applied potential and feedstock composition based on phase equilibria in the Fe-Si system and free energy values for SiO2 and FeO reported in the literature; (3) definition of compositions of products in actual experiments; and (4) definition of the form the product takes (whether phases separate or remain fixed, whether crystals settle or float in the remaining melt, and how large crystals form). Third, materials for these highly corrosive high-temperature silicate melts were to be identified. This includes identifing materials that may be either inert or thermodynamically stable in these melts, and experimental testing of the materials to confirm that they do not deteriorate. The results are discussed within this framework.

Haskin, Larry A.

Wormhole Formation in RSRM Nozzle Joint Backfill

The RSRM nozzle uses a barrier of RTV rubber upstream of the nozzle O-ring seals. Post flight inspection of the RSRM nozzle continues to reveal occurrence of "wormholes" into the RTV backfill. The term "wormholes", sometimes called "gas paths", indicates a gas flow path not caused by pre-existing voids, but by a little-understood internal failure mode of the material during motor operation. Fundamental understanding of the mechanics of the RSRM nozzle joints during motor operation, nonlinear viscoelastic characterization of the RTV backfill material, identification of the conditions that predispose the RTV to form wormholes, and screening of candidate replacement materials is being pursued by a joint effort between Thiokol Propulsion, NASA, and the Army Propulsion & Structures Directorate at Redstone Arsenal. The performance of the RTV backfill in the joint is controlled by the joint environment. Joint movement, which applies a tension and shear load on the material, coupled with the introduction of high pressure gas in combination create an environment that exceeds the capability of the material to withstand the wormhole effect. Little data exists to evaluate why the material fails under the modeled joint conditions, so an effort to characterize and evaluate the material under these conditions was undertaken. Viscoelastic property data from characterization testing will anchor structural analysis models. Data over a range of temperatures, environmental pressures, and strain rates was used to develop a nonlinear viscoelastic model to predict material performance, develop criteria for replacement materials, and quantify material properties influencing wormhole growth. Three joint simulation analogs were developed to analyze and validate joint thermal barrier (backfill) material performance. Two exploratory tests focus on detection of wormhole failure under specific motor operating conditions. A "validation" test system provides data to "validate" computer models and predictions. Finally, two candidate replacement materials are being screened and "validated" using the developed test systems.

Stevens, J.

Identification of visual evoked response parameters sensitive to pilot mental state

Systems analysis techniques were developed and demonstrated for modeling the electroencephalographic (EEG) steady state visual evoked response (ssVER), for use in EEG data compression and as an indicator of mental workload. The study focused on steady state frequency domain stimulation and response analysis, implemented with a sum-of-sines (SOS) stimulus generator and an off-line describing function response analyzer. Three major tasks were conducted: (1) VER related systems identification material was reviewed; (2) Software for experiment control and data analysis was developed and implemented; and (3) ssVER identification and modeling was demonstrated, via a mental loading experiment. It was found that a systems approach to ssVER functional modeling can serve as the basis for eventual development of a mental workload indicator. The review showed how transient visual evoked response (tVER) and ssVER research are related at the functional level, the software development showed how systems techniques can be used for ssVER characterization, and the pilot experiment showed how a simple model can be used to capture the basic dynamic response of the ssVER, under varying loads.

Zacharias, G. L.

SELKIE Spectral Extraction Results

This document describes results from the WAMOR Chesapeake Bay collection in October 2024, a data collection from the Arkeus HSOR hyperspectral sensor. The calibration array was staged at USCG Station Little Creek, Virginia. With the provided HSOR imagery and data, our analysis does not show a capability to effectively detect materials of interest from non-materials of interest as well as differentiate materials of interest from each other, the materials of interest being polypropylene, polyester, and cotton. The fundamental reasons are (1) the spectral bands of HSOR do not reside in specific wavelength locations to allow for material identification and (2) the provided imagery appears to not have consistent band-to-band relationships indicative of a lack of calibration (gain/offsets) or some other calibration issues. With regard to analyzing a subset of the delivered HSOR, HSOR can make general statements about materials not being water and have the capability to make color assessments.

36 MATERIALS SCIENCE

Hyperspectral imaging for real-time waste materials characterization and recovery using endmember extraction and abundance detection

Hyperspectral imaging, combined with advanced spectral unmixing techniques and artificial intelligence, offers a powerful solution for improving material identification and classification. Here, this study evaluates the effectiveness of the pixel purity index and the sequential maximum angle convex cone algorithms in extracting and validating spectral signatures from pure samples of paper components (cellulose and lignin) and plastic (polypropylene). Principal-component analysis showed that both algorithms captured nearly all relevant variance for the tested materials. Spectral signatures were compared using the spectral angle mapper, revealing high similarity in the short-wave infrared region and greater variability in the visible near-infrared range. The methodology was then applied to a disposable coffee cup to detect and quantify mixed materials, accurately estimating material abundance and object area with less than 1% error. This approach enhances material classification, supporting product verification, quality control, and automated sorting for sustainable waste management and resource recovery.

