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At least 487 records · Page 27

All sources NASA NTRS DOE OSTI

DOE OSTI2016

AGR-3/4 Irradiation Test Train Disassembly and Component Metrology First Look Report

The AGR-3/4 experiment was designed to study fission product transport within graphitic matrix material and nuclear-grade graphite. To this end, this experiment consisted of 12 capsules, each fueled with 4 compacts containing UCO TRISO particles as driver fuel and 20 UCO designed-to-fail (DTF) fuel particles in each compact. The DTF fuel was fabricated with a thin pyrocarbon layer which was intended to fail during irradiation and provide a source of fission products. These fission products could then migrate through the compact and into the surrounding concentric rings of graphitic matrix material and/or nuclear graphite. Through post-irradiation examination (PIE) of the rings (including physical sampling and gamma scanning) fission product concentration profiles within the rings can be determined. These data can be used to elucidate fission product transport parameters (e.g. diffusion coefficients within the test materials) which will be used to inform and refine models of fission product transport. After irradiation in the Advanced Test Reactor (ATR) had been completed in April 2014, the AGR-3/4 experiment was shipped to the Hot Fuel Examination Facility (HFEF) at the Materials and Fuels Complex (MFC) for inspection, disassembly, and metrology. The AGR-3/4 test train was received at MFC in two separate shipments between February and April 2015. Visual examinations of the test train exterior did not indicate dimensional distortion, and only two small discolored areas were observed at the bottom of Capsules 8 and 9. No corresponding discoloration was found on the inside of these capsules, however. Prior to disassembly, the two test train sections were subject to analysis via the Precision Gamma Scanner (PGS), which did not indicate that any gross fuel relocation had occurred. A series of specialized tools (including clamps, cutters, and drills) had been designed and fabricated in order to carry out test train disassembly and recovery of capsule components (graphite rings and fuel compacts). This equipment performed well for separating each capsule in the test train and extracting the capsule components. Only a few problems were encountered. In one case, the outermost ring (the sink ring) was cracked during removal of the capsule through tubes. Although the sink ring will be analyzed in order to obtain a mass balance of fission products in the experiment, these cracks do not pose a major concern because the sink ring will not be analyzed in detail to obtain the spatial distribution of fission products. In Capsules 4 and 5, the compacts could not be removed from the inner rings. Strategies for removing the compacts are being evaluated. Sampling the inner rings with the compacts in-place is also an option. Dimensional measurements were made on the compacts, inner rings, outer rings, and sink rings. The diameters of all compacts decreased by 0.5 to 2.0 %. Generally, the extent of diametric shrinkage increased linearly with increasing neutron fluence. Most compact lengths also decreased. Compact lengths decreased with increasing fluence, reaching maximum shrinkage of about 0.9 % at a fast fluence of 4.0x1025 n/m2 E > 0.18 MeV. Above this fluence, the extent of length shrinkage appeared to decrease with fluence, and two compacts from Capsule 7 were found to have slightly increased in length (< 0.1 %) after a fluence of 5.2x1025 n/m2.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2018

Exploring Flexible Communications for Streamlining DNN Ensemble Training Pipelines

Parallel training of a Deep Neural Network (DNN) ensemble on a cluster of nodes is a common practice to train multiple models in order to construct a model with a higher prediction accuracy. Existing ensemble training pipelines can perform a great deal of redundant operations, resulting in unnecessary CPU usage, or even poor pipeline performance. In order to remove these redundancies, we need pipelines with more communication flexibility than existing DNN frameworks can provide. This project investigates a series of designs to improve pipeline flexibility and adaptivity, while also increasing performance. We implement our designs using Tensorflow with Horovod, and test it using several large DNNs in a large scale GPU cluster, Titan supercomputer at Oak Ridge National Lab. Our results show that the CPU time spent during training is reduced by 2-11X. Furthermore, our implementation can achieve up to 10X speedups when CPU core limits are imposed. Our best pipeline also reduces the average power draw of the ensemble training process by 5-16% when compared to the baseline.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

UTrain Manual for Training Administrators

This Institutional Training Administrative Procedure provides the business rules that must be followed when working in UTrain. As commercial off-the-shelf software with customizations, UTrain cannot address all the implementation business requirements, and thus, administrative business rules and processes were defined and documented. Many business rules were incorporated into the UTrain training materials. UTrain is the Laboratory’s official learning management system. UTrain is the system of record for all Laboratory training, qualification, certification, and worker authorization. All training at LANL must be documented in UTrain. The detailed steps of how to operate UTrain are contained in the UTrain courses and training manuals and in the UTrain job aids, not in this document.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2020

