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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Status Update for the Canister Deposition Field Demonstration

This report updates the high-level test plan for evaluating surface deposition on three commercial 32PTH2 spent nuclear fuel (SNF) canisters inside NUTECH Horizontal Modular Storage (NUHOMS) Advanced Horizontal Storage Modules (AHSMs) from Orano (formerly Transnuclear Inc.) and provides a description of the surface characterization activities that have been conducted to date. The details contained in this report represent the best designs and approaches explored for testing as of this publication. Given the rapidly developing nature of this test program, some of these plans may change to accommodate new objectives or requirements. The goal of the testing is to collect highly defensible and detailed dust deposition measurements from the surface of dry storage canisters in a marine coastal environment to guide chloride-induced stress corrosion crack (CISCC) research. To facilitate surface sampling, the otherwise highly prototypic dry storage systems will not contain SNF but rather will be electrically heated to mimic the decay heat and thermal hydraulic environment. Test and heater design is supported by detailed computational fluid dynamics modeling. Instrumentation throughout the canister, storage module, and environment will provide extensive information about thermal-hydraulic behavior. Manual sampling over a comprehensive portion of the canister surface at regular time intervals will offer a high-fidelity quantification of the conditions experienced in a harsh yet realistic environment. Functional testing of the finalized heater assemblies and test apparatus is set to begin in December 2022. The proposed delivery of the canisters to the host test site is June/July 2023, which is well ahead of when the AHSM installations would be completed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Neural network methods for radiation detectors and imaging

Recent advances in image data proccesing through deep learning allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware. This enables radiation experiments, which includes photon sciences in synchrotron and X-ray free electron lasers as a subclass, through data-endowed artificial intelligence. We give an overview of data generation at photon sources, deep learning-based methods for image processing tasks, and hardware solutions for deep learning acceleration. Most existing deep learning approaches are trained offline, typically using large amounts of computational resources. However, once trained, DNNs can achieve fast inference speeds and can be deployed to edge devices. A new trend is edge computing with less energy consumption (hundreds of watts or less) and real-time analysis potential. While popularly used for edge computing, electronic-based hardware accelerators ranging from general purpose processors such as central processing units (CPUs) to application-specific integrated circuits (ASICs) are constantly reaching performance limits in latency, energy consumption, and other physical constraints. These limits give rise to next-generation analog neuromorhpic hardware platforms, such as optical neural networks (ONNs), for high parallel, low latency, and low energy computing to boost deep learning acceleration (LA-UR-23-32395).

edge computing↗
DOE OSTI2023

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗
DOE OSTI2011

A Hierarchical Security Architecture for Cyber-Physical Systems

Security of control systems is becoming a pivotal concern in critical national infrastructures such as the power grid and nuclear plants. In this paper, we adopt a hierarchical viewpoint to these security issues, addressing security concerns at each level and emphasizing a holistic cross-layer philosophy for developing security solutions. We propose a bottom-up framework that establishes a model from the physical and control levels to the supervisory level, incorporating concerns from network and communication levels. We show that the game-theoretical approach can yield cross-layer security strategy solutions to the cyber-physical systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Characterization and classification of vegetation canopy structure and distribution within the Great Smoky Mountains National Park using LiDAR

Vegetation canopy structure is a critically important habit characteristic for many threatened and endangered birds and other animal species, and it is key information needed by forest and wildlife managers for monitoring and managing forest resources, conservation planning and fostering biodiversity. Advances in Light Detection and Ranging (LiDAR) technologies have enabled remote sensing-based studies of vegetation canopies by capturing three-dimensional structures, yielding information not available in two-dimensional images of the landscape pro- vided by traditional multi-spectral remote sensing platforms. However, the large volume data sets produced by airborne LiDAR instruments pose a significant computational challenge, requiring algorithms to identify and analyze patterns of interest buried within LiDAR point clouds in a computationally efficient manner, utilizing state-of-art computing infrastructure. We developed and applied a computationally efficient approach to analyze a large volume of LiDAR data and to characterize and map the vegetation canopy structures for 139,859 hectares (540 sq. miles) in the Great Smoky Mountains National Park. This study helps improve our understanding of the distribution of vegetation and animal habitats in this extremely diverse ecosystem.

