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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

An Incremental Tensor Train Decomposition Algorithm

We present a new algorithm for incrementally updating the tensor train decomposition of a stream of tensor data. This new algorithm, called the tensor train incremental core expansion (TT-ICE) improves upon the current state-of-the-art algorithms for compressing in tensor train format by developing a new adaptive approach that incurs significantly slower rank growth and guarantees compression accuracy. This capability is achieved by limiting the number of new vectors appended to the TT-cores of an existing accumulation tensor after each data increment. These vectors represent directions orthogonal to the span of existing cores and are limited to those needed to represent a newly arrived tensor to a target accuracy. We provide two versions of the algorithm: TT-ICE and TT-ICE accelerated with heuristics (TT-ICE*). Here, we provide a proof of correctness for TT-ICE and empirically demonstrate the performance of the algorithms in compressing large-scale video and scientific simulation datasets. Compared to existing approaches that also use rank adaptation, TT-ICE* achieves 57× higher compression and up to 95% reduction in computational time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC

We propose a neural network training method capable of accounting for the effects of systematic variations of the data model in the training process and describe its extension towards neural network multiclass classification. The procedure is evaluated on the realistic case of the measurement of Higgs boson production via gluon fusion and vector boson fusion in the τ τ decay channel at the CMS experiment. The neural network output functions are used to infer the signal strengths for inclusive production of Higgs bosons as well as for their production via gluon fusion and vector boson fusion. We observe improvements of 12 and 16% in the uncertainty in the signal strengths for gluon and vector-boson fusion, respectively, compared with a conventional neural network training based on cross-entropy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

QuantumNAT: quantum noise-aware training with noise injection, quantization and normalization

Parameterized Quantum Circuits (PQC) are promising towards quantum advantage on near-term quantum hardware. However, due to the large quantum noises (errors), the performance of PQC models has a severe degradation on real quantum devices. Take Quantum Neural Network (QNN) as an example, the accuracy gap between noise-free simulation and noisy results on IBMQ-Yorktown for MNIST-4 classification is over 60%. Existing noise mitigation methods are general ones without leveraging unique characteristics of PQC; on the other hand, existing PQC work does not consider noise effect. To this end, we present QuantumNAT, a PQC-specific framework to perform noise-aware optimizations in both training and inference stages to improve robustness. We experimentally observe that the effect of quantum noise to PQC measurement outcome is a linear map from noise-free outcome with a scaling and a shift factor. Motivated by that, we propose post-measurement normalization to mitigate the feature distribution differences between noise-free and noisy scenarios. Furthermore, to improve the robustness against noise, we propose noise injection to the training process by inserting quantum error gates to PQC according to realistic noise models of quantum hardware. Lastly, post-measurement quantization is introduced to quantize the measurement outcomes to discrete values, achieving the denoising effect. Extensive experiments on 8 classification tasks using 6 quantum devices demonstrate that QuantumNAT improves accuracy by up to 43%, and achieves over 94% 2-class, 80% 4-class, and 34% 10-class classification accuracy measured on real quantum computers. The code for construction and noise-aware training of PQC is available in the TorchQuantum library.

Wang, Hanrui↗
DOE OSTI2023

Multiobjective Hyperparameter Optimization for Deep Learning Interatomic Potential Training Using NSGA-II

Deep neural network (DNN) potentials are an emerging tool for simulation of dynamical atomistic systems, with the promise of quantum mechanical accuracy at speedups of 10000$\times$. As with other DNN methods, hyperparameters used during training can make a substantial difference in model accuracy, and optimal settings vary with dataset. To enable rapid tuning of hyperparameters for DNN potential training, we developed a scalable multiobjective optimization evolutionary algorithm for supercomputers and tested it on the Summit system at the Oak Ridge Leadership Computing Facility (OLCF). The multiobjective approach is required due to the coupling of two learned values defining the potential: the energy and force. Using a large-scale implementation of the NSGA-II algorithm adapted for training DNN potentials, we discovered several optimal multiobjective combinations, including best choices of activation functions, learning rate scaling scheme, and pairing of the two radial cutoffs used in the three dimensional descriptor function.

Coletti, Mark↗
DOE OSTI2025

Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training

With the end of Moore’s law and Dennard scaling, efficient training increasingly requires rethinking data volume. Can we train better models with significantly less data via intelligent subsampling? To explore this, we develop SICKLE, a sparse intelligent curation framework for efficient learning, featuring a novel maximum entropy (MaxEnt) sampling approach, scalable training, and energy benchmarking. We compare MaxEnt with random and phase-space sampling on large direct numerical simulation (DNS) datasets of turbulence. Evaluating SICKLE at scale on Frontier, we show that subsampling as a preprocessing step can, in many cases, improve model accuracy and substantially lower energy consumption, with observed reductions of up to 38×.

