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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Improving metrology with quantum scrambling

Quantum scrambling describes the spreading of information into many degrees of freedom in quantum systems, such that the information is no longer accessible locally but becomes distributed throughout the system. This idea can explain how quantum systems become classical and acquire a finite temperature, or how in black holes the information about the matter falling in is seemingly erased. We probe the exponential scrambling of a multiparticle system near a bistable point in phase space and utilize it for entanglement-enhanced metrology. A time-reversal protocol is used to observe a simultaneous exponential growth of both the metrological gain and the out-of-time-order correlator, thereby experimentally verifying the relation between quantum metrology and quantum information scrambling. Our results show that rapid scrambling dynamics capable of exponentially fast entanglement generation are useful for practical metrology, resulting in a 6.8(4)-decibel gain beyond the standard quantum limit.

Science & Technology - Other Topics↗
DOE OSTI2023

Characterizing the Impacts of Multi-Scale Heterogeneity on Solute Transport in Fracture Networks

We model flow and transport in fracture networks with varying degrees of fracture-to-fracture aperture heterogeneity and network intensity to show how changes in these properties can cause the emergence of anomalous flow and transport behavior. If fracture-to-fracture aperture heterogeneity is increased in sparse networks, velocity fluctuations inhibit high flow rates and solute transport can be delayed. Surprisingly, transport can be slowed even in cases where hydraulic aperture is monotonically increased. As the intensity of the networks is increased, more connected pathways allow for particles to bypass these effects. There exists transition behavior where with relatively few connected pathways in a network, first arrival times of particles are not heavily affected by fracture-to-fracture aperture heterogeneity, but the scaling behavior of the tails is strongly influenced. These results reinforce the importance of considering multi-scale effects in fractured systems and can inform flow and transport processes in both natural and engineered fractured systems.

58 GEOSCIENCES↗
DOE OSTI2024

Identifiability and characterization of transmon qutrits through Bayesian experimental design

Robust control of a quantum system is essential to utilize the current noisy quantum hardware to its full potential, such as quantum algorithms. To achieve such a goal, a systematic search for an optimal control for any given experiment is essential. The design of optimal control pulses requires accurate numerical models and, therefore, accurate characterization of the system parameters. We present an online Bayesian approach for quantum characterization of qutrit systems, which automatically and systematically identifies optimal experiments that provide maximum information on the system parameters, thereby greatly reducing the number of experiments that need to be performed on the quantum testbed. Unlike most characterization protocols that provide point-estimates of the parameters, the proposed approach is able to estimate their probability distribution. The applicability of the Bayesian experimental design technique was demonstrated on test problems, where each experiment was defined by a parameterized control pulse. In addition to this, we also present an approach for iterative pulse extension, which is robust under uncertainties in transition frequencies and coherence times, and shot noise, despite being initialized with wide uninformative priors. Furthermore, we provide a mathematical proof of the theoretical identifiability of the model parameters and present conditions on the quantum state under which the parameters are identifiable. The proof and conditions for identifiability are presented for both closed and open quantum systems using the Schrödinger equation and the Lindblad master equation, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Sparse-Data Deep Learning Strategies for Radiographic Non-Destructive Testing

Radiography is an imaging technique used in a variety of applications, such as medical diagnosis, airport security, and nondestructive testing. We present a deep learning system for extracting information from radiographic images. We perform various prediction tasks using our system, including material classification and regression on the dimensions of a given object that is being radiographed. Our system is designed to address the sparse-data issue for radiographic nondestructive testing applications. It uses a radiographic simulation tool for synthetic data augmentation, and it uses transfer learning with a pre-trained convolutional neural network model. Using this system, our preliminary results indicate that the object geometry regression task saw an improvement of 70% in the R-squared value when using a multi-regime model. In addition, we increase the performance of the object material classification tasks by utilizing data from different imaging systems. In particular, using neutron imaging improved the material classification accuracy by 20% when compared to x-ray imaging.

convolutional neural networks↗
DOE OSTI2026

Delivery Ring Spill Characterization and Impulse Study

High-intensity particle physics experiments require uniform beam extraction to prevent instantaneous rate spikes from overwhelming detector systems. By analyzing accelerator parameters and extracted beam dynamics, we directly inform spill regulation systems that make real-time adjustments to minimize non-uniformity. This Department of Energy Visiting Faculty Program project transitioned from characterizing Main Injector half-integer slow extraction for SpinQuest to Delivery Ring third-integer slow extraction for Mu2e. Working alongside the Fast Adaptive Neural Control (FANC) group, we developed an automated pipeline that aligns asynchronous instrument channels, embeds quality metrics, and isolates clean spill populations. Analyzing baseline spills alongside a dedicated quadrupole impulse study allowed us to quantify noise structures while mapping time-varying beam response and transit-delay dynamics. These empirical measurements directly ground digital twin models, supporting FANC’s deployment of real-time, FPGA-based neural network controllers in the Mu2e Spill Regulation System.