Clustering↗
DOE OSTI2018

Human performance benefits gained by dynamic instructions compared to smart PDFs

Human performance benefits gained by dynamic instructions compared to smart PDFs In recent years multiple nuclear utilities have deployed an electronic work management solution to replace the traditional paper-based processes. There are many benefits of electronic work management solutions compared to paper processes, such as a more streamlined review and approval process of work packages and a better way to track and resolve any potential bottlenecks causing delays in the review process. These benefits have been recognized where electronic work management solutions have been successfully deployed. However the utilities also hoped the new solution would help the field worker conduct his/her job more efficiently, correctly, and safely. Unfortunately, the industry is now realizing that the deployed solutions did not fully help them reach that goal. The electronic work management solutions present the work instructions as an electronic copy (i.e., a portable document format (PDF)) of the paper instruction. The PDF versions used in these electronic work management solutions have some ability for data input, notes, and mark-ups, but are unable to provide many additional benefits compared to using paper. In fact, conducting work using a PDF on a tablet can be more cumbersome than with a paper instruction. For example, flipping back and forth in a paper instruction can be time consuming, but it is not at all as frustrating as scrolling back and forth on a tablet or continuously having to navigate between tabs. The researchers at Idaho National Laboratory (INL) approached the next step beyond smart PDFs from a human factors perspective. In close collaboration with multiple utilities they investigated how to present instruction content and how to design the interaction with the instruction to improve the workers performance and efficiency. Their solution is a dynamic presentation of the instruction that guides the worker through the correct path as well as ensuring the worker is on the correct component. The solution also automatically validates input and provides guidance if unexpected conditions are identified. A well-designed solution has the opportunity to vastly reduce the risk of human errors in the field, such as preventing actions on incorrect component or unintentionally skipping steps. The human factors researchers at INL leveraged their expertise, existing human performance tools, and a great amount of involvement from field workers in multiple utilities to develop and evaluate design concepts that will provide the human performance and efficiency benefits that the electronic work management solutions fall short to deliver. This paper will describe in detail how these new design concepts improve human performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Fiber optic instrumentation for nuclear fuels and materials irradiations

The ability to predict the safety and reliability of new and advanced nuclear fuels and materials requires high-fidelity measurements during instrumented irradiation experiments. Optical fiber-based sensors are promising candidates for instrumentation in high-temperature (300-1200 C. degrees) irradiation environments, provided that signal attenuation due to radiation damage and/or high-temperature operation is not prohibitively large. To determine the feasibility of using silica optical fibers in instrumented irradiation capsules, a computational model has been developed to predict broadband optical attenuation in fused silica fiber materials as a function of time, temperature, and dose. Defect concentrations are calculated using a set of 11 coupled differential equations that include neutron-induced displacement, radiation-induced bond cleavage at precursor sites, and thermally-assisted molecular diffusion and defect recombination at elevated temperatures. Defect concentrations are coupled with the known optical properties of the various defects in fused silica to predict optical attenuation. The effects of radiation-induced compaction (i.e., formation of the 'metamict' phase) on the optical attenuation are also included. Results show that elevated temperatures significantly reduce attenuation resulting from defect centers (attenuation below ∼750 nm) as well as attenuation resulting from radiation-induced compaction (attenuation above ∼800 nm). The predicted attenuation is acceptable for most fiber optic-based sensor systems with a dynamic range on the order of 20-50 dB for a reasonable length (1-2 meters) of fiber in the high-flux region. Many optical components use a wavelength of 850 nm, which lies in the low-attenuation window. An irradiation campaign has been initiated to provide temperature-dependent optical attenuation data at high dose to validate the predictive model. (author)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Simulating Alpha Particles Incident on MKID Chips for Quantum Sensitivity Analysis

Superconducting quantum devices, such as microwave kinetic inductance detectors (MKIDs), are highly sensitive instruments used in quantum computing and advanced sensing technologies. However, their extreme sensitivity also makes them vulnerable to background noise from natural sources like radiation. One significant contributor to this noise is alpha particles emitted by 210Po, a radon decay daughter that accumulates on surfaces near the detector. This project investigates how alpha particles emitted from 210Po interact with MKID chips. These particles can deposit energy on the detector surface, disrupting its operation and generating false signals. Understanding the energy and behavior of these particles is crucial for improving the design and reliability of quantum devices. To explore this, we first modeled the decay chain starting from 210Pb to 210Po using differential equations. This allowed us to predict how the activity of alpha-emitting isotopes changes over time, reaching a steady state after about two years. Next, we simulated alpha particle interactions with the MKID chip using the Geant4 software toolkit. We built a detailed computer model of the detector housing, including the copper lid where alpha particles originate, the silicon chip, and a thin aluminum sensor layer. Alpha particles were emitted isotropically from just beneath the copper lid’s surface, mimicking natural decay conditions. The simulation tracked how these particles deposit energy on the chip, generating electron-hole pairs and phonons. The results provide insight into the behavior of the resultant electron-hole pairs and phonons, giving us a clear understanding of the energy deposition distribution on the chip. This work supports efforts to mitigate background noise in superconducting sensors, advancing their use in quantum computing and sensitive physics experiments.

