Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “material training”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Geographic Information System Based Emergency Response Training Assessments for DOE Radioactive Materials Transport - 20027

Safety and security are priorities of U.S. Department of Energy (DOE) radioactive materials shipping campaigns. In the more than 70-year history of domestic transport of spent nuclear fuel (SNF), there has never been a transportation-related radiological injury. To support transportation planning, among the tools that DOE uses is the Stakeholder Tool for Assessing Radioactive Transportation (START). START contains geospatial data and transportation route analyses capabilities designed to support a range of DOE transportation planning initiatives. One of those functions is the capability to support emergency response planning and training for State and Tribal jurisdictions located on routes used for DOE shipments of radioactive materials. As part of the Department's commitment to public safety, DOE provides federally-funded radiological response training to emergency responders along DOE radioactive materials transportation corridors through its Transportation Emergency Preparedness Program (TEPP). START contains spatial data representing the locations and emergency-response capabilities of fire departments, police, hospitals, State emergency response centers, and where TEPP-trained personnel are based. The START tool supports State and Tribal users' ability to evaluate emergency-response coverage on active and potential DOE radioactive materials transport routes through their jurisdictions, provide expected response times to reach the scene of an incident, identify equipment available to support a response, and identify the number of response personnel and their respective training levels. In addition, START can be used to identify gaps in coverage along a transportation corridor where additional radiological emergency response training may be needed. This paper describes the data, features, and functionality DOE uses to provide a resource for emergency response training needs assessments for States and Tribes along active and potential routes for transporting radioactive materials, and illustrates its use. It also discusses future plans to integrate TEPP and Federal Emergency Management Agency (FEMA) radiological training data to provide a more comprehensive source of geospatial information on personnel who have received equivalent radiological response training. (authors)

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

Machine learning predictions of high-Curie-temperature materials

Technologies that function at room temperature often require magnets with a high Curie temperature, $T$ C , and can be improved with better materials. Discovering magnetic materials with a substantial $T$ C is challenging because of the large number of candidates and the cost of fabricating and testing them. Using the two largest known datasets of experimental Curie temperatures, we develop machine-learning models to make rapid $T$ C predictions solely based on the chemical composition of a material. We train a random-forest model and a k -NN one and predict on an initial dataset of over 2500 materials and then validate the model on a new dataset containing over 3000 entries. The accuracy is compared for multiple compounds' representations (“descriptors”) and regression approaches. A random-forest model provides the most accurate predictions and is not improved by dimensionality reduction or by using more complex descriptors based on atomic properties. Further, a random-forest model trained on a combination of both datasets shows that cobalt-rich and iron-rich materials have the highest Curie temperatures for all binary and ternary compounds. An analysis of the model reveals systematic error that causes the model to over-predict low-$T$ C materials and under-predict high-$T$ C materials. For exhaustive searches to find new high-$T$ C materials, analysis of the learning rate suggests either that much more data is needed or that more efficient descriptors are necessary.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

GL4U: Bioinformatics training for students and educators using space omics data

NASA’s GeneLab project provides researchers open access to space-relevant experiment multi-omics data that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. The GL4U direct training pilot program was conducted in June 2021. During the pilot, students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrated the capacity of GL4U for training young scientists and encouraging data re-use. During the educator pilot, scheduled for June 2022, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative.

Amanda Marie Saravia-butler↗
NASA NTRSDate not supplied

GL4U: Bioinformatics Training for Students and Educators Using Space Omics Data

NASA’s GeneLab project provides researchers open access to space-relevant experiment multi-omics data that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. The GL4U direct training pilot program was conducted in June 2021. During the pilot, students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrated the capacity of GL4U for training young scientists and encouraging data re-use. During the educator pilot, scheduled for June 2022, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative.