Dolen, James William [Purdue U., West Lafayette] (↗
DOE OSTI2023

Autonomous sense and guide machine learning system

A system for generating a machine learning system to generate guidance information based on locations of objects is provided. The system accesses training data that includes training time-of-arrival (“TOA”) information of looks and guidance information for each look. The guidance information is based on a training collection of object locations. The TOA of a look represents, for each object location of a training collection of object locations, times between signals transmitted by transmitters and return signals received by receivers. The return signals represent signals reflected from an object at the object location. The system trains a machine learning system using the training data wherein the machine learning system inputs TOA information and outputs guidance information.

Beer, N. Reginald↗
DOE OSTI2023

Cyber-Informed Engineering Guidance—Implementing CIE in Early Systems Engineering Lifecycle Stages

Traditionally, cybersecurity is not considered in the design process. Design engineers typically focus on building safety and reliability into their products and applications. Security against malicious cyber incidents is often an afterthought, resulting in deployment of security solutions during installation or operation. Unfortunately, waiting to consider cybersecurity until later in the systems engineering lifecycle often results in less effective and more expense security. Idaho National Laboratory (INL) developed the concept of Cyber-Informed Engineering (CIE) in 2015 to provide a framework that enables cybersecurity to be built into systems beginning at the conceptual design stage. In addition to ongoing research by INL, the U.S. Department of Energy (DOE) Office of Cybersecurity, Energy Security, and Emergency Response has recently developed a National CIE Strategy document for incorporating CIE into the design and operation of infrastructure systems reliant on digital monitoring or controls. This paper provides a brief review of this National CIE Strategy as well as a roadmap to historical, current, and future CIE research by INL through the U.S. DOE Office of Nuclear Energy (NE) Cybersecurity Crosscutting Technology Development Program. A near-term focus of the DOE-NE’s research and development is to extend the foundational CIE work into detailed guidance for implementation during initial systems engineering stages in nuclear digital instrumentation and control projects and to demonstrate use of the guidance in an integrated energy systems project.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Integration of Data Analytics with Plant System Health Program

This report summarizes the R&D activities of the Plant Health Management (PHM) project during fiscal year 2020 (FY20). This project focuses on the development of methods that integrate component health data and propagate such information at the system level to evaluate system sources of risk. This project development lives in cooperation with the Risk Informed Asset Management (RIAM) project. The RIAM project, in fact, uses the reliability models and data generated by the PHM project to optimize plant operations (e.g., maintenance/replacement schedule, optimal maintenance posture). This year’s activities for the PHM project focused mainly on the development of two classes of models. The first one includes a series of component reliability models which include aging, testing and maintenance. The second class includes a series of system reliability models which focus on the secondary side of exiting U.S. reactors. These models are based on fault tree logic structures such that they can be used by existing plant PRA software. We also started to focus on the management of health data and we tackled this issue in two directions. The first one focuses on the integration of monitor data with simulation models to assess component health. This approach is moving from a classical data based to a model+data based approach with the goal of improving higher component health information. The second direction focuses on linking equipment reliability data (e.g., maintenance/failure reports, component monitoring data) directly to system reliability models using a safety margin based language rather than a probability based language. The main advantage of a safety margin based language is that it can provide to system engineers more tangible information on system/component health and how it propagates to the system level.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Editorial: Quantum Information and Quantum Computing for Chemical Systems

