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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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Data and Models: A Framework for Advancing AI in Science
On June 5, 2019, the Office of Science (SC) organized a one-day roundtable to focus on enhancing access to high-quality and fully traceable research data, models, and computing resources to increase the value of such resources for artificial intelligence (AI) research and development and the SC mission.1 In this report, we consider AI to be inclusive of, for example, machine learning (ML), deep learning (DL), neural networks (NN), computer vision, and natural language processing (NLP). We consider “data for AI” to mean the digital artifacts used to generate AI models and/or employed in combination with AI models during inference. In part, this roundtable was motivated by the recognition that a large portion of science data currently are not well suited for AI.
Atomic kinetics of laboratory photoionized plasmas relevant to astrophysics. Final report
The major goal of this project was to study the atomic and radiation physics of photoionized plasmas driven by a broadband x-ray flux and its impact on plasma x-ray heating and ionization, emissivity and opacity, and radiation-hydrodynamics. This problem is relevant to the physics of accretion disks surrounding black holes, x-ray binaries, and active galactic nuclei in astrophysics. In addition, it is also important for understanding non-equilibrium laboratory plasmas driven by a distribution of photons. To this end, and motivated by previous and ongoing gas-cell experiments performed by the PI at the Z facility of Sandia National Laboratories, we developed a new experimental platform at the 1MA Zebra pulsed power accelerator of the University of Nevada, Reno based on using a gas jet produced by a supersonic nozzle. The gas jet is turned into a photoionized plasma driven by the x- ray flux from a wire-array z-pinch. Establishing this new experimental platform at Zebra was a major goal of this project. In addition, we have also dedicated effort to continue complementary experiments at the Z facility using the gas-cell photoionized plasma set up with support from the Fundamental Science Program at Z as well as to perform data processing and analysis, and theory and modeling simulations, including electron kinetics and radiation-hydrodynamics. Since we seek to test and establish what physics models are needed to describe photoionized plasma guided by experimental observation, these complementary experimental efforts were central to achieving the goals of this project.
Asynchronous Ballistic Reversible Computing using Superconducting elements
Computing uses energy. At the bare minimum, erasing information in a computer increases the entropy. Landauer has calculated %7E k B T log(2) Joules is dissipated per bit of energy erased. While the success of Moores law has allowed increasing computing power and efficiency for many years, these improvements are coming to an end. This project asks if there is a way to continue those gains by circumventing Landauer through reversible computing. We explore a new reversible computing paradigm, asynchronous ballistic reversible computing or ABRC. The ballistic nature of data in ABRC matches well with superconductivity which provides a low-loss environment and a quantized bit encoding the fluxon. We discuss both these and our development of a superconducting fabrication process at Sandia. We describe a fully reversible 1-bit memory cell based on fluxon dynamics. Building on this model, we propose several other gates which may also offer reversible operation.
Classified library celebrates two-year anniversary print your own commemorative history posters
Today is the National Security Research Center’s second anniversary. These two years, though, have been eight decades in the making, said NSRC Director Riz Ali. “We opened our doors in 2019, but the Center’s beginnings date back to World War II when J. Robert Oppenheimer created the Lab in Los Alamos to develop the first-ever nuclear weapons,” Ali said. “An important part of opening the Lab included standing up a technical reports library. Since then, we have amassed millions of holdings and today we are one of the top research institutions in the country.” What had long-been a repository of classified archival materials is today a dynamic, vibrant library that outsizes the collections of other similar research libraries around the country. Today, the NSRC houses the Laboratory’s classified nuclear weapons research, data and reports, as well as unclassified historical artifacts – the stories, photos, films and more from the people who have been a part of the Lab from the start.
Quality Assurance Plan for Federal Guidance Report 16
Federal Guidance Report No. 16 will provide risk coefficients for use in projecting the probability of cancer morbidity or mortality from exposure to environmental radionuclides. The derivation of nuclide-specific risk coefficients involves multiple steps, with each step relying on the accuracy of the previous step as well as the numerical data utilized within each step. Thus, to ensure the quality of the derived risk coefficients, it is necessary to evaluate and confirm the quality and proper implementation of input data at each step of the derivation. The Oak Ridge National Laboratory (ORNL) dosimetry research team currently has two largely independent software codes for deriving radiation dose estimates, dose coefficients, and cancer risk coefficients for internally deposited radionuclides: DCAL and QCAL. The agreement of derived values based on alternate codes is an important step in quality assurance (QA) of the risk coefficients produced in this project. DCAL is an integration of the dosimetric software and numerical databases developed at ORNL over the past 25 years for the EPA’s Office of Radiation and Indoor Air (ORIA). QCAL was developed in 2015 to serve as a substitute for DCAL while DCAL was undergoing revision and to provide QA for the updated version of DCAL. The complex methodology employed in the derivation of cancer risk coefficients presents a challenge for QA, and as such, this report describes the QA steps used by the ORNL dosimetry team in this process as implemented in DCAL and QCAL for Federal Guidance Report No. 16.