36 MATERIALS SCIENCE

AI-powered municipal solid waste management: a comprehensive review from generation to utilization

The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).

09 BIOMASS FUELS

Identification Uncertainty in Inverse Material Model Parameter Determination: A Sensitivity‐Based Decision Process for Load Path Selection

This research proposes a sensitivity-based framework for selecting the optimal prescribed loading path for a biaxial cruciform specimen. Optimality here is determined by the direction and magnitude of the prescribed displacement that minimizes the influence of random noise on the material model parameter identification. Using simulated experimental data based on finite element simulation, in this work, we identify the material model parameters of a Ludwik hardening model and plane stress implementation of the Hill-48 yield criterion using finite element model updating (FEMU). Our analysis reveals that the identification (or estimator) uncertainty of model parameters depends on the displacement boundary conditions (i.e., loading sequence) and the ground-truth value of the individual parameters. Optimal experimental design (OED) criteria based on the Fisher information matrix were investigated to mitigate indecision in the choice of optimal load path when the identification uncertainty of different material model parameters optimized at different load paths. The determinant of the Fisher information matrix was chosen here as the more useful metric due to its ability to capture uncertainty of the most influential material model parameters. The proposed framework demonstrates potential for real-time automated load step selection using scalar criteria derived prior to mechanical loading. The framework can be generalized to other geometries, boundary conditions and material models, allowing this procedure to be utilized for different experimental configurations and materials.

Fayad, Samuel S. [University of Illinois at Urbana

An Assessment of GEO Orbital Debris Photometric Properties Derived from Laboratory-Based Measurements

Optical observations of orbital debris offer insights that differ from radar measurements (specifically the size parameter and wavelength regime). For example, time-dependent photometric data yield lightcurves in multiple bandpasses that aid in material identification and possible periodic orientations. This data can also be used to help identify shapes and optical properties at multiple phase angles. Capitalizing on optical data products and applying them to generate a more complete understanding of orbital space objects, is a key objective of NASA s Optical Measurement Program, and a primary driver for creation of the Optical Measurements Center (OMC). The OMC attempts to emulate space-based illumination conditions using equipment and techniques that parallel telescopic observations and source-target-sensor orientations. The OMC uses a 300 Watt Xenon arc lamp as a solar simulator, a CCD camera with Johnson/Bessel colored filters, and a robotic arm to orientate/rotate objects to simulate an object's orbit/rotational period. A high-resolution, high bandwidth (350nm-2500nm) Analytical Spectral Devices (ASD) spectrometer is also employed to baseline various material types. Since observation of GEO targets are generally restricted to the optical regime (due to radar range limitations), analysis of their properties is tailored to those revealed by optical data products. In this connection, much attention has been directed towards understanding the lightcurves of orbital debris with high area-to-mass (A/m) ratios (greater than 0.9 square meters per kilogram). A small population of GEO debris was recently identified, which exhibits the properties of high A/m objects, such as variable eccentricities and inclinations a dynamical characteristic generally resulting from varying solar radiation pressure on high A/m objects. Materials such as multi-layered insulation (MLI) and solar panels are two examples of materials with high area-to mass ratios. Lightcurves for such objects can vary greatly (even for the same object under different illumination conditions). For example, specular reflections from multiple facets of the target surface (e.g. Mylar or Aluminized Kapton) can lead to erratic, orientation-dependent light curves. This paper will investigate published color photometric data for a series of orbital debris targets and compare it to the empirical photometric measurements generated in the OMC. The specific materials investigated (known to exist in GEO) are: an intact piece of MLI, separated layers of MLI, and multiple solar cells materials. Using the data acquired over specific rotational angles through different filters (B, V, R, I), a color index is acquired (B-R, R-I). As a secondary check, the spectrometer is used to define color indexes for the same material. Using these values and their associated lightcurves, this laboratory data is compared to observational data obtained on the 1m telescope of the Astronomical Institute of the University of Bern (AUIB) and the 0.9 m Small and Moderate Aperture Research Telescope System (SMARTS) telescope at Cerro Tololo Inter-American Observatory (CTIO). We will present laboratory generated lightcurves with color indexes of the high A/m materials alongside telescopic data of targets with high A/m values. We will discuss the relationship of laboratory to telescope data in the context of classification of GEO debris objects.

