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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge

CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning

Abstract The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated β processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation could induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at a high accuracy over ML models for stress-driven activation events. Interestingly, a quantitative “between-task” transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantitative imaging and automated fuel pin identification for passive gamma emission tomography

Compliance of member States to the Treaty on the Non-Proliferation of Nuclear Weapons is monitored through nuclear safeguards. The Passive Gamma Emission Tomography (PGET) system is a novel instrument developed within the framework of the International Atomic Energy Agency (IAEA) project JNT 1510, which included the European Commission, Finland, Hungary and Sweden. The PGET is used for the verification of spent nuclear fuel stored in water pools. Advanced image reconstruction techniques are crucial for obtaining high-quality cross-sectional images of the spent-fuel bundle to allow inspectors of the IAEA to monitor nuclear material and promptly identify its diversion. In this work, we have developed a software suite to accurately reconstruct the spent-fuel cross sectional image, automatically identify present fuel rods, and estimate their activity. Unique image reconstruction challenges are posed by the measurement of spent fuel, due to its high activity and the self-attenuation. While the former is mitigated by detector physical collimation, we implemented a linear forward model to model the detector responses to the fuel rods inside the PGET, to account for the latter. The image reconstruction is performed by solving a regularized linear inverse problem using the fast-iterative shrinkage-thresholding algorithm. We have also implemented the traditional filtered back projection (FBP) method based on the inverse Radon transform for comparison and applied both methods to reconstruct images of simulated mockup fuel assemblies. Higher image resolution and fewer reconstruction artifacts were obtained with the inverse-problem approach, with the mean-square-error reduced by 50%, and the structural-similarity improved by 200%. We then used a convolutional neural network (CNN) to automatically identify the bundle type and extract the pin locations from the images; the estimated activity levels finally being compared with the ground truth. The proposed computational methods accurately estimated the activity levels of the present pins, with an associated uncertainty of approximately 5%.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Fabrication of THz corrugated wakefield structure and its high power test

We present overall process for developing terahertz (THz) corrugated structure and its beam-based measurement results. 0.2-THz corrugated structures were fabricated by die stamping method as the first step demonstration towards GW THz radiation source and GV/m THz wakefield accelerator. 150-μm thick disks were produced from an OFHC (C10100) foil by stamping. Two types of disks were stacked alternately to form 46 mm structure with ~ 170 corrugations. Custom assembly was designed to provide diffusion bonding with a high precision alignment of disks. The compliance of the fabricated structure have been verified through beam-based wakefield measurement at Argonne Wakefield Accelerator Facility. Both measured longitudinal and transverse wakefield showed good agreement with simulated wakefields. Measured peak gradients, 9.4 MV/m/nC for a long single bunch and 35.4 MV/m/nC for a four bunch trains, showed good agreement with the simulation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Estimates of lithium mass yields from produced water sourced from the Devonian-aged Marcellus Shale

Abstract Decarbonatization initiatives have rapidly increased the demand for lithium. This study uses public waste compliance reports and Monte Carlo approaches to estimate total lithium mass yields from produced water (PW) sourced from the Marcellus Shale in Pennsylvania (PA). Statewide, Marcellus Shale PW has substantial extractable lithium, however, concentrations, production volumes and extraction efficiencies vary between the northeast and southwest operating zones. Annual estimates suggest statewide lithium mass yields of approximately 1160 (95% CI 1140–1180) metric tons (mt) per year. Production decline curve analysis on PW volumes reveal cumulative volumetric disparities between the northeast (median = 2.89 X 10 7 L/10-year) and southwest (median = 5.56 × 10 7 L/10-year) regions of the state, influencing lithium yield estimates of individual wells in southwest [2.90 (95% CI 2.80–2.99) mt/10-year] and northeast [1.96 (CI 1.86–2.07) mt/10-year] PA. Moreover, Mg/Li mass ratios vary regionally, where NE PA are low Mg/Li fluids, having a median Mg/Li mass ratio of 5.39 (IQR, 2.66–7.26) and SW PA PW is higher with a median Mg/Li mass ratio of 17.8 (IQR, 14.3–20.7). These estimates indicate substantial lithium yields from Marcellus PW, though regional variability in chemistry and production may impact recovery efficiencies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantifying local and global mass balance errors in physics-informed neural networks