A Comparative Study of Joint Modeling Methods and Analysis of Fasteners [Slides]
Motivation: Crucial aspect of mechanical design is joining methodology of parts. Ability to analyze joint and fasteners in system for structural integrity is fundamental. Different modeling representations of fasteners include spring, beam, and solid elements. Various methods compared for linear system to decide method appropriate for design study. New method for modeling fastener joint is explored from full system perspective. Analysis results match well with published experimental data for new method.
Report summarizing demonstration of thermal conductivity measurement methods for coated zirconium cladding
Thermal diffusivity measurements on Zirconium-based cladding materials have historically been a challenge due to the difficulty to measure on specimens with curved geometries, including nuclear grade Zircaloy cladding materials. In this work, we first used Laser Flash Analysis methods to measure the thermal diffusivity of Zircaloy tubes, which show good agreement with Zircaloy plates in this work, as well as previously published data. The consistent results proved the applicability of the Laser Flash Analysis setup for investigating thermal diffusivity of Zircaloy tubes. We further investigate the hydrogen effect on thermal diffusivity of Zircaloy tubes. Hydrogen plays significant roles in temperature dependent thermal diffusivity trends of Zircaloy tube. For low hydrogen concentration, where hydride dissolved into single phase α-Zr at higher temperature, thermal diffusivity is relatively unchanged at low temperature at two phase regions (α-Zr and δ-hydride), and it increases as a function of temperature at single phase α-Zr region. For high hydrogen concentration, where phase transformation (α-Zr + δ-hydride → α-Zr + β-Zr) occurs at 567 °C, thermal diffusivity decreases as a function of temperature below 567 °C before the transformation (α-Zr and δ-hydride), while increase at higher temperature after the transformation (α-Zr and β-Zr).
Computed Tomography Scanning and Geophysical Measurements of State 16-2 Well in the Paradox Basin
The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the National Energy Technology Laboratory (NETL) in Morgantown, West Virginia, were used to characterize Paradox Basin core from Grand County, Utah. The primary impetus of this work is a collaboration between the U.S. Department of Energy (DOE), the Utah Geological Survey, the University of Utah, and Zephyr Energy to characterize and make publicly available core information from the Cane Creek interval of the Paradox Basin. This stratigraphic well and the core data produced in this report will aid in understanding the structural complexities of the Cane Creek interval and its production potential.
Computed Tomography Scanning and Geophysical Measurements of the One Earth Energy Well #1 Core
The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the National Energy Technology Laboratory (NETL) in Morgantown, West Virginia were used to characterize the Lower Mt. Simon Sandstone and Eau Claire Formation core from the One Earth Energy Well #1 (OEE Well #1) in the Illinois Basin. The primary impetus of this work is a collaboration between the U.S. Department of Energy (DOE), the Illinois State Geological Survey (ISGS), the University of Illinois, and One Earth Energy to characterize and make publicly available core information of interest to carbon sequestration efforts in the Illinois Basin. This stratigraphic well and the core data produced in this report will aid in understanding the carbon sequestration potential of the Lower Mt. Simon Sandstone and the sealing capacity of the overlying units. The resultant datasets are presented in this report and can be accessed from NETL's Energy Data eXchange (EDX) online system using the following link: https://edx.netl.doe.gov/dataset/illinois-storage-corridor-one-earth-energy-1-core.