Rodriquez-Cowardin, H.

Replacing critical and strategic refractory metal elements in nickel-base superalloys

Because of the import status and essential nature of their use, cobalt, chromium, tantalum, and niobium were identified as strategic and critical in the aerospace industry. NASA's Conservation of Strategic Aerospace Materials (COSAM) program aims to reduce the need for strategic materials used in gas turbine engines. Technological thrusts in two major areas are under way to meet the primary objective of conserving the use of strategic materials in nickelbase superalloys. These thrusts consist of strategic element substitution and alternative material identification. The program emphasizes cooperative research teams involving NASA Lewis Research Center, universities, and industry. The adoption of refractory metals in nickel-base superalloys is summarized including their roles in mechanical strengthening and environmental resistance; current research activities under way in the COSAM Program are presented as well as research findings to date.

Stephens, J. R.

Image processing software for imaging spectrometry data analysis

Imaging spectrometers simultaneously collect image data in hundreds of spectral channels, from the near-UV to the IR, and can thereby provide direct surface materials identification by means resembling laboratory reflectance spectroscopy. Attention is presently given to a software system, the Spectral Analysis Manager (SPAM) for the analysis of imaging spectrometer data. SPAM requires only modest computational resources and is composed of one main routine and a set of subroutine libraries. Additions and modifications are relatively easy, and special-purpose algorithms have been incorporated that are tailored to geological applications.

Mazer, Alan

Approach to SSME health monitoring. II - Exhaust plume emission spectroscopy at the DTF

The Diagnostics Testbed Facility (DTF) located at the Stennis Space Center (SSC) is used for obtaining extensive sets of H2O2 exhaust plume emission spectral data for the SSME critical components related elements and materials. The SSME related elements and materials are simulated by mixing appropriate amounts of compounds of their respective constituent elements in an aqueous solution which is injected into the combustion chamber of the DTFT. Five of the most critical components of the SSME which have experienced very severe wear and tear problems in the past are analyzed. These are high pressure turbopump (HPTP) turbine blades, HPTP turbine disks, HPTP bearing, main injector LOX posts, and the main combustion chamber structural shell. The alloys used in the manufacturing of these components are MAR-M 246 + Hf, Waspaloy X, AISI 440C, Haynes 188, and Inconel 718, respectively. The experimental setup and procedures at the DTF are described; stratospheric data for the five alloys are presented; and strategies for the material identification in the SSME exhaust plume are discussed.

Tejwani, Gopal D.

Onboard Processor for Compressing HSI Data

With EO-1 Hyperion and MightySat in orbit NASA and the DoD are showing their continued commitment to hyperspectral imaging (HSI). As HSI sensor technology continues to mature, the ever-increasing amounts of sensor data generated will result in a need for more cost effective communication and data handling systems. Lockheed Martin, with considerable experience in spacecraft design and developing special purpose onboard processors, has teamed with Applied Signal & Image Technology (ASIT), who has an extensive heritage in HSI, to develop a real-time and intelligent onboard processing (OBP) system to reduce HSI sensor downlink requirements. Our goal is to reduce the downlink requirement by a factor greater than 100, while retaining the necessary spectral fidelity of the sensor data needed to satisfy the many science, military, and intelligence goals of these systems. Our initial spectral compression experiments leverage commercial-off-the-shelf (COTS) spectral exploitation algorithms for segmentation, material identification and spectral compression that ASIT has developed. ASIT will also support the modification and integration of this COTS software into the OBP. Other commercially available COTS software for spatial compression will also be employed as part of the overall compression processing sequence. Over the next year elements of a high-performance reconfigurable OBP will be developed to implement proven preprocessing steps that distill the HSI data stream in both spectral and spatial dimensions. The system will intelligently reduce the volume of data that must be stored, transmitted to the ground, and processed while minimizing the loss of information.

Cook, Sid

Followup to Columbia Investigation: Reinforced Carbon/Carbon From the Breach Location in the Wing Leading Edge Studied

Initial estimates on the temperature and conditions of the breach in the Space Shuttle Columbia's wing focused on analyses of the slag deposits. These deposits are complex mixtures of the reinforced carbon/carbon (RCC) constituents, insulation material, and wing structural materials. Identification of melted/solidified Cerachrome insulation (Thermal Ceramics, Inc., Augusta, GA) indicated that the temperatures at the breach had exceeded 1760 C.

Jacobson, Nathan S.