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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2024

Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data

Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Performance Evaluation of a High Salinity Produced Water Treatment Train: Chemical Analysis and Aryl Hydrocarbon Activation

Water scarcity and increased energy demands have put a strong focus on improving industries at the heart of the water–energy nexus. Treatment of oil and gas produced water (PW) can help reduce freshwater consumption during hydraulic fracturing, especially in arid regions, while also removing harmful contaminants from entering the environment. However, it is also difficult to treat because PW contains high concentrations of many environmentally toxic contaminants, which require complex and expensive treatment processes to achieve their removal. To demonstrate the possibility of PW treatment and reuse in the O&G industry, a comprehensive environmental toxicity and water quality analysis throughout a five-process treatment train was performed on high salinity (>120 g/L) Permian basin raw PW. Here, the concentrations of naturally occurring radioactive materials were reduced by over 99%, total organic carbon was reduced by 93%, and inorganic constituents, including total dissolved solids, were reduced by over 99%. Compounds that induced the aryl hydrocarbon receptor and caused cytotoxicity in MCF-7 cells were also removed. Overall, the results of this study show that a short treatment train (five distinct unit processes) can be effective in treating PW to a level suitable for use outside of the oil industry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Train Traffic as a Powerful Noise Source for Monitoring Active Faults With Seismic Interferometry

Laboratory experiments report that detectable seismic velocity changes should occur in the vicinity of fault zones prior to earthquakes. However, operating permanent active seismic sources to monitor natural faults at seismogenic depth is found to be nearly impossible to achieve. We show that seismic noise generated by vehicle traffic, and especially heavy freight trains, can be turned into a powerful repetitive seismic source to continuously probe the Earth's crust at a few kilometers depth. Results of an exploratory seismic experiment in Southern California demonstrate that correlations of train-generated seismic signals allow daily reconstruction of direct P body waves probing the San Jacinto Fault down to 4-km depth. This new approach may facilitate monitoring most of the San Andreas Fault system using the railway and highway network of California.

58 GEOSCIENCES↗
DOE OSTI2025

Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect

Abstract Predicting forced, long‐term radiative feedbacks from internal climate variability has been a decades‐long quest in climate science. We train a convolutional neural network (CNN) to predict annual‐ and global‐mean top of the atmosphere radiation anomalies from time‐varying maps of near‐surface temperature in climate models. Trained on internal variability alone, the nonlinear CNN can predict radiation under strong climate change, outperforms a regularized linear regression approach, and works within and across different climate models. We show with explainable artificial intelligence methods that the CNN draws predictive skill from physically meaningful regions but at much smaller spatial scales than currently assumed.

Rugenstein, Maria [Colorado State University Fort ↗
DOE OSTI2024

In Situ Velocity‐Strain Sensitivity Near the San Jacinto Fault Zone Analyzed Through Train Tremors

Abstract We utilize train tremors as P‐wave seismic sources to investigate velocity‐strain sensitivity near the San Jacinto Fault Zone. A dense nodal array deployed at the Piñon Flat Observatory is used to detect and identify repeating train energy emitted from a railway in the Coachella valley. We construct P‐wave correlation functions across the fault zone and estimate the spatially averaged dt / t versus strain sensitivity to be 6.25 × 10 4 . Through numerical simulations, we explore how the sensitivity decays exponentially with depth. The optimal solution reveals a subsurface sensitivity of 1.2 × 10 5 and a depth decay rate of 0.05 km −1 . This sensitivity aligns with previous findings but is toward the higher end, likely due to the fractured fault‐zone rocks. The depth decay rate, previously unreported, is notably smaller than assumed in empirical models. This raises the necessity of further investigations of this parameter, which is crucial to study stress and velocity variations at seismogenic depth.

Geology↗