Quantum computing has emerged as an exciting inter-disciplinary research topic that cuts across the traditional fields of physics, computer science, and engineering. It is a revolutionary model of computation that has offered new insights into methods for modeling and simulation of chemical systems. Applications of quantum computing to chemistry have demonstrated rapid progress on both theoretical and experimental fronts. Past theoretical efforts have shown how to adapt quantum computation to a variety of problems including electronic structure and molecular dynamics. In addition, the development of quantum algorithms for quantum chemistry has been stimulated by the greater availability of more capable quantum computing devices. Advances in the number and quality of qubits continues to enable remarkable proof-of-concept demonstrations working towards a milestone of quantum computational advantage.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Integration of proteomics and metabolomics into the Design, Build, Test, Learn cycle to improve 3-hydroxypropionic acid production in Aspergillus pseudoterreus

Biological engineering of microorganisms to produce value-added chemicals is a promising route to sustainable manufacturing. However, overproduction of metabolic intermediates at high titer, rate, and yield from inexpensive substrates is challenging in non-model systems where limited information is available regarding metabolic flux and its control in production conditions. Integrated multi-omic analyses of engineered strains offers an in-depth look at metabolites and proteins directly involved in growth and production of target and non-target bioproducts. Here we applied multi-omic analyses to overproduction of the polymer precursor 3-hydroxypropionic acid (3HP) in the filamentous fungus Aspergillus pseudoterreus. A synthetic pathway consisting of aspartate decarboxylase, beta-alanine pyruvate transaminase, and 3HP dehydrogenase was designed and built for A. pseudoterreus. Strains with single- and multi-copy integration events were isolated and multi-omics analysis consisting of intracellular and extracellular metabolomics and targeted and global proteomics was used to interrogate the strains in shake-flask and bioreactor conditions. Production of a variety of co-products (organic acids and glycerol) and oxidative degradation of 3HP were identified as metabolic pathways competing with 3HP production. Intracellular accumulation of nitrogen as 2,4-diaminobutanoate was identified as an off-target nitrogen sink that may also limit flux through the engineered 3HP pathway. Elimination of the high-expression oxidative 3HP degradation pathway by deletion of a putative malonate semialdehyde dehydrogenase improved the yield of 3HP by 3.4× after 10 days in shake-flask culture. This is the first report of 3HP production in a filamentous fungus amenable to industrial scale biomanufacturing of organic acids at high titer and low pH.

Pomraning, Kyle R.↗
DOE OSTI2026

Observational Data for Next-Generation Climate Model Evaluation: Requirements, Considerations, and Best Practices

Climate model simulations are an important source of information about our planet’s climate system and also enable informed decision-making under different future scenarios. As a new archive of results from the next generation of climate models is anticipated to become available with the Coupled Model Intercomparison Project phase 7 (CMIP7), the need to develop efficient and robust methods to evaluate models is paramount. Observations are an integral part of model evaluation, providing a means to quantify and understand the degree to which climate models can faithfully reproduce Earth system processes. Such analysis is critical for constraining climate projections, identifying areas of focus for model development, and assisting analysts in deciphering the utility of models for specific applications. Observations of Earth system come from a diversity of sources, span different space–time domains, and are produced by different communities, and each dataset features different data structures and formats, metadata standards, and its own unique uncertainties. Uncertainties in an observational dataset may stem from gaps in temporal and spatial coverage, instrumentation errors, or assumptions in retrieval and processing methods. How then does one ensure that observational data are ready for use and utilized in the most appropriate way for robust, rapid, and routine climate model evaluation? The CMIP7 Model Benchmarking Task Team with input from the broader climate modeling, model evaluation, and observational data communities present a vision and considerations for best practices toward the optimal and appropriate use of observational data to support next-generation climate model evaluation.

Climate models↗
DOE OSTI2020

Load altering attack-tolerant defense strategy for load frequency control system

Cyber attacks are emerging threats to every information-oriented energy management system. By violating the cyber systems, the hacker can disrupt the security and stability due to the strong coupling between the cyber and physical facilities. In this paper, one type of cyber attacks designated as the load altering attack is studied for the power system frequency control, and corresponding defense strategies are proposed to improve the frequency control performance. Considering the difficulty of the application of model-based controller into large-scale power systems, a novel model-free defense framework is for the first time presented. Under this framework, both active defense and passive defense strategies are designed. The former assumes that the defender has the initiative to learn different attack scenarios. Adaptive defense strategies are implemented using the online attack identification information and off-line trained strategy pool. The latter assumes that the defender passively tolerates various attack scenarios via the pre-trained off-line strategy. Both approaches prove to be effective through validation based on the IEEE benchmark systems. The proposed defense framework and defense strategies can be extended to other energy control systems to enhance their attack tolerance capability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Graphite Oxidation Rate Study on ET-10 and ETU-10 Grades - Task 4: QA Support and Testing for Structural Graphite Oxidation