Computed Tomography Scanning and Geophysical Measurements of Appalachian Basin Core from the Jones and Laughlin #1 Well, Beaver County, PA
The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the National Energy Technology Laboratory (NETL) in Morgantown, West Virginia, were used to characterize Appalachian Basin core from Beaver County, Pennsylvania. The primary impetus of this work is a collaboration between the U.S. Department of Energy (DOE) and the Pennsylvania Geological Survey to characterize and make publicly available core information from the Onondaga-Huntersville formations of the Appalachian Basin. This stratigraphic well and the core data produced in this report will aid in understanding the structural complexities of the Onondaga-Huntersville formations. The resultant datasets are presented in this report and can be accessed from NETL's Energy Data eXchange (EDX) online system using the following link: https://edx.netl.doe.gov/dataset/jonesandlaughlin1well. All equipment and techniques used were non-destructive, enabling future examinations and analyses to be performed on these cores. Fractures, discontinuities, and millimeter-scale features were readily detectable with imaging performed with the NETL medical CT scanner over the entire core. Qualitative analysis of the medical CT images, coupled with X-ray fluorescence (XRF), and magnetic susceptibility measurements from the MSCL were useful in identifying zones of interest for further study. Targeted higher resolution CT scanning of select sections was performed with NETL’s micro-CT scanner. The combination of methods used provides a multiscale analysis of the core; the resulting macro and micro descriptions are relevant to many subsurface energy related examinations traditionally performed at NETL.
NRC Reactor Operating Experience Analysis and Trend Summary: 2022 Update
This report presents a summary of the Nuclear Regulatory Commission (NRC) reactor operating experience analyses with data through 2022 as well as the reliability and frequency trends identified in the 2022 update reports for component performance study, loss of offsite power analysis, initiating events analysis, and system study provided on the NRC Reactor Operating Experience Results and Databases website (https://nrcoe.inl.gov/).
Validating TSLs for ENDF/B-VIII.1 [Slides]
The purpose of this talk is to determine whether there is well-defined, publicly available data to validate TSL files.
Low-Flow Marine Hydrokinetic Turbine for Small Autonomous Unmanned Mobile Recharge Stations
A prototype low-flow marine current turbine for deployment from a small unmanned mobile floating platform has been developed for autonomously seeking and harnessing tidal/coastal currents. The support platform is an unmanned surface vehicle (USV), in the form of a catamaran with two electric outboard motors and with capabilities for autonomous navigation. The USV utilized is a WAM-V 16 vehicle that has been developed separately with support from the Office of Naval Research (ONR) [1]. The marine current turbine is based on a freestream waterwheel (FSWW), also known as an undershot waterwheel (FSWW), mounted on the stern of the USV. The concept of operation involves the USV autonomously navigating to a designated marine current resource. Upon arrival, the USV anchors itself, aligns with the current, and deploys the FSWW turbine using a custom cable-lift mechanism. The turbine harnesses the local current, and an onboard power-take-off (PTO) device converts the mechanical energy into electricity, which is stored in an onboard battery bank. When energy harvesting is completed, the turbine and the anchor are retrieved and the USV navigates to a selected location. These unmanned at-sea platforms can provide power to other unmanned maritime systems. Specifically, in this project, the power generated onboard can be used to charge aerial drones via a custom flight deck that has been developed for the USV. The recharging capabilities offered by a fleet of such strategically placed recharging stations can significantly benefit aerial drones operating in the maritime domain by eliminating the need to travel back and forth to land or ship based charging stations. The project has resulted in the development of subcomponents, including the FSWW turbine, a novel PTO, an automated anchoring system for the USV, an automated turbine deployment system, and a flight deck with capabilities onboard the USV for landing, direct-contact charging and takeoff of aerial drones. The design and development of these subsystems have culminated in the overall prototype marine hydrokinetic platform (MHK Platform, Fig. 1). Comprehensive lab and field testing have been conducted to validate the functionality and performance of the platform and its components. The project demonstrates the potential for autonomous, unmanned systems to harness renewable energy from marine currents, and provide sustainable power solutions for maritime applications such as coastal surveillance and environmental monitoring; shoreline mapping; search and rescue; oceanographic research; inspection and maintenance of offshore energy installations like wind turbines and oil rigs; oil spill response; maritime disaster response; and aerial surveys, as well as facilitation of data transfer drones and shore stations.