Physics-informed neural networks (PINN) have recently become attractive for solving partial differential equations (PDEs) that describe physics laws. By including PDE-based loss functions, physics laws such as mass balance are enforced softly in PINN. This paper investigates how mass balance constraints are satisfied when PINN is used to solve the resulting PDEs. We investigate PINN’s ability to solve the 1D saturated groundwater flow equations (diffusion equations) for homogeneous and heterogeneous media and evaluate the local and global mass balance errors. We compare the obtained PINN’s solution and associated mass balance errors against a two-point finite volume numerical method and the corresponding analytical solution. We also evaluate the accuracy of PINN in solving the 1D saturated groundwater flow equation with and without incorporating hydraulic heads as training data. We demonstrate that PINN’s local and global mass balance errors are significant compared to the finite volume approach. Tuning the PINN’s hyperparameters, such as the number of collocation points, training data, hidden layers, nodes, epochs, and learning rate, did not improve the solution accuracy or the mass balance errors compared to the finite volume solution. Mass balance errors could considerably challenge the utility of PINN in applications where ensuring compliance with physical and mathematical properties is crucial.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Enhancing Drinking Water Quality Modeling: Leveraging Physics Informed Neural Networks for Learning with Imperfect Reaction Models and Partial Data

Chemical kinetics models, typically formulated as systems of ordinary or partial differential equations, are valuable tools for simulating drinking water quality. However, these models often face inaccuracies due to discrepancies between the laboratory and the real-world conditions, as well as limitations in experimental analytical methods, hindering the accurate representation of the true underlying chemical mechanisms. In this study, we propose a Physics Informed Neural Network (PINN), using the eXtreme Theory of Functional Connections, to improve the prediction of chemical concentrations over time. The PINN method accounts for imperfect chemical models and incorporates partial data to improve predictions. Focusing on reactions describing water disinfection residual and disinfectant byproduct formation, which are crucial for public health and regulatory compliance, we demonstrate that the PINN model is able to accurately predict the concentrations of chemical species across various pH values. Notably, the model extends its accuracy to predict concentrations of chemical species not originally included in its training data. The developed method can be extended to a variety of chemical systems, offering a wide array of potential applications.

13 HYDRO ENERGY↗
DOE OSTI2025

Reassessment of caustic scrubbing for radioiodine capture during UNF processing

The effective removal of iodine-129 from gaseous emissions during used nuclear fuel processing is critical for minimizing environmental contamination and ensuring environmental regulatory compliance. Recent research has focused on optimizing process air, scrubber conditions, and integrating complementary techniques, such as solid sorbents as a polishing step, to improve iodine capture efficiency. The efficiency of a caustic scrubber is influenced by several factors, such as pH, temperature, gas–liquid contact time, and the presence of oxidants, yet the existing literature tends not to consider how these factors might interact or change in importance with process scaling. This perspective advocates for reconsidering how to mitigate many of these factors, especially in view of the transition from laboratory bench to pilot scale and beyond. This paper reviews the principles, operational parameters, and advancements in caustic aqueous scrubbing for radioiodine mitigation, aims to direct the next scientific pursuit of this technology, and inform environmental decision-making.

