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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Current Status of the DOE Nuclear Criticality Safety Program Hands-on Criticality Safety Training Course at Sandia

A hands-on critical-experiment training class has been developed by the US DOE Nuclear Criticality Safety Program using the water-moderated pin-fueled critical experiments at Sandia National Laboratories. The class is offered as part of the NCSP training program for Nuclear Criticality Safety Engineers in a facility that allows attendance by both cleared and uncleared personnel. Laboratory exercises have been developed that demonstrate the effects of varying a number of the parameters that are considered important to criticality safety. Accompanying the experiments is a series of classroom presentations that emphasize the concepts that are demonstrated in the experiments.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2016

Training in Tbilisi nuclear facility provides new sampling perspectives for IAEA inspectors

Office of Nonproliferation and Arms Control- (NPAC-) sponsored training in a “cold” nuclear facility in Tbilisi, Georgia provides International Atomic Energy Agency (IAEA) inspectors with a new perspective on environmental sampling strategies. Sponsored by the Nuclear Safeguards program under the NPAC, Pacific Northwest National Laboratory (PNNL) experts have been conducting an annual weeklong class for IAEA inspectors in a closed nuclear facility since 2011. The Andronikashvili Institute of Physics and the Republic of Georgia collaborate with PNNL to provide the training, and the U.S. Department of State, the U.S. Embassy in Tbilisi and the U.S. Mission to International Organizations in Vienna provide logistical support.

environmental sampling↗
DOE OSTI2020

Computer Security Training

Computers and the internet have changed our lives for the good in many ways, but with change comes risk. In a nuclear regime, risk must be managed appropriately, and to do so, personnel must be properly trained. There are multiple ways to train personnel; however, to properly engage learners and have the key principles transferred to their work environment within their computer security programs, there are two main instructional strategies that should be used. One is for basics concepts and principles while the other is for advanced concepts that is meant to be taken away and applied in their work environment.

Training, computer security, nuclear security, ins↗
DOE OSTI2020

REopt Lite Overview & Training Exercise

This training exercise provides users with an introduction to and hands-on, interactive exploration of REopt Lite's capabilities. REopt Lite is a free, publicly available techno-economic optimization web tool for distributed energy systems, developed at the National Renewable Energy Laboratory (NREL). REopt Lite helps organizations evaluate the economic viability of grid-connected solar photovoltaics (PV), wind turbines, and battery storage; identify system sizes and battery dispatch strategies to minimize energy costs; and estimate how long a system can sustain critical load during a grid outage. The model is formulated as a mixed-integer linear program based in an underlying application programming interface (API) that is also free and publicly available. This training activity is structured as a group exercise. Participants split into eight groups and each group is assigned a different hypothetical site to model and assess the opportunity for solar PV + battery storage. Groups work together to develop results for their site and then re-convene to compare and discuss results, inputs/drivers of the analysis, and other factors impacting the decision-making process for behind-the-meter solar PV and battery storage.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

AGR-5/6/7 Test Train Disassembly & Tools Phases I & II

This document provides the process for qualification of the AGR-5/6/7 Test Train Disassembly equipment for remote operation in the HFEF Main Cell. The test train is composed of five welded stainless steel capsules that are expected to be transported in two sections. The first section will be Capsules 1 and 2. The second section will include Capsules 3, 4 and 5. The capsules range in length from about 9 inches to about 12 inches. Inside the capsules is a graphite holder that contains multiple fueled AGR compacts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

Cementitious Barriers Partnership (CBP): Training and Release of CBP Toolbox Software, Version 1.0 - 13480

The Cementitious Barriers Partnership (CBP) Project is a multi-disciplinary, multi-institutional collaboration supported by the Office of Tank Waste Management within the Office of Environmental Management of U.S. Department of Energy (US DOE). The CBP program has developed a set of integrated tools (based on state-of-the-art models and leaching test methods) that improve understanding and predictions of the long-term hydraulic and chemical performance of cementitious barriers used in nuclear applications. Tools selected for and developed under this program are intended to evaluate and predict the behavior of cementitious barriers used in near-surface engineered waste disposal systems for periods of performance up to or longer than 100 years for operating facilities and longer than 1,000 years for waste management purposes. CBP software tools were made available to selected DOE Office of Environmental Management and field site users for training and evaluation based on a set of important degradation scenarios, including sulfate ingress/attack and carbonation of cementitious materials. The tools were presented at two-day training workshops held at U.S. National Institute of Standards and Technology (NIST), Savannah River, and Hanford included LeachXS{sup TM}/ORCHESTRA, STADIUM{sup R}, and a CBP-developed GoldSim Dashboard interface. Collectively, these components form the CBP Software Toolbox. The new U.S. Environmental Protection Agency leaching test methods based on the Leaching Environmental Assessment Framework (LEAF) were also presented. The CBP Dashboard uses a custom Dynamic-link library developed by CBP to couple to the LeachXS{sup TM}/ORCHESTRA and STADIUM{sup R} codes to simulate reactive transport and degradation in cementitious materials for selected performance assessment scenarios. The first day of the workshop introduced participants to the software components via presentation materials, and the second day included hands-on tutorial exercises followed by discussions of enhancements desired by participants. Tools were revised based on feedback obtained during the workshops held from April through June 2012. The resulting improved CBP Software Toolbox, including evaluation versions of and LeachXS{sup TM}/ORCHESTRA and STADIUM{sup R} has been made available to workshop and selected other participants for further assessment. Inquiries about future workshops and requests for access to the Toolbox software can be made via the CBP web site [1]. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