Addressing Issues with Working Memory in Video Object Segmentation
Contemporary state-of-the-art video object segmentation (VOS) models compare incoming unannotated images to a history of image-mask relations via affinity or cross-attention to predict object masks. We refer to the internal memory state of the initial image-mask pair and past image-masks as a working memory buffer. While the current state of the art models perform very well on clean video data, their reliance on a working memory of previous frames leaves room for error. Affinity-based algorithms include the inductive bias that there is temporal continuity between consecutive frames. To account for inconsistent camera views of the desired object, working memory models need an algorithmic modification that regulates the memory updates and avoid writing irrelevant frames into working memory. A simple algorithmic change is proposed that can be applied to any existing working memory-based VOS model to improve performance on inconsistent views, such as sudden camera cuts, frame interjections, and extreme context changes. The resulting model performances show significant improvement on video data with these frame interjections over the same model without the algorithmic addition. Our contribution is a simple decision function that determines whether working memory should be updated based on the detection of sudden, extreme changes and the assumption that the object is no longer in frame. By implementing algorithmic changes, such as this, we can increase the real-world applicability of current VOS models.
Modeling Partial Reflection Paths for Infrasound Analysis
Numerical methods enabling simulation of scattered and partially reflected infrasonic propagation paths produced by interaction with fine-scale structure in the middle atmosphere have been implemented in the infraGA ray tracing software. This capability enables simulation of ensonification in the classical stratospheric “shadow zone” that has been observed during the Humming Roadrunner and LSECE surface explosion campaigns as well as in other data sets. In the case of LSECE, a pair of stations roughly 140 kilometers east of the source location observed arrivals with celerities (horizontal group velocities) slightly slower than observed stratospheric paths at similar azimuths. The arrivals exhibited increasing trace velocity later in the wavetrain indicating a steepening of the arrival path for longer or slower propagation paths. Simulation of partially reflected paths using the updated infraGA software methods finds good agreement between observed and predicted infrasonic ensonification at these locations within the stratospheric shadow zone. Further development of the partial reflection physics and comparison with other data sets is needed to more robustly understand how such anomalous infrasonic signals can be predicted; however, the demonstration of this capability is a promising first step in such analyses.
Schema Elements for Granta Annual Report: FY2024
Granta: Materials Intelligence (Granta: MI) is a commercial database software distributed by Ansys, Inc. that is utilized by the Nuclear Security Enterprise (NSE) to organize and store relevant materials data. Lack of standard and well-documented database schema is the primary obstacle to an NSE materials data management solution, so the objective of this project is to create and document such a schema. In FY21, an approach for designing, documenting, and managing a standard database schema was described based on the creation of schema elements (collections of attributes used to describe particular aspects of the data) to be used as building blocks for creating various database tables without duplication. In FY22, these methods were applied through a multi-site collaboration to create and document the schema elements necessary to build a thermogravimetric analysis (TGA) testing table. In FY23 the schema was expanded to include elements for a differential scanning calorimetry (DSC) table, along with schema for supporting metadata tables including Instruments, Projects, Documents, and Testing Series. In FY24 the following progress was made, again through multi-site collaboration: • The existing schema elements were modified to accommodate thermomechanical analysis (TMA) data, and a table, Test Data: TMA, was created for managing TMA data. • The elements necessary for the following additive manufacturing (AM) data tables (directed at data specific to selective laser sintering AM technology) were created: • AM Builds • AM Processes • AM Part Designs • Built AM Parts • AM Feedstock Materials • AM Feedstock Material Batches • The elements necessary for creating a Calibrated Material Models table were created, and the Calibrated Material Models table was created. In FY25 the existing schema will be deployed on the production enterprise Granta instance on the enterprise secure network. Schema elements will be appended, and new elements created as necessary, to allow the creation of tables specifically to support materials testing, AM process development, and design and analysis for modernization programs.
High-Temperature Gas Sensor Materials with Properties Predicted via First-Principles Calculations with Machine Learning Modeling and Experimental Corroboration
Understanding the temperature dependence of functional properties of sensing materials is vital for their applications in combustion environments. The electron-phonon coupling that derives the electronic structure change with temperatures is a key property of interest as it affects other sensing responses. Herein, we first assess the temperature dependence of band gap renormalization in sensing materials by employing Allen-Heine-Cardona (AHC) theory with density functional theory (DFT) simulations corroborated with experimental observation. As the AHC calculations are impractical for high-throughput screening of materials, we employ data-driven Gaussian process regression to predict the parameters employed in the O’Donnell empirical model from a set of physical features. To mitigate the reliability issues arising from the small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as to quantify the uncertainty associated with theoretical predictions. These models capture well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions and, therefore, the variation of bandgap as a function of temperature for other novel materials. The predicted candidates from machine learning models are further validated by experiments and DFT calculations.