Amanda Marie Saravia-butler↗
DOE OSTI2022

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems *

Abstract We introduce a multi-tasking graph convolutional neural network, HydraGNN, to simultaneously predict both global and atomic physical properties and demonstrate with ferromagnetic materials. We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum with a fixed body centered tetragonal lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range. By taking advantage of underlying physical correlations between material properties, multi-task learning (MTL) with HydraGNN provides effective training even with modest amounts of data. Moreover, this is achieved with just one architecture instead of three, as required by single-task learning (STL). The first convolutional layers of the HydraGNN architecture are shared by all learning tasks and extract features common to all material properties. The following layers discriminate the features of the different properties, the results of which are fed to the separate heads of the final layer to produce predictions. Numerical results show that HydraGNN effectively captures the relation between the configurational entropy and the material properties over the entire compositional range. Overall, the accuracy of simultaneous MTL predictions is comparable to the accuracy of the STL predictions. In addition, the computational cost of training HydraGNN for MTL is much lower than the original DFT calculations and also lower than training separate STL models for each property.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRSDate not supplied

GL4U: Training the next generation of bioinformaticians, one omics datatype at a time

Spaceflight modifies gene expression in every organism examined to date, including humans. Understanding how these gene expression changes affect physiology is crucial for the development of countermeasures to enable long-duration manned missions. NASA’s GeneLab project provides researchers open access to multi-omics data, including genetic and gene expression data, from spaceflight experiments that can be mined to understand the effects of spaceflight on biological systems. To ensure new knowledge generation through data re-use, it is important to maximize the number of scientists who utilize GeneLab data. Training students on the GeneLab platform is the best way to create long-term adopters of this NASA database and its tools. Turning students into future instructors and advocates will also accelerate the dissemination of these data and tools to the broader scientific community. Therefore, in collaboration with the GeneLab Educational Working Group (EWG), GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. During the bootcamp, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative. The GL4U direct training pilot program was conducted in June 2021 in collaboration with USRA and San Jose State University (SJSU). During the pilot, SJSU students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrates the capacity of GL4U for training young scientists and encouraging data re-use.

Jonathan Matthew Galazka↗
NASA NTRS1990

An ICAI architecture for troubleshooting in complex, dynamic systems

Ahab, an intelligent computer-aided instruction (ICAI) program, illustrates an architecture for simulator-based ICAI programs to teach troubleshooting in complex, dynamic environments. The architecture posits three elements of a computerized instructor: the task model, the student model, and the instructional module. The task model is a prescriptive model of expert performance that uses symptomatic and topographic search strategies to provide students with directed problem-solving aids. The student model is a descriptive model of student performance in the context of the task model. This student model compares the student and task models, critiques student performance, and provides interactive performance feedback. The instructional module coordinates information presented by the instructional media, the task model, and the student model so that each student receives individualized instruction. Concept and metaconcept knowledge that supports these elements is contained in frames and production rules, respectively. The results of an experimental evaluation are discussed. They support the hypothesis that training with an adaptive online system built using the Ahab architecture produces better performance than training using simulator practice alone, at least with unfamiliar problems. It is not sufficient to develop an expert strategy and present it to students using offline materials. The training is most effective if it adapts to individual student needs.

Fath, Janet L.↗
DOE OSTI2022

Machine‐Learning Spectral Indicators of Topology

Abstract Topological materials discovery has emerged as an important frontier in condensed matter physics. While theoretical classification frameworks have been used to identify thousands of candidate topological materials, experimental determination of materials’ topology often poses significant technical challenges. X‐ray absorption spectroscopy (XAS) is a widely used materials characterization technique sensitive to atoms’ local symmetry and chemical bonding, which are intimately linked to band topology by the theory of topological quantum chemistry (TQC). Moreover, as a local structural probe, XAS is known to have high quantitative agreement between experiment and calculation, suggesting that insights from computational spectra can effectively inform experiments. In this work, computed X‐ray absorption near‐edge structure (XANES) spectra of more than 10 000 inorganic materials to train a neural network (NN) classifier that predicts topological class directly from XANES signatures, achieving F 1 scores of 89% and 93% for topological and trivial classes, respectively is leveraged. Given the simplicity of the XAS setup and its compatibility with multimodal sample environments, the proposed machine‐learning‐augmented XAS topological indicator has the potential to discover broader categories of topological materials, such as non‐cleavable compounds and amorphous materials, and may further inform field‐driven phenomena in situ, such as magnetic field‐driven topological phase transitions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantitative Encapsulation and Homogeneity Assessment of Sol–Gel Based Nuclear Explosive Debris Simulants