Metaevaluation Final Report: Sandia National Laboratories Recorded Information Management Training Program Evaluation

For my internship project, I chose to evaluate the training program for the Sandia National Laboratories (SNL) Recorded Information Management (RIM) Program. The purpose of the evaluation was to identify: Where the current training meets federal and corporate requirements and if not, what gaps exist; If the training is accessible in a consistent location; If the training contains a consistent message about the responsibility of managing documents; If the training is organized by records management subject areas.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2020

TA-55 Forensic Support Operations Cross-Training Exercise

As part of the annual FBI/LANL training schedule, a joint training exercise was conducted during the week of 10FEB2020 with FBI HEAT members and LANL Fissile Material Handlers (FMHs). The joint training exercise was conducted in a non-radiological area to reduce cost and ensure the test object would not be contaminated. The exercise test object was fabricated by LANL using surrogate materials to represent an object containing Special Nuclear Material (SNM). Working with Jim Blankenship and Kevin Swearingen, functional requirements of the test object included the following: unclassified, disassembly (to include screws), and moderately heavy. To meet the objectives, the test object was fabricated by welding a custom aluminum box (8” x 8” x 12”) with a lid which was secured by four (4) screws. The custom box housed a track and field shot put (16 lbs.) that was anchored by a hose clamp. The intent of the training was to disassemble the test object and conduct traditional forensic determinations on the parts. This lessons learned report documents all of the associated aspects related to the joint training effort.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

The Need for Nuclear Deterrent Fundamentals Training

NNSA policy requires that personnel working in the Nuclear Security Enterprise (NSE) receive comprehensive training to be fully capable before they begin their work and that personnel receive ongoing training to maintain their capability and competence. However, the loss of experienced NSE staff through retirements and the influx of personnel who are inexperienced and untrained in nuclear weapons technology make these training requirements difficult to achieve. This paper presents an approach for developing and delivering a nuclear deterrent (ND) fundamentals curriculum to respond to these challenges. The training approach leverages existing material already used by Sandia National Laboratories in the ND training arena and proposes to deliver the material in an e-learning format as much as possible. The approach also seeks to maximize the amount of material to be delivered in an unclassified setting.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2024

EV Champion Training Webinar 1: ZEV and EV Charging Fundamentals [Slides]

The Electric Vehicle (EV) Champion Training Series, hosted by the National Renewable Energy Laboratory (NREL), is tailored for fleet managers, facility managers, and other stakeholders involved in the deployment of EVs and charging stations. This series equips participants with the skills and knowledge necessary to become subject matter experts in EV implementation. This is the first training in a four-part series and serves as an introductory training. This training covers fundamental topics such as EV and charging technology, utility basics, and financial considerations for EVs. Additionally, this session introduces the Federal Fleet ZEV Ready Center framework, a one-stop location for federal fleet electrification resources. Participants will gain a solid foundation to support the effective deployment and management of EVs and their infrastructure. Visit the Federal Fleet ZEV Ready Center website to learn more about the ZEV Ready Center process. EV Champion Training Webinar 1: ZEV and EV Charging Fundamentals will enable attendees to: Identify EV and EV charging technology fundamentals; Recognize EV market and GSA Schedule options; Identify utility basics, incentives, and rates as they relate to EV charging; and Identify methods to calculate life cycle costs for EVs and gasoline vehicles.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2025

EV Champion Training Webinar 2: ZEV and EV Charging Planning [Slides]

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Building Datasets and Training Methods for ML Based Magnet Quench Detection

Detecting quenches in superconducting (SC) magnets during training is a challenging process that involves capturing physical events that occur at different frequencies and appear as various signal features. These events may be correlated across instrumentation type, thermal cycle, and ramp. These events together build a more complete picture of continuous processes occurring in the magnet, and may allow us to flag potential precursors for quench detection. We present our work on building an automatic machine learning (ML) based quench detection system. We build upon our existing work on unsupervised auto-encoders for acoustic sensors and quench antenna (QA) by first establishing a supervised ML training pipeline. We show the results of an event tagging, analysis, and simulation framework on our QA and acoustic data which are used concurrently to build a training dataset for a supervised implementation. We then show how this supervised training can be used as a prior in a semi-supervised framework and compare this to the unsupervised neural network auto-encoder performance.This allows us to have a more concrete understanding of the performance of our algorithms relative to physical events occurring in the magnet, and also provides a baseline software tool to generically evaluate our quench prediction autoencoders under completely unsupervised, supervised, and semi-supervised training conditions.