Brewer, Wes [ORNL] (ORCID:0000000236393956)↗
DOE OSTI2021

Humming Trains in Seismology: An Opportune Source for Probing the Shallow Crust

Abstract Seismologists are eagerly seeking new and preferably low-cost ways to map and track changes in the complex structure of the top few kilometers of the crust. By understanding it better, they can build on what is known regarding important, practical issues. These include telling us whether imminent earthquakes and volcanic eruptions are generating telltale underground signs of hazard, about mitigation of induced seismicity such as from deep injection of wastewater, how the Earth and its atmosphere couple, and where accessible natural resources are. Passive seismic imaging usually relies on blind correlations within extended recordings of Earth’s ceaseless “hum” or coda of well-mixed, small vibrations. In this article, we propose a complementary approach. It is seismic interferometry using opportune sources—specifically ones not stationary in time and moving in a well-understood configuration. Its interpretation relies on an accurate understanding of how these sources radiate seismic waves, precise timing, careful placement of pairs of listening stations, and seismic phase differentiation (surface and body waves). Massive freight trains were only recently recognized as such a persistent, powerful cultural (human activity-caused) seismic source. One train passage may generate a tremor with an energy output of a magnitude 1 earthquake and be detectable for up to 100 km from the track. We discuss the source mechanisms of train tremors and review the basic theory on sources. Finally, we present case studies of body- and surface-wave retrieval as an aid to mineral exploration in Canada and to monitoring of a southern California fault zone. We believe noise recovery from this new signal source, together with dense data acquisition technologies such as nodes or distributed acoustic sensing, will deeply transform our ability to monitor activity in the shallow crust at sharpened resolution in time and space.

Geochemistry & Geophysics↗
DOE OSTI2013

Training and Research on Probabilistic Hydro-Thermo-Mechanical Modeling of Carbon Dioxide Geological Sequestration in Fractured Porous Rocks

Colorado School of Mines conducted research and training in the development and validation of an advanced CO 2 GS (Geological Sequestration) probabilistic simulation and risk assessment model. CO 2 GS simulation and risk assessment is used to develop advanced numerical simulation models of the subsurface to forecast CO 2 behavior and transport; optimize site operational practices; ensure site safety; and refine site monitoring, verification, and accounting efforts. As simulation models are refined with new data, the uncertainty surrounding the identified risks decrease, thereby providing more accurate risk assessment. The models considered the full coupling of multiple physical processes (geomechanical and fluid flow) and describe the effects of stochastic hydro-mechanical (H-M) parameters on the modeling of CO 2 flow and transport in fractured porous rocks. Graduate students were involved in the development and validation of the model that can be used to predict the fate, movement, and storage of CO 2 in subsurface formations, and to evaluate the risk of potential leakage to the atmosphere and underground aquifers. The main major contributions from the project include the development of: 1) an improved procedure to rigorously couple the simulations of hydro-thermo-mechanical (H-M) processes involved in CO 2 GS; 2) models for the hydro-mechanical behavior of fractured porous rocks with random fracture patterns; and 3) probabilistic methods to account for the effects of stochastic fluid flow and geomechanical properties on flow, transport, storage and leakage associated with CO 2 GS. The research project provided the means to educate and train graduate students in the science and technology of CO 2 GS, with a focus on geologic storage. Specifically, the training included the investigation of an advanced CO 2 GS simulation and risk assessment model that can be used to predict the fate, movement, and storage of CO 2 in underground formations, and the evaluation of the risk of potential CO 2 leakage to the atmosphere and underground aquifers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Recovery Act: Geologic Sequestration Training and Research

Work under the project entitled "Geologic Sequestration Training and Research," was performed by the University of Alabama at Birmingham and Southern Company from December 1, 2009, to June 30, 2013. The emphasis was on training of students and faculty through research on topics central to further development, demonstration, and commercialization of carbon capture, utilization, and storage (CCUS). The project had the following components: (1) establishment of a laboratory for measurement of rock properties, (2) evaluation of the sealing capacity of caprocks, (3) evaluation of porosity, permeability, and storage capacity of reservoirs, (4) simulation of CO 2 migration and trapping in storage reservoirs and seepage through seal layers, (5) education and training of students through independent research on rock properties and reservoir simulation, and (6) development of an advanced undergraduate/graduate level course on coal combustion and gasification, climate change, and carbon sequestration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Electric Vehicle Service Personnel Training Program