Nuclear explosive debris simulants are an important material in training and validating aspects of post-detonation nuclear forensic processes. Realistic simulants should replicate several aspects of nuclear explosive debris such as the size, shape, color, density, and chemical and radiological properties. Silica particles produced via sol-gel synthesis have recently been found to successfully reproduce many of these parameters including the controllable incorporation of radionuclide content. However, to be useful as a benchmarking material for validation and verification of laboratory methodologies, radionuclide content from batch-to-batch must be reproducible. Here, in this work, we explore the variance in radionuclide distribution incorporated into sol-gel benchmarking materials with respect to sample subdivision. Results will help inform the sample sizes required to minimize variance between samples.

36 - MATERIALS SCIENCE↗
NASA NTRS1980

Progress report 3 of cooperative program for design, fabrication, and testing of high modulus composite helicopter shafting

This report describes the third phase of work, the objective of which was to overcome the excessive brittleness of the previously developed UH-1 helicopter tail rotor drive shaft design which demonstrated a shaft train weight savings of 53.1% over the current 2024-T3 aluminum shaft train. A materials impact program demonstrated exceptionally noteworthy performance of two woven constructions containing E-glass and PRD 49-III (designation later changed to KEVLAR 49) fibers in an epoxy resin matrix. Thermoplastic matrices and PRD 49-III fiber provided impact resistance at low weight which was superior to composites having the same fiber in a thermoset resin matrix. A design, fabrication, and test program showed that shaft impact resistance could be improved over the previously developed graphite composite design at a cost in shaft train rate savings. The shaft train weight savings of the most impact tolerant construction was 4.0% over the current aluminum shaft train. Alternating plies of graphite and glass appear to provide substantially greater tube impact durability than that provided by hybridization of the two fibers into one tape wound to a ply design equivalent in strength and stiffness to that of the alternating ply design. Recommendations were made to continue research work to exploit the potential for more impact-durable structures through the use of KEVLAR 49 fiber, woven structures, thermoplastic matrices and THORNEL 50-S/KEVLAR 49 blends with thermoset matrices.

Wright, C. C.↗
DOE OSTI2020

IAEA ITC28 Project Report. 28th International Training Course on the Physical Protection of Nuclear Materials and Nuclear Facilities

Sandia National Laboratories (SNL) has hosted the International Training Course on the Physical Protection of Nuclear Materials and Nuclear Facilities since 1978. This course is the flagship training course of the International Atomic Energy Agency (IAEA). On behalf of the National Nuclear Security Administration (NNSA), SNL manages, develops, and coordinates all course materials, and works closely with the IAEA to arrange all logistical details for the course. ITC-28 incorporated several new approaches based on feedback and experience with ITC-27 and earlier versions of the course. For ITC-28, an addition to the Integrated Security Facility (ISF) at SNL was a mock reactor hall with a mock reactor pool. Other facilities at the ISF include a mock processing facility, material receiving area, and central alarm station. The physical protection system at the ISF—an area that formerly housed Category I nuclear material—provides many opportunities for hands-on, real world training in the design and evaluation of a physical protection system (PPS). This document provides a brief description of ITC-28, including a summary of lessons learned and key recommendations for future development efforts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Codename: Audacious Aardvark

In mid-August, nine members of the Federal Bureau of Investigation (FBI) joined nine LANL employees for a three-day cross-training drill in which FBI forensics experts would train fissile material handlers in the work of traditional forensics on nuclear components. The drill was called Audacious Aardvark, reflecting an aardvark’s diligence and fearlessness.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Educational Consortium for Energy-related Data Science & Computation in Building Engineering Programs