Khan, Maira [Fermilab]↗

DOE OSTI2025

Virtual Reality for Shoot/No-Shoot Decision Training in Law Enforcement: A Literature Review and Research Agenda

Virtual reality (VR) can materially improve “shoot / no-shoot” (SNS) training by giving officers realistic, repeatable practice making high-stakes decisions under pressure. Traditional tools—live-fire ranges and video simulators—build basics, but they cannot adapt to each officer in real time or fully mirror the complexity of the field. VR closes that gap by creating immersive scenarios that are safer, more flexible, easier to scale across units, and able to capture objective performance data. SNS decisions are not just about marksmanship; they rely on perception, judgment, memory, and the ability to hold fire when a threat is uncertain. Effective training therefore needs realism, decision complexity, and branching outcomes that reflect the true consequences of choices. These elements strengthen recognition of hostile intent while reducing false positives and building the self-control required in ambiguous situations. VR brings specific advantages: dynamic environments, full-body interaction, and the ability to measure performance with precision—enabling targeted feedback and better transfer of learning to the street. At the same time, responsible deployment must address scenario quality (credible environments and behaviors), lawful decision models, and user wellbeing (appropriate stress levels, comfort, and safety). Sandia’s VIPER Lab is positioned to lead this work. The team combines human-performance science, AI/ML, and VR/AR development with a deep equipment bench (e.g., omnidirectional treadmill, eye-tracking, haptics, multiple HMDs). This ecosystem supports building and validating next-generation SNS training that is immersive, measurable, and trustworthy. Bottom line: Investment in VR-enabled SNS training that blends evidence-based design with careful validation and legal safeguards is expected to pay off in safer, more consistent decision-making and improved community trust, delivered through training that is practical to deploy at scale.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2026

Cybersecurity Workforce Training for SMR Integration into Distribution Grids: A Competency Framework and Containerized Hands-On Lab for the SMR/DER/Microgrid Boundary

Small modular reactors (SMRs) and microreactors are entering the U.S. distribution grid as synchronous generation on feeders designed for loads and inverter-based distributed energy resources (DERs). No existing cybersecurity training program addresses this intersection of nuclear operations, DER management, and operational technology security. As subcontractor to Iowa State University on the CyDERMS Center, Argonne analyzed the relevant standards and training landscape, translated the resulting gaps into a twelve-objective competency framework across distribution-operator and graduate-analyst role tracks, and built a containerized training lab using a ∼400-bus composite grid model behind a realistically simulated Modbus TCP SCADA stack. The analysis isolates the balance-of-plant / energy-management-system (BOP/EMS) boundary as the critical jurisdictional seam where, as of March 2026, neither NRC nor NERC CIP cleanly claims cybersecurity responsibility for distribution-connected SMRs. The framework maps each objective across NIST CSF 2.0, ISA/IEC 62443, NIST NICE Task–Knowledge–Skill statements, and NRC RG 5.71 awareness-and-training controls. The training lab implements operator-recognition assessment scenarios spanning grid-side disturbances and telemetry-layer anomalies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Datasets for Custom-trained Machine-learning Interatomic Potentials: Nitric Acid Aqueous Solution

This dataset was generated using an iterative active learning strategy with the ArcaNN software package (https://github.com/arcann-chem/arcann_training) to train machine-learning interatomic potentials (MLIPs) for aqueous nitric acid. Each active-learning cycle consisted of three stages: (1) training, (2) exploration, and (3) labeling. The initial training set comprised approximately 800 randomly selected configurations from a previous study by Lewis et al. (https://doi.org/10.1021/jp205510q), which investigated nitric acid solutions at 2, 3, 4, and 5 mol/L. For all configurations, single-point calculations of atomic forces and total energies were performed at the quantum density functional theory BLYP-D2 and PBE-D3 levels of theory using the CP2K Quickstep module. Valence electrons were treated explicitly, while core electrons on all atoms were represented by norm-conserving Goedecker–Teter–Hutter (GTH) pseudopotentials. Long-range dispersion interactions were accounted for using Grimme dispersion corrections. Wave functions were expanded in a mixed Gaussian-and-plane-wave scheme using TZV2P-MOLOPT basis sets for all elements and an 800 Ry auxiliary plane-wave cutoff for the electron density. Self-consistent field convergence was accelerated using orbital transformation and Direct Inversion in the Iterative Subspace, with a convergence threshold of 10^{-6}. All single-point calculations were carried out in periodic orthorhombic cells whose dimensions match those of the molecular configurations sampled from earlier trajectories. The CELL_REF keyword in CP2K was used to define a fixed reference cell, ensuring consistency in the reference data used for MLIP training, particularly when cell fluctuations are present in NpT simulations. The resulting high-fidelity energies and forces constitute the ground-truth labels used to train the MLIPs contained in this dataset.