As the share of hybrid, plug-in hybrid (PHEV), electric (EV) and fuel-cell (FCV) vehicles grows in the national automotive fleet, an entirely new set of diagnostic and technical skills needs to be obtained by the maintenance workforce. Electrically-powered vehicles require new diagnostic tools, technique and vocabulary when compared to existing internal combustion engine-powered models. While the manufacturers of these new vehicles train their own maintenance personnel, training for students, independent working technicians and fleet operators is less focused and organized. This DOE-funded effort provided training to these three target groups to help expand availability of skills and to provide more competition (and lower consumer cost) in the maintenance of these hybrid- and electric-powered vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2013

Alliance for Sequestration Training, Outreach, Research & Education

The Sequestration Training, Outreach, Research and Education (STORE) Alliance at The University of Texas at Austin completed its activity under Department of Energy Funding (DE-FE0002254) on September 1, 2013. The program began as a partnership between the Institute for Geophysics, the Bureau of Economic Geology and the Petroleum and Geosystems Engineering Department at UT. The initial vision of the program was to promote better understanding of CO 2 utilization and storage science and engineering technology through programs and opportunities centered on training, outreach, research and technology transfer, and education. With over 8,000 hrs of formal training and education (and almost 4,500 of those hours awarded as continuing education credits) to almost 1,100 people, STORE programs and activities have provided benefits to the Carbon Storage Program of the Department of Energy by helping to build a skilled workforce for the future CCS and larger energy industry, and fostering scientific public literacy needed to continue the U.S. leadership position in climate change mitigation and energy technologies and application. Now in sustaining mode, the program is housed at the Center for Petroleum and Geosystems Engineering, and benefits from partnerships with the Gulf Coast Carbon Center, TOPCORP and other programs at the university receiving industry funding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Training Students to Analyze Spatial and Temporal Heterogeneities in Reservoir and Seal Petrology, Mineralogy, and Geochemistry: Implications for CO{sub 2} Sequestration Prediction, Simulation, and Monitoring

The objective of this project was to expose and train multiple students in geological tools that are essential to reservoir characterization and geologic sequestration including but not limited to advanced petrological methods, mineralogical methods, and geochemical methods; core analysis, and geophysical well-log interpretation. These efforts have included training of multiple students through geologically based curriculum and research using advanced petrological, mineralogical, and geochemical methods. In whole, over the last 3+ years, this award has supported 5,828 hours of student research, supporting the work of several graduate and undergraduate students. They have all received training directly related to ongoing CO{sub 2} sequestration demonstrations. The students have all conducted original scientific research on topics related to understanding the importance of lithological, textural, and compositional variability in formations that are being targeted as CO{sub 2} sequestration reservoirs and seals. This research was linked to the Mount Simon Sandstone reservoir and overlying Eau Claire Formation seal in the Illinois Basin- a system where over one million tons of CO{sub 2} are actively being injected with the first large-scale demonstration of anthropogenic CO{sub 2} storage in the U.S. Student projects focused specifically on 1) reservoir porosity characterization and evaluation, 2) petrographic, mineralogical, and geochemical evidence of fluid-related diagenesis in the caprock, 3) textural changes in reservoir samples exposed to experimental CO{sub 2} + brine conditions, 4) controls on spatial heterogeneity in composition and texture in both the reservoir and seal, 5) the implications of small-scale fractures within the reservoir, and 6) petrographic and stable isotope analyses of carbonates in the seal to understand the burial history of the system. The student-led research associated with this project provided real-time and hands-on experience with a relevant CO{sub 2} system, provided relevant information to the regional partnerships who are working within these formations, and provides more broadly applicable understanding and method development for other carbon capture and storage systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Geological Sequestration Training and Research Program in Capture and Transport: Development of the Most Economical Separation Method for CO2 Capture

The project provided hands-on training and networking opportunities to undergraduate students in the area of carbon dioxide (CO2) capture and transport, through fundamental research study focused on advanced separation methods that can be applied to the capture of CO2 resulting from the combustion of fossil-fuels for power generation . The project team’s approach to achieve its objectives was to leverage existing Carbon Capture and Storage (CCS) course materials and teaching methods to create and implement an annual CCS short course for the Tuskegee University community; conduct a survey of CO2 separation and capture methods; utilize data to verify and develop computer models for CO2 capture and build CCS networks and hands-on training experiences. The objectives accomplished as a result of this project were: (1) A comprehensive survey of CO2 capture methods was conducted and mathematical models were developed to compare the potential economics of the different methods based on the total cost per year per unit of CO2 avoidance; and (2) Training was provided to introduce the latest CO2 capture technologies and deployment issues to the university community.