The project spearheaded by Pennsylvania State University aims to address the growing need for integrating energy-focused computation and data science into building engineering education. As the demand for energy-efficient building designs and operations increases, the educational sector must adapt to equip future engineers with the necessary skills. This initiative responds to this need by developing a consortium that unites multiple institutions to enhance curriculum development, dataset curation, and resource sharing, thereby ensuring students are well-prepared for the evolving energy sector. The primary goal of the project is to establish a consortium that will develop and disseminate educational materials and training programs focused on energy-related data science and computation. Key accomplishments include the creation of a beta website for resource sharing, the development of training programs and standalone modules, and the curation of datasets accessible to the public. This effort will culminate in a curriculum that incorporates advanced modeling technologies and data science skills into building engineering programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1974

The LARSYS educational package: Instructor's notes

Materials are presented for assisting instructors in teaching the LARSYS Educational Package, which is a set of instructional materials to train people to analyze remotely sensed multispectral data. The seven units of the package are described. These units are: quantitative remote sensing, overview of the LARSYS software system, the 2780 remote terminal, demonstration of LARSYS on the 2780 remote terminal, exercises, guide to multispectral data analysis, and a case study using LARSYS for analysis of LANDSAT data.

Lindenlaub, J. C.↗
NASA NTRS2002

Portsmouth Atmospheric Science School (PASS) Project

The Portsmouth Atmospheric Science School Project (PASS) Project was granted a one-year no cost extension for 2001-2002. In year three of the project, objectives and strategies were modified based on the previous year-end evaluation. The recommendations were incorporated and the program was replicated within most of the remaining elementary schools in Portsmouth, Virginia and continued in the four middle schools. The Portsmouth Atmospheric Science School Project is a partnership, which includes Norfolk State University, Cooperating Hampton Roads Organizations for Minorities in Engineering (CHROME), NASA Langley Research Center, and the City of Portsmouth, Virginia Public Schools. The project seeks to strengthen the knowledge of Portsmouth Public Schools students in the field of atmospheric sciences and enhance teacher awareness of hands on activities in the atmospheric sciences. The project specifically seeks to: 1) increase the interest and participation of elementary and middle school students in science and mathematics; 2) strengthen existing science programs; and 3) facilitate greater achievement in core subjects, which are necessary for math, science, and technical careers. Emphasis was placed on providing training activities, materials and resources for elementary students (grades 3 - 5) and middle school students (grades 6 - 8), and teachers through a CHROME club structure. The first year of the project focused on introducing elementary students to concepts and activities in atmospheric science. Year two of the project built on the first year's activities and utilizes advanced topics and activities appropriate for middle school students. During the third year of the project, in addition to the approaches used in years one and two, emphasis was placed on activities that enhanced the Virginia Standards of Learning (SOL).

Coleman, Clarence D.↗
NASA NTRS2011

Gas Dynamic Spray Technology Demonstration Project Management

The standard practice for protecting metallic substrates in atmospheric environments is the use of an applied coating system. Current coating systems used across AFSPC and NASA contain volatile organic compounds (VOCs) and hazardous air pollutants (HAPs). These coatings are sUbject to environmental regulations at the Federal and State levels that limit their usage. In addition, these coatings often cannot withstand the high temperatures and exhaust that may be experienced by Air Force Space Command (AFSPC) and NASA structures. In response to these concerns, AFSPC and NASA have approved the use of thermal spray coatings (TSCs). Thermal spray coatings are extremely durable and environmentally friendly coating alternatives, but utilize large cumbersome equipment for application that make the coatings difficult and time consuming to repair. Other concerns include difficulties coating complex geometries and the cost of equipment, training, and materials. Gas Dynamic Spray (GOS) technology (also known as Cold Spray) was evaluated as a smaller, more maneuverable repair method as well as for areas where thermal spray techniques are not as effective. The technology can result in reduced maintenance and thus reduced hazardous materials/wastes associated with current processes. Thermal spray and GOS coatings also have no VOCs and are environmentally preferable coatings. The primary objective of this effort was to demonstrate GDS technology as a repair method for TSCs. The aim was that successful completion of this project would result in approval of GDS technology as a repair method for TSCs at AFSPC and NASA installations to improve corrosion protection at critical systems, facilitate easier maintenance activity, extend maintenance cycles, eliminate flight hardware contamination, and reduce the amount of hazardous waste generated.