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗

DOE OSTI2025

Dataset, Code, and Models for Training Deep Learning Potentials for Low Temperature Plasma-Surface Interactions

This repository contains datasets, training scripts, and finished models, and test simulations used in the development of DeepREBO— a machine-learned interatomic potential trained to emulate the REBO2 empirical potential. The data was generated to study deep potential development for simulations of plasma-surface interactions. It uses an active learning framework, starting from a minimal dataset and iteratively expanding it. Included are those generated datasets, the trained models, and simulations used to evaluate the performance of the training process. This resource supports reproducibility and provides a reference framework for training deep potentials in plasma-surface interaction studies.

active learning↗

DOE OSTI2017

Data-driven, Interpretable Photometric Redshifts Trained on Heterogeneous and Unrepresentative Data

We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data-driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine learning methods and template fitting methods by building template SEDs directly from the spectroscopic training data. This is made computationally tractable with Gaussian processes operating in flux–redshift space, encoding the physics of redshifts and the projection of galaxy SEDs onto photometric bandpasses. This method alleviates the need to acquire representative training data or to construct detailed galaxy SED models; it requires only that the photometric bandpasses and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data with reliable redshifts, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the i -magnitude-selected, spectroscopically confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST ) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes or to simulate populations of galaxies with realistic fluxes and redshifts, for example.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1972

Stimulus control in pigeons after extended discriminative training

The effects of amount of training on conditioned inhibition and on the degree of stimulus control were studied using pigeons. The ability of an S- associated with non-reinforcement of suppress positive reinforced behavior was acquired very rapidly during discriminative training. Increased S+, S- training appeared to weaken this conditioned inhibitory effect while at the same time more S+ training apparently increased the amount of external inhibition (non-conditioned inhibition) of positively reinforced behavior by a novel stimulus. Behavioral contrast and incremental generalization gradients along the S- dimension (inhibitory dimensional control) were absent at all stages of training. Behavioral contrast and inhibitory dimensional control are therefore not necessary concomitants of conditioned inhibition by an S-. A new method of assessing the suppressive effects of stimuli during generalization tests was described.

Yarczower, M.↗

NASA NTRS1981

Guidelines for Line-Oriented Flight Training, Volume 1

Line-Oriented Flight Training (LOFT) is a developing training technology which synthesizes high-fidelity aircraft simulation and high-fidelity line-operations simulation to provide realistic, dynamic pilot training in a simulated line environment. LOFT is an augmentation of existing pilot training which concentrates upon command, leadership, and resource management skills. This report, based on an NASA/Industry workshop held in January, 1981, is designed to serve as a handbook for LOFT users. In addition to providing background information, guidelines are presented for designing LOFT scenarios, conducting real-time LOFT operations, pilot debriefing, and instructor qualification and training. The final chapter addressed other uses of LOFT and line-operations (or full-mission) simulation.

Lauber, J. K.↗

NASA NTRS1981

Group 4: Instructor training and qualifications

Each professional instructor or check airman used in LOFT training course should complete an FAA approved training course in the appropriate aircraft type. Instructors used in such courses need not be type-rated. If an instructor or check airman who is presently not line-qualified is used as a LOFT instructor, he or she should remain current in line-operational procedures by observing operating procedures from the jump seat on three typical line segments pr 90 days on the appropriate aircraft type. ("Line qualification" means completion as a flight crew member of at least three typical line segments per 90 days on the appropriate aircraft type.) The training should include the requirement of four hours of LOFT training, in lieu of actual aircraft training or line operating experience.

Sessa, R.↗

NASA NTRS1982

Assessment of MSFC's supervisory training programs and courses

Courses and special programs to train supervisors at Marshall Space Flight Center (MSFC) were to determine the adequacy of the present MSFC Supervisory Training Program and to recommend changes, if appropriate. The content, procedures, and student evaluations of the required Office of Personnel Management (OPM) 80 hours training for supervisors, the optional 120 hours, the MSFC Management Development Program (MDP), NASA's Management Education Program (MEP), various OPM and special contract programs, pertinent procedural guidelines, regulations, and letters, as well as various MSFC computer reports which indicate who took what training were analyzed. Various interviews with MSFC personnel involved in training are included. Recommendations consist of: (1) the choice of courses selected for the basic required OPM 80 hours be improved; (2) the optional 120 hours be discontinued and a shorter module be developed dealing with managerial decision making and human relations skills; (3) the MDP and MEP be continued as at present; and (4) that a broad array of developmental strategies be incorporated to provide a variety of opportunities for supervisory improvement.

Brindley, T. A.↗