INL performed targeted oxidation tests to measure oxidation rates for samples of ET-10 and ETU-10 graphite under CRADA No. 21CRA22 Mod. 3, Annex A, “Tritium Testing to Support Kairos Power Advanced Reactor Demonstration” (04/02/2024). All testing was conducted within INL’s Carbon Characterization Laboratory (CCL) using test standard ASTM D7542-21 "Standard Test Method for Air Oxidation of Carbon and Graphite in the Kinetic Regime" [ASTM International, 2021]. Kairos Power provided all test specimens through its graphite vendor Ibiden, Inc. to INL and ASTM specimen specified dimensions. Information within this report only provides the Arrhenius oxidation rate plots as a function of temperature for each graphite grade tested. The raw mass loss per time data will be provided on the Nuclear Data Management and Analysis System (NDMAS) portal located on the INL information system.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Degradation and Failure Phenomena of Advanced Reactor Fuel Concepts: Sodium-Cooled Fast Reactor Metallic Fuel

The U.S. Nuclear Regulatory Commission (NRC) is anticipating licensing applications and commercial use of new fuel types in advanced nuclear power reactors that would be designed and built in the United States. Pacific Northwest National Laboratory (PNNL) is providing technical assistance to the NRC related to the newly proposed nuclear fuel and cladding designs that would be deployed in these reactors. This report focuses specifically on the metallic fuel that is being considered for sodium-cooled fast reactors and specifically on mechanisms that would cause damage or failure to the fuel under reactor operating conditions and design basis accident conditions. There is historic experience with both metallic and oxide fuels in experimental sodium-cooled fast reactors, but this report will focus solely on metallic fuel. Currently two U.S.-based reactor designers are engaged with the NRC in the application or pre-application review and considering a sodium-cooled fast reactor. TerraPower is engaged with NRC in pre-application review of its Natrium reactor. The current design for this fuel uses a sodium-bonded uranium-10wt% zirconium (U10Zr) fuel clad in HT9 stainless steel. ARC Clean Technology is engaged with NRC in pre-application review of its ARC-100 reactor. The current design for this fuel uses a sodium-bonded uranium-10wt% zirconium (U10Zr) fuel with steel cladding. This report will focus on this fuel system specifically, with broader information given regarding other metallic fuel systems with other stainless steel alloy claddings. To support the NRC’s readiness efforts, this report will identify and discuss degradation and failure modes of metallic fuel concepts for sodium-cooled fast reactors, including fuel performance characteristics that may not be addressed within existing regulatory documents.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Data-Informed Synthetic Networks of Water Distribution Systems for Resilience Analysis in Puerto Rico

The increasing potential of infrastructure disruptions calls for high-quality infrastructure models to be used in resilience analysis and decision making. Unfortunately, many utilities and communities do not have access to accurate and detailed models due to a lack of data and resources. Furthermore, security restrictions on sharing infrastructure models present roadblocks to research, analysis, and decision making. Recent advances in the development of synthetic water distribution models provide a potential solution to this problem. There is an opportunity to improve these methods by leveraging incomplete pipe datasets to aid synthetic network generation. To address this gap, we developed a methodology for synthetic network generation that incorporates partial pipe data using a modification of the minimum cost flow algorithm for network generation and pipe sizing. This methodology demonstrates how partial pipe data can be leveraged to improve site-specific synthetic network generation. For the study area of Mayagüez, Puerto Rico, a synthetic model generated using 50% of real pipe data matches the pressure of the validation system with an average error of 23.5 m of head, which improves upon the average error of 31.6 m of head produced by a synthetic model generated using no data of the real pipes. Additionally, synthetic networks are shown to replicate the pressure response under a disruption scenario of the validation network, suggesting potential use in resilience analysis.

resilience analysis↗
DOE OSTI2020

HPC at NREL: FY 2019

Brochure featuring Fiscal Year 2019 accomplishments at the NREL High-Performance Computing User Facility and Data Center at the Energy Systems Integration Facility, including information on the facility's HPC systems as well as highlights from several modeling and simulation projects across the spectrum of energy efficiency and renewable energy technologies.

97 MATHEMATICS AND COMPUTING↗