Lewis, Pattie↗
NASA NTRS2008

Joint Test Plan for Gas Dynamic Spray Technology Demonstration

Air Force Space Command (AFSPC) and NASA have similar missions, facilities, and structures located in similar harsh environments. Both are responsible for a number of facilities/structures with metallic structural and non-structural components in highly and moderately corrosive environments. Regardless of the corrosivity of the environment, all metals require periodic maintenance activity to guard against the insidious effects of corrosion and thus ensure that structures meet or exceed design or performance life. The standard practice for protecting metallic substrates in atmospheric environments is the use of an applied coating system. Current coating systems used across AFSPC and NASA contain volatile organic compounds (VOCs) and hazardous air pollutants (HAPs). These coatings are subject to environmental regulations at the Federal and State levels that limit their usage. In addition, these coatings often cannot withstand the high temperatures and exhaust that may be experienced by AFSPC and NASA structures. In response to these concerns, AFSPC and NASA have approved the use of thermal spray coatings (TSCs). Thermal spray coatings are extremely durable and environmentally friendly coating alternatives, but utilize large cumbersome equipment for application that make the coatings difficult and time consuming to repair. Other concerns include difficulties coating complex geometries and the cost of equipment, training, and materials. Gas Dynamic Spray (GDS) technology (also known as Cold Spray) will be evaluated as a smaller, more maneuverable repair method as well as for areas where thermal spray techniques are not as effective. The technology can result in reduced maintenance and thus reduced hazardous materials/wastes associated with current processes. Thermal spray and GDS coatings also have no VOCs and are environmentally preferable coatings. To achieve a condition suitable for the application of a coating system, including GDS coatings, the substrate must undergo some type of surface preparation and/or depainting operation to ensure adhesion of the new coating system. The GDS unit selected for demonstration has a powder feeding system that can be used for surface preparation or coating application. The surface preparation feature will also be examined. The primary objective of this effort is to demonstrate GDS technology as a repair method for TSCs. The project will also determine the optimal GDS coating thickness for acceptable performance. Successful completion of this project will result in approval of GDS technology as a repair method for TSCs at AFSPC and NASA installations and will improve corrosion protection at critical systems, facilitate easier maintenance activity, extend maintenance cycles, eliminate flight hardware contamination, and reduce the amount of hazardous waste generated.

Lewis, Pattie↗
DOE OSTI2021

Visualization of shocked material instabilities using a fast-framing camera and XFEL four-pulse train

Many questions regarding dynamic materials could be answered by using time-resolved ultra-fast imaging techniques to characterize the physical and chemical behavior of materials in extreme conditions and their evolution on the nanosecond scale. In this work, we perform multi-frame phase-contrast imaging (PCI) of micro-voids in low density polymers under laser-driven shock compression. At the Matter in Extreme Conditions (MEC) Instrument at the Linac Coherent Light Source (LCLS), we used a train of four x-ray free electron laser (XFEL) pulses to probe the evolution of the samples. To visualize the void and shock wave interaction, here, we deployed the Icarus V2 detector to record up to four XFEL pulses, separated by 1-3 nanoseconds. In this work, we image elastic waves interacting with the micro-voids at a pressure of several GPa. Monitoring how the material’s heterogeneities, like micro-voids, dictate its response to a compressive wave is important for benchmarking the performances of inertial confinement fusion energy materials. For the first time in a single sample, we have combined an ultrafast x-ray framing camera and four XFEL pulse train to create an ultrafast movie of micro-void evolution under laser-driven shock compression. Eventually, we hope this technique will resolve the material density as it evolves dynamically under laser shock compression.

fusion↗