Hall, Matthew [Fermilab; UCLA]↗
DOE OSTI2016

Direct comparison of elastic incoherent neutron scattering experiments with molecular dynamics simulations of DMPC phase transitions

Neutron scattering techniques have been employed to investigate 1,2-dimyristoyl-sn-glycero-3- phosphocholine (DMPC) membranes in the form of multilamellar vesicles (MLVs) and deposited, stacked multilamellar-bilayers (MLBs), covering transitions from the gel to the liquid phase. Neutron diffraction was used to characterise the samples in terms of transition temperatures, whereas elastic incoherent neutron scattering (EINS) demonstrates that the dynamics on the sub-macromolecular length-scale and pico- to nano-second time-scale are correlated with the structural transitions through a discontinuity in the observed elastic intensities and the derived mean square displacements. Molecular dynamics simulations have been performed in parallel focussing on the length-, time- and temperature-scales of the neutron experiments. They correctly reproduce the structural features of the main gel-liquid phase transition. Particular emphasis is placed on the dynamical amplitudes derived from experiment and simulations. Two methods are used to analyse the experimental data and mean square displacements. They agree within a factor of 2 irrespective of the probed time-scale, i.e. the instrument utilized. Mean square displacements computed from simulations show a comparable level of agreement with the experimental values, albeit, the best match with the two methods varies for the two instruments. Consequently, experiments and simulations together give a consistent picture of the structural and dynamical aspects of the main lipid transition and provide a basis for future, theoretical modelling of dynamics and phase behaviour in membranes. The need for more detailed analytical models is pointed out by the remaining variation of the dynamical amplitudes derived in two different ways from experiments on the one hand and simulations on the other.

Aoun, Bachir↗
DOE OSTI2023

Leveraging History to Predict Infrequent Abnormal Transfers in Distributed Workflows

Scientific computing heavily relies on data shared by the community, especially in distributed data-intensive applications. This research focuses on predicting slow connections that create bottlenecks in distributed workflows. In this study, we analyze network traffic logs collected between January 2021 and August 2022 at the National Energy Research Scientific Computing Center (NERSC). Based on the observed patterns, we define a set of features primarily based on history for identifying low-performing data transfers. Typically, there are far fewer slow connections on well-maintained networks, which creates difficulty in learning to identify these abnormally slow connections from the normal ones. We devise several stratified sampling techniques to address the class-imbalance challenge and study how they affect the machine learning approaches. Our tests show that a relatively simple technique that undersamples the normal cases to balance the number of samples in two classes (normal and slow) is very effective for model training. This model predicts slow connections with an F1 score of 0.926.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Crossing the Digital Divide

Nuclear utilities continue to upgrade aging instrumentation and control (I&C) systems with modern digital-based systems to address reliability and obsolescence concerns. In addition, the industry has demonstrated that these systems can be successfully developed and deployed even for highly safety-significant applications, such as reactor protection. However, three factors have retarded wide-scale implementation of digital technology for plant modernization and business innovation: *Large-scale digital upgrades entail considerable risk; *The upgrades are very costly and have not led to bottom-line business improvement, financial or otherwise; *Cyber Security concerns discourage wide-scale digital integration. A new national research program is under way that has been designed to address these industry concerns about expanding the use of digital technology. The Idaho National Laboratory (INL) has lead responsibility for the Advanced Instrumentation, Information, and Control Systems Technologies research pathway within the Light Water Reactor Sustainability (LWRS) Program. The LWRS Program is a research and development (R&D) program sponsored by the U.S. Department of Energy (DOE) and performed in close collaboration with industry R&D and improvement programs (as defined in INL’s FY2009–2013 Light Water Reactor Sustainability Research and Development Program Plan, INL/MIS08-14918). Its purpose is to provide the technical foundations for licensing and managing the long-term, safe, and economical operation of existing nuclear power plants. The DOE’s program focus is on longer-term and higher-risk/reward research that contributes to the national policy objectives of energy security and environmental security. The specific concern addressed by this program is that large-scale I&C obsolescence could become a life-limiting issue for the current nuclear operating fleet.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Developing the Proof of Concept for the SERI QC Flag Translation