Ngelale, Randy [Oak Ridge National Laboratory (ORN↗
DOE OSTI2022

Forward-Looking State-of-the-Art Review on Earned Value Management Systems: The Disconnect between Academia and Industry

Earned value management (EVM) is a project management approach that can enhance the probability of project success. It is applied widely across different industry sectors (e.g., energy, aerospace, construction, defense, and manufacturing), generally through the use of an earned value management system (EVMS). A holistic and up-to-date literature review on EVM and EVMS does not exist. A literature review can provide a comprehensive perspective on the topic, identifying and summarizing the existing body of knowledge, as a foundation to advance the state of practice of EVM/EVMS. Therefore, the objective of this paper is to investigate the EVM/EVMS state of the art by critically reviewing academic and industry publications, with a specific focus on the maturity of EVMS and the environment surrounding its implementation. By performing a systematic literature review, the authors identified 600 publications since the inception of the EVM concept in 1962, and then narrowed down this list to 160 relevant publications from the last decade for closer review. The findings include the discovery of eight emergent themes. Of these themes, “forecasting/prediction” constitutes the largest portion of the recent literature, followed by “application of EVMS.” One interesting finding is that EVMS maturity, although being a critical topic, is only discussed in one publication. Publications focused on EVM/EVMS have increased in the last decade and significant differences were found between academia and industry literature in terms of the limitations and extensions of EVM/EVMS, EVMS environment, and compliance. A key finding is that designing a reliable EVMS should combine both technical and social aspects of implementation. This forward-looking paper provides a state-of-the-art review while highlighting gaps in the existing EVM/EVMS body of knowledge and introducing new perspectives to support EVMS research and application.

42 ENGINEERING↗
DOE OSTI2022

Passenger Boarding Station and Curbfront Configuration Concepts for On-Demand Services with Small Automated Vehicles

This paper explores the various configurations of off-line stations and vehicle berthing that have been found to be fundamentally important parameters in the analysis of system performance, operations, and capacity. The proposed automated transit network system concept, which provides a direct ride between a passenger's origin and destination station, is applicable to both fixed guideway automated transit and to systems with self-driving vehicles. Addressed at a conceptual level is the vehicle/station interface in terms of Americans with Disability Act compliance and station capacity concerns. The concept of open-edge platforms monitored by advanced sensing technology combined with artificial intelligence-powered perception for the purpose of protecting passengers from entering the active vehicle lanes is also discussed. Finally, the paper draws initial conclusions on how these station configurations, operational complexities, and associated costs can be better understood through appropriate research and development.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Novel Earned Value Management System Maturity Framework and Its Relation to Project Performance

Earned value management systems (EVMS) are used by industry practitioners to successfully manage projects and programs; however, there has been no past study to characterize and evaluate the impact of EVMS maturity on project performance. This paper introduces a novel framework, developed to measure the EVMS maturity, composed of 56 attributes arrayed across ten subprocesses, which together make up an integrated project/program management system. This paper describes how the framework was conceived and developed, based on literature review and data collected from industry workshops. Then, it examines the relationship between EVMS maturity and ten project performance metrics through statistical analyses. The study analyzed project data from 35 projects and programs worth over $21.8 billion in total cost. For this sample, results showed statistically significant differences between projects with high and low EVMS maturity; on average, projects exhibiting high EVMS maturity have 54% less cost overruns and 19% less change orders versus their performance measurement baseline. Higher maturity projects and programs also achieve better customer satisfaction, meet business objectives more often, and are more often in compliance with EVMS standards and guidelines. This paper contributes to the body of knowledge by providing a practical and novel framework for assessing the maturity of earned value management systems. Practitioners can use the framework to improve their integrated project and program management.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Resistive switching properties of monolayer h-BN atomristors with different electrodes

Resistive switching properties based on molecular beam epitaxy-grown monolayer hexagonal boron nitride (h-BN) atomristors are studied by using metal insulator metal configurations with different electrode materials. Au/monolayer h-BN/Ni devices demonstrate a forming-free bipolar resistive switching (BRS) behavior, a good endurance with up to 97 cycles at a high compliance current of 100 mA, an average on/off ratio of 10 3 , and a low set/reset voltage variability. Metal/monolayer h-BN/graphite/Co devices exhibit self-compliant current BRS characteristics. Both metal/h-BN/Ni and metal/h-BN/graphite/Co devices show the coexistence of BRS, unipolar resistive switching (URS), and nonvolatile threshold switching (TH) modes. The formation of conductive filaments is attributed to the diffusion and trapping of metal ions on the defect sites driven by the electric field, while the rupture is driven by the electric field in BRS and by Joule heating in URS and TH modes.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Direct measurement of storage and loss behavior in AFM force–distance experiments using the modified Fourier transformation