NEPA training is essential: it shouldn't just be for environmental staff - 15063

In times of increasing workloads and flat budgets, it is important that the U.S. Nuclear Regulatory Commission (NRC) use available resources and techniques efficiently and effectively to achieve success in environmental reviews throughout the agency. By implementing a contract with Duke University, co-sponsored by the Council on Environmental Quality, to present courses near the NRC Headquarters in the Implementation of National Environmental Policy Act (NEPA), the NRC major program offices achieved success in training a large number of staff in NEPA in a timely and cost-effective manner. In addition, this training is positioning NRC staff to complete the Duke University graduate-level professional certificate in NEPA. (authors)

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Virtual/Augmented Reality (VR/AR):Tools for Nuclear Training, Operations and Design

Introduction: Problems: Mistakes in the field due to a lack of information (Ex: Pump repair failure rate 50%); Avoidable exposure due to multiple employees for procedure adherence and monitoring; Debates over 2D drawings can be more easily resolved. Purpose: Training: Employees can be trained more effectively. Operating: Operations can be carried out more efficiently with fewer workers required with AR. Design Review: Pull the designers and designs into VR. Design: VR: Unreal Engine, Unity, Custom. AR: HoloLens: Unity, Custom - DirectX 12.

42 ENGINEERING↗
DOE OSTI2025

A New NCSP Nuclear Criticality Safety Training and Pipeline Program

Nuclear criticality safety (NCS) training is vital and mandatory for NCS professionals to ensure the safe processing, handling, storage, and transportation of fissionable materials outside nuclear reactors. The requirement for this training is prescribed in several international and domestic guidance documents.

(NCS) Training↗
DOE OSTI2025

LOTO Training Flow Simulator Design

The LOTO Training Flow Simulator Design poster discusses the intended purpose of the simulator along with the steps taken in the design process. It highlights the two main systems designed in the primary flow loop and the bubbler system, along with a brief description of the flow calculations made to ensure the functionality of the system.

42 - ENGINEERING↗
DOE OSTI2017

A “Train-Track” Technique in Anatomic Reconstruction of SVC Bifurcation Complicated by Cardiac Tamponade: An Introspection

This report describes a stenting technique used to anatomically reconstruct superior vena cava (SVC) bifurcation in a patient with benign SVC syndrome. After recanalizing the SVC bifurcation, we exchanged two 0.035-in. wires for two 0.018-in. wires, deployed the SVC stent over these two wires (“train-track” technique), and stented each innominate vein over one wire. However, our decisions to recanalize both innominate veins, use the “buddy-wire” technique for SVC dilation, and dilate the SVC to 16 mm before stent deployment likely contributed to SVC tear, which was managed by resuscitation, SVC stent placement, and pericardial drainage. Here, we describe the steps of the train-track technique, which can be adopted to reconstruct other bifurcations; we also discuss the controversial aspects of this case.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Modulational instability and soliton trains in a model for two-mode fiber ring lasers

A model of orthogonally polarized two-field fiber ring laser with a linear gain is considered, with emphasis on the continuous-wave stability and the existence of soliton trains. The continuous-wave stability analysis is carried out within the framework of the modulational-instability approach, the variations of the gain spectrum with the modulation frequency and characteristic parameters of the model give rise to a rich variety of stability features including single-band and multiband stability regions. Seeking for pulse structures of the model, the two coupled cubic complex Ginzburg–Landau equations describing individual mode propagations are transformed into a set of coupled, first-order nonlinear ordinary-differential equations for the amplitudes and phases of the two modes. Numerical simulations of the last set of coupled equations indicate that in the anomalous dispersion regime, envelopes of the two fields are periodic trains of pulses the amplitudes of which are affected by the linear gain.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem

Three-dimensional target identification using scattering techniques requires high accuracy solutions and very fast computations for real-time predictions in some critical applications. We first train a deep neural operator (DeepONet) to solve wave propagation problems described by the Helmholtz equation in a domain without scatterers but at different wavenumbers and with a complex absorbing boundary condition. We then design two classes of fast meta-solvers by combining DeepONet with either relaxation methods, such as Jacobi and Gauss-Seidel, or with Krylov methods, such as GMRES and BiCGStab, using the trunk basis of DeepONet as a coarse-scale preconditioner. We leverage the spectral bias of neural networks to account for the lower part of the spectrum in the error distribution while the upper part is handled inexpensively using relaxation methods or fine-scale preconditioners. The meta-solvers are then applied to solve scattering problems with different shape of scatterers, at no extra training cost. We first demonstrate that the resulting meta-solvers are shape-agnostic, fast, and robust, whereas the standard standalone solvers may even fail to converge without the DeepONet. We then apply both classes of meta-solvers to scattering from a submarine, a complex three-dimensional problem. We achieve very fast solutions, especially with the DeepONet-Krylov methods, which require orders of magnitude fewer iterations than any of the standalone solvers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Inferring fracture dilation and shear slip from surface deformation utilising trained surrogate models

An important task in energy and CO 2 storage (sequestration) in the subsurface is to verify that the surrounding fractures and faults are not activated, acting as leakage pathways. This is achievable through effective and efficient Measurement, Monitoring and Verification (MMV) plans. In this work, two surrogate models are trained to captures dilation (opening) and shear deformation of fractures, and the associated surface deformation. The trained surrogate model, based on conditional Generative-Adversarial Networks (cGAN) receives fracture apertures from dilational fractures together with fracture slips from shear fractures and predicts the combined surface deformation. An inversion algorithm based on Bayesian framework is proposed to identify the geometry of both types of fractures, as well as volume of dilational fractures and deformation moment induced by shear fractures, all from the measured surface deformation data. The inversion algorithm utilises the Differential Evolution (DE) optimisation technique that has the superior performance in finding the global minimum of cost function. The proposed surrogate-assisted inversion successfully inferred the unknown dip, dip direction and the volume of the dilational fractures as well as the induced deformation moment in shear fractures. The model was further tested for the inversion of a field hydraulic fracturing tilt dataset applying different scenarios with varying unknowns to show the model's performance, as well as incorporating shear deformation for better match with the observed data.

Dilation and shear↗
DOE OSTI2026

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications

Physics-informed deep operator networks (DeepONets) have emerged as a promising approach toward numerically approximating the solution of partial differential equations (PDEs). In this work, we aim to develop further understanding of what is being learned by physics-informed DeepONets by assessing the universality of the extracted basis functions and demonstrating their potential toward model reduction with spectral methods. Results provide clarity about measuring the performance of a physics-informed DeepONet through the decays of singular values and expansion coefficients. In addition, we propose a transfer learning approach for improving training for physics-informed DeepONets between parameters of the same PDE as well as across different, but related, PDEs where these models struggle to train well. This approach results in significant error reduction and learned basis functions that are more effective in representing the solution of a PDE.

Deep operator networks↗
DOE OSTI2021

Improving scalability of parallel CNN training by adaptively adjusting parameter update frequency

Synchronous SGD with data parallelism, the most popular parallelization strategy for CNN training, suffers from the expensive communication cost of averaging gradients among all workers. The iterative parameter updates of SGD cause frequent communications and it becomes the performance bottleneck. In this paper, we propose a lazy parameter update algorithm that adaptively adjusts the parameter update frequency to address the expensive communication cost issue. Our algorithm accumulates the gradients if the difference of the accumulated gradients and the latest gradients is sufficiently small. Here, the less frequent parameter updates reduce the per-iteration communication cost while maintaining the model accuracy. Our experimental results demonstrate that the lazy update method remarkably improves the scalability while maintaining the model accuracy. For ResNet50 training on ImageNet, the proposed algorithm achieves a significantly higher speedup (739.6 on 2048 Cori KNL nodes) as compared to the vanilla synchronous SGD (276.6) while the model accuracy is almost not affected (<0.2% difference).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data

Machine-learned interatomic models represent a significant advancement in simulation methods, extending the predictive ability of first-principles methods to previously inaccessible length and time scales. However, the data-driven nature of these models can lead to difficult-to-detect errors that can compromise prediction accuracy. To address this challenge, we introduce a novel fingerprinting approach based on the Chebyshev Interaction Model for Efficient Simulation (ChIMES) ML-IAM graph-based descriptor. Our strategy enables efficient and statistically rigorous analysis of system configurations used in ML-IAM training and those generated by their application, e.g., in molecular dynamics simulations. We demonstrate that these fingerprints can effectively assess novelty of a configuration relative to an existing data set and determine dissimilarity among individual configurations, which are two key tasks in workflows for active learning-based ML-IAM training, data set curation, and on-the-fly uncertainty quantification.

36 MATERIALS SCIENCE↗