SERI QC is a robust solar data quality assessment software tool that has been in continuous use for more than three decades. This report, the second of six in the Data Quality and Uncertainty Integration project, develops the proof of concept, including programming in C and a collection of spreadsheet tools, for estimating the operational uncertainty from three-component solar irradiance measurements using information available from SERI QC. The report establishes a field reference for determining the operational uncertainty for each irradiance component based on the SERI QC coupling. Results demonstrate that the operational uncertainty estimates based on global irradiance best represent the collective results for all sky conditions.

14 SOLAR ENERGY↗
DOE OSTI2024

Integrating ORNL’s HPC and Neutron Facilities with a Performance-Portable CPU/GPU Ecosystem

We explore the development of a performance-portable CPU/GPU ecosystem to integrate two of the US Department of Energy’s (DOE’s) largest scientific instruments, the Oak Ridge Leadership Computing facility and the Spallation Neutron Source (SNS), both of which are housed at Oak Ridge National Laboratory. We select a relevant data reduction workflow use-case to obtain the differential scattering cross-section from data collected by SNS’s CORELLI and TOPAZ instruments. We compare the current CPU-only production implementation using the Garnet Python multiprocess package based on the Mantid C++ framework against our proposed CPU/GPU implementation that uses the LLVM-based, just-in-time Julia scientific language and the JACC.jl performance-portable package. Two proxy apps were developed: (i) an app for extracting relevant Mantid kernels (MDNorm) in C++ and (ii) the Julia MiniVATES.jl miniapp. We present performance results for NVIDIA A100 and AMD MI100 GPUs and AMD EPYC 7513 and 7662 CPUs. The results provide insights for future generations of data reduction software that can embrace performance portability for an integrated research infrastructure across DOE’s experimental and computational facilities.

Hahn, Steven↗
DOE OSTI2021

Efficient loading of reduced data ensembles produced at ORNL SNS/HFIR neutron time-of-flight facilities

We present algorithmic improvements to the loading operations of certain reduced data ensembles produced from neutron scattering experiments at Oak Ridge National Laboratory (ORNL) facilities. Ensembles from multiple measurements are required to cover a wide range of the phase space of a sample material of interest. They are stored using the standard NeXus schema on individual HDF5 files. This makes it a scalability challenge, as the number of experiments stored increases in a single ensemble file. The present work follows up on our previous efforts on data management algorithms, to address identified input output (I/O) bottlenecks in Mantid, an open-source data analysis framework used across several neutron science facilities around the world. We reuse an in-memory binary-tree metadata index that resembles data access patterns, to provide a scalable search and extraction mechanism. In addition, several memory operations are refactored and optimized for the current common use cases, ranging most frequently from 10 to 180, and up to 360 separate measurement configurations. Results from this work show consistent speed ups in wall-clock time on the Mantid LoadMD routine, ranging from 19% to 23% on average, on ORNL production computing systems. The latter depends on the complexity of the targeted instrument-specific data and the system I/O and compute variability for the shared computational resources available to users of ORNL’s Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR) instruments. Nevertheless, we continue to highlight the need for more research to address reduction challenges as experimental data volumes, user time and processing costs increase.

Godoy, William↗
DOE OSTI2014

Compiler-Assisted Detection of Transient Memory Errors

The probability of bit flips in hardware memory systems is projected to increase significantly as memory systems continue to scale in size and complexity. Effective hardware-based error detection and correction requires that the complete data path, involving all parts of the memory system, be protected with sufficient redundancy. First, this may be costly to employ on commodity computing platforms and second, even on high-end systems, protection against multi-bit errors may be lacking. Therefore, augmenting hardware error detection schemes with software techniques is of consider- able interest. In this paper, we consider software-level mechanisms to comprehensively detect transient memory faults. We develop novel compile-time algorithms to instrument application programs with checksum computation codes so as to detect memory errors. Unlike prior approaches that employ checksums on computational and architectural state, our scheme verifies every data access and works by tracking variables as they are produced and consumed. Experimental evaluation demonstrates that the proposed comprehensive error detection solution is viable as a completely software-only scheme. We also demonstrate that with limited hardware support, overheads of error detection can be further reduced.

def-use analysis↗