Force–distance curve experiments are commonly performed in atomic force microscopy (AFM) to obtain the viscoelastic characteristics of materials, such as the storage and loss moduli or compliances. The classic methods used to obtain these characteristics consist of fitting a viscoelastic material model to the experimentally obtained AFM data. Here, we demonstrate a new method that utilizes the modified discrete Fourier transform to approximate the storage and loss behavior of a material directly from the data, without the need for a fit. Additionally, one may still fit a model to the resulting storage and loss behavior if a parameterized description of the material is desired. In contrast to fitting the data to a model chosen a priori, departing from a model-free description of the material's frequency behavior guides the selection of the model, such that the user may choose the one that is most appropriate for the particular material under study. To this end, we also include modified Fourier domain descriptions of commonly used viscoelastic models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Improving equations of state calibrations in the toroidal DAC—The case study of molybdenum

We report an updated isothermal equation of state (EoS) of molybdenum (Mo) obtained by compression in beveled and toroidal diamond-anvil cells (DACs). For an improved compression environment, we developed a copper (Cu) pressure-transmitting medium (PTM) for the toroidal diamond-anvil cell samples, as it is a soft metal compared to Mo with a well calibrated EoS. A Ne PTM was used for the conventional beveled DAC samples. The unit-cell volumes of Mo were measured to 336(1) GPa in the Cu PTM and 231.2(6) GPa in the Ne PTM at room temperature. We additionally calculated elastic stiffness and compliance constants and evaluated the uniaxial stress of Mo and Cu with pressure. A new EoS for Mo is presented from data collected in all sample environments and compared to our theoretical predictions as well as previous compression studies of Mo. The (200) lattice plane of Mo produced the lowest volumes across the pressure range of this study for all compression environments, suggesting that it is less affected by nonhydrostatic stresses in the DAC compared to the other observed diffraction planes. The presented Mo EoS is compatible with extrapolations of EoS fits of Mo in helium (He) within ~1% at 330 GPa. Results from this work demonstrate that compressing a sample in a softer metal in the toroidal DAC can improve the compression environment and result in measured sample volumes comparable to those collected in noble-gas media at multi-megabar conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Multiscale porous elastomer substrates for multifunctional on-skin electronics with passive-cooling capabilities

In addition to mechanical compliance, achieving the full potential of on-skin electronics needs the introduction of other features. For example, substantial progress has been achieved in creating biodegradable, self-healing, or breathable, on-skin electronics. However, the research of making on-skin electronics with passive-cooling capabilities, which can reduce energy consumption and improve user comfort, is still rare. Herein, we report the development of multifunctional on-skin electronics, which can passively cool human bodies without needing any energy consumption. This property is inherited from multiscale porous polystyrene-block-poly(ethylene-ran-butylene)-block-polystyrene (SEBS) supporting substrates. The multiscale pores of SEBS substrates, with characteristic sizes ranging from around 0.2 to 7 µm, can effectively backscatter sunlight to minimize heat absorption but are too small to reflect human-body midinfrared radiation to retain heat dissipation, thereby delivering around 6 °C cooling effects under a solar intensity of 840 W∙m -2 . Other desired properties, rooted in multiscale porous SEBS substrates, include high breathability and outstanding waterproofing. The proof-of-concept bioelectronic devices include electrophysiological sensors, temperature sensors, hydration sensors, pressure sensors, and electrical stimulators, which are made via spray printing of silver nanowires on multiscale porous SEBS substrates. The devices show comparable electrical performances with conventional, rigid, nonporous ones. Also, their applications in cuffless blood pressure measurement, interactive virtual reality, and human–machine interface are demonstrated. Notably, the enabled on-skin devices are dissolvable in several organic solvents and can be recycled to reduce electronic waste and manufacturing cost. Such on-skin electronics can serve as the basis for future multifunctional smart textiles with passive-cooling functionalities.