Vahdat, Nader↗
DOE OSTI2015

Training package 1 for slitting data analysis

This document and accompanying files are intended as a first training package on how to analyze slitting data. The end goal is to have Idaho National Laboratory (INL) personnel trained to analyze future slitting data taken in the INL Hot Cell on clad, Low-Enriched Uranium (LEU) fuel plates. This first data package will cover data analysis for a monolithic material (as compared to a layered material like the clad fuel plates). The additional issues for layered specimens will be covered in a future training package.

42 ENGINEERING↗
DOE OSTI2016

Can YAG screen accept LEReC bunch train?

LEReC RF diagnostic beamline is supposed to accept 250 us long pulse trains of 1.6 MeV – 2.6 MeV (kinetic energy) electrons. This beamline is equipped with YAG profile monitor. Since we are interested in observing only the last macro bunch in the train, one of the possibilities is to install a fast kicker and a dedicated dump upstream of the YAG screen (and related diagnostics equipment). This approach is expensive and challenging from engineering point of view. Another possibility is to send the whole pulse train to the YAG screen and to use a fast gated camera (such as Imperex B0610 with trigger jitter under 60ns) to observe the image from the last pulse only. In this paper we study the feasibility of the last approach.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

Phasor Simulator for Operator Training Project

Synchrophasor systems are being deployed in power systems throughout the North American Power Grid and there are plans to integrate this technology and its associated tools into Independent System Operator (ISO)/utility control room operations. A pre-requisite to using synchrophasor technologies in control rooms is for operators to obtain training and understand how to use this technology in real-time situations. The Phasor Simulator for Operator Training (PSOT) project objective was to develop, deploy and demonstrate a pre-commercial training simulator for operators on the use of this technology and to promote acceptance of the technology in utility and ISO/Regional Transmission Owner (RTO) control centers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Biosafety Level 3 Recon Training

The Biosafety Level 3 Recon training is a 3D virtual tool developed for the Counter WMD Analysis Cell (CWAC) and the Asymmetric Warfare Group (AWG) by the Application Modeling and Development Team within the NEN-3 International Threat Reduction Group. The training simulates a situation where friendly forces have secured from hostile forces a suspected bioweapons development laboratory. The trainee is a squad member tasked to investigate the facility, locate laboratories within the facility, and identify hazards to entrants and the surrounding area. Before beginning the 3D simulation, the trainee must select the appropriate MOPP level for entering the facility. The items in the simulation, including inside and outside the bioweapon facility, are items that are commonly used by scientists in Biosafety Level (BSL) laboratories. Each item has clickable red tags that, when activated, give the trainee a brief description of the item and a controllable turn-around view. The descriptions also contain information about potential hazards the item can present. Trainees must find all tagged items in order to complete the simulation, but can also reference descriptions and turn-around view of the items in a glossary menu. Training is intended to familiarize individuals whom have little or no biology or chemistry background with technical equipment used in BSL laboratories. The revised edition of this simulation (Biosafety Level 3 Virtual Lab) changes the trainee into a investigator instead of a military combatant. Many doors now require a virtual badge swipe to open. Airlock doors may come in sets such that the open door must be closed before the next door in the set can be opened. A user interface was added so that the instructor can edit the information about the items (the brief descriptions mentioned above) using the simulation software instead of the previous method of manually entering the material in xml settings files. Facility labels, such as "No Parking" and "Men's room", were changed from Korean, into English. No other changes were made.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Improving Insider Threat Training Awareness and Mitigation Programs at Nuclear Facilities.

In recent years, insider threat programs have become an important aspect of nuclear security, and nuclear security training courses. However, many nuclear security insider threat programs fail to address the insider threat attack and monitoring potential that exists on information technology (IT) systems. This failure is critical because of the importance of information technology and networks in today’s world. IT systems offer an opportunity to perpetrate dangerous insider attacks, but they also present an opportunity to monitor for them and prevent them. This paper suggests a number of best practices for monitoring and preventing insider attacks on IT systems, and proposes the development of a new IT insider threat tabletop that can be used to help train nuclear security practitioners on how best to implement IT insider threat prevention best practices. The development of IT insider threat best practices and a practical tabletop exercise will allow nuclear security practitioners to improve nuclear security trainings as it integrates a critical part of insider threat prevention into the broader nuclear security system.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