42 ENGINEERING↗
DOE OSTI2024

Utility and Industry Perceptions of Control Room Modernization Over the Last 10 Years

Within the current United States (U.S.) nuclear power fleet, main control room modernization (CRM) is an important step towards cost savings. In recent decades, plants have been engaged in upgrades to varying degrees. This process requires a nuanced, balanced, and timely approach that ensures continued safety and long-term sustainability. In 2012 a survey was issued to individuals from the nuclear industry to learn their perspectives on a range of CRM issues. The survey targeted the benefits and challenges for utilities undertaking this process, including the main drivers and barriers to technology upgrades, regulatory compliance, and the effects these factors have on concepts of operations, strategic approaches, and staffing. In 2022, the survey was issued again to understand whether CRM perceptions had changed in the last 10 years. Our findings identify changes in industry thinking from a decade ago. Here we reveal perspective shifts that represent increased optimism and, in some instances, increased doubt regarding the opportunities and challenges inherent in CRM and implementation. We also report nuanced differences in CRM perspectives between utility and surrounding nuclear industry respondents.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Computational Thermal Hydraulics of a High-Performance Low-Enriched-Uranium Annular Target for HFIR Irradiation

Molybdenum-99 has historically been generated via isolation from fissioned highly enriched uranium (HEU) targets. Here, this isotope is in high demand due to its daily use across the world in radiopharmaceutical medical procedures. The primary objective of this work was to design and analyze an experimental target assembly containing one low-enriched-uranium (LEU) annular target for irradiation at the High Flux Isotope Reactor (HFIR). Efforts included incorporating spatially dependent energy sources from neutron and gamma interactions, quantifying thermal contact conductance at material interfaces, performing grid-independent studies, comparing turbulence models, and simulating various steady-state and transient scenarios relevant for irradiation qualification and eventual insertion. These models provide velocity, pressure, and temperature distributions in both space and time. Such results enable the selection of an appropriate irradiation location, fission rate density, and flow-limiting orifice size and demonstrate compliance with HFIR safety requirements such that insertion into the reactor can be approved. This analysis shows that across all scenarios, wetted surface temperatures remain below the coolant saturation temperature with no net vapor formation in the coolant. In every scenario, all components stay below 30% of the aluminum 6061 melting temperature. Computational fluid dynamics and system-level models predict peak target temperatures that agree within 4%, though the predicted axial location of the peak differs by about 10% of the heated length due to differences in flow development length. These results de-risk the irradiation of LEU (annular targets) and strengthen a domestic, HEU-independent 99 Mo supply by providing important fuel performance data to form the foundation for a robust licensing basis.

Molybdenum-99↗
DOE OSTI2026

United States Nuclear Power Reactor Used Nuclear Fuel Database and Applications

The Unified Database (UDB) within STANDARDS serves as the foundational data infrastructure for managing the United States' spent nuclear fuel inventory of 315,111 discharged assemblies totaling 91,036 metric tons of heavy metal. The database organizes this complex inventory through over 200 interconnected tables structured into eight primary attribute categories, supporting integrated analyses across storage, transportation, and disposal domains. Data enters the UDB through the GC-859 Nuclear Fuel Data Survey, which transitioned to web-based collection in 2023, improving data quality through real-time validation. The UDB enables automated generation of input files for nuclear safety analyses, reducing preparation time from weeks to hours while maintaining traceability. Applications include national inventory reporting, Certificate of Compliance assessments, and facility optimization. The three-tier distribution model balances accessibility with security requirements for federal agencies, national laboratories, and research organizations. The UDB provides essential data infrastructure as spent fuel management transitions from site-specific to integrated national campaigns.

Stefanovic, Peter↗