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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

OWL Change Control Process

The Online Waste Library (OWL) provides a consolidated source of information on Department of Energy-managed radioactive waste likely to require deep geologic disposal. With the release of OWL Version 1.0 in fiscal year 2019 (FY2019), much of the FY2020 work involved developing the OWL change control process and the OWL release process. These two processes (in draft form) were put into use for OWL Version 2.0, which was released in early FY2021. With the knowledge gained, the OWL team refined and documented the two processes in two separate reports. This report focuses on the change control process and discusses the following: (1) definitions and system components; (2) roles and responsibilities; (3) origin of changes; (4) the change control process including the Change List, Task List, activity categories, implementation examples, and checking and review; and (5) the role of the re lease process in ensuring changes in the Change List are incorporated into a public release.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Process damping identification using Bayesian learning and time domain simulation

Process damping can provide improved machining productivity by increasing the stability limit at low spindle speeds. However, existing methods for identifying process damping models experimentally require specialized setups and/or multiple cutting tests. While the phenomenon is well known, the modeling challenges limit pre-process parameter selection that leverages the potential increases in material removal rates. This paper proposes a physics-informed Bayesian method that can identify the cutting force and process damping models from a limited set of test cuts without requiring direct measurements of cutting force or vibration. The method uses time domain simulation to incorporate process damping and provide a basis for test selection. Here, new strategies for efficient sampling and dimensionality reduction are applied to lower computation time and minimize the effect of model error. The proposed method is demonstrated and the identified cutting and damping force coefficients are compared to values obtained using machining tests and least-squares fitting.

42 ENGINEERING↗
DOE OSTI2022

Characterization of the Fusion Zone in Laser Beam Welds on SS304L

Designing a welded joint requires consideration of the welding process, component/assembly structure and weld geometry, material composition, properties, and desired joint performance. The designer must also specify a set of acceptance criteria used to evaluate the welds. This study was conducted to support the design team responsible for specifying the spot weld profile by investigating the relationship between weld process parameters and the weld geometry and properties to further inform design optimization. The subjects of this study are two thin stainless steel 304L (SS304L) components joined by Laser Beam Welding (LBW). The components are the Housing, which contains flexible circuitry, and the Cover that protects the circuitry during handling, installation, and under service loads. Figure 1 illustrates coupon versions of the Cover and Housing used to simulate the interface between these components for weld testing. These coupon representations of assembly components were designed to match the material and relevant geometry related to the welded joint while other features not relevant to the welded joint are excluded.The objective of this study was to characterize the sensitivity of the weld geometry and mechanical properties to the input weld parameters to inform further Finite Element Analysis (FEA) efforts in balancing joint strength against assembly deformation caused by the welding process. Characterization was accomplished by varying laser welding parameters around the current design values and measuring weld geometry on the cross-sections. Nanoindentation with a spherical and flat-ended tip was used on polished section cuts of the weld to obtain direct estimates of the stress-strain response of the Fusion Zone (FZ) for comparison to the Base Metal (BM).

42 ENGINEERING↗
DOE OSTI2024

Invertible Temper Modeling using Normalizing Flows and the Effects of Structure Preserving Loss

Advanced manufacturing research and development is typically small-scale, owing to costly experiments associated with these novel processes. Deep learning techniques could help accelerate this development cycle but frequently struggle in small-data regimes like the advanced manufacturing space. While prior work has applied deep learning to modeling visually plausible advanced manufacturing microstructures, little work has been done on data-driven modeling of how microstructures are affected by heat treatment, or assessing the degree to which synthetic microstructures are able to support existing workflows. We propose to address this gap by using invertible neural networks (normalizing flows) to model the effects of heat treatment, e.g., tempering. The model is developed using scanning electron microscope imagery from samples produced using shear-assisted processing and extrusion (ShAPE) manufacturing. This approach not only produces visually and topologically plausible samples, but also captures information related to a sample’s material properties or experimental process parameters. We also demonstrate that topological data analysis, used in prior work to characterize microstructures, can also be used to stabilize model training, preserve structure, and improve downstream results. We assess directions for future work and identify our approach as an important step towards end-to-end deep learning system for accelerating advanced manufacturing research and development.

Howland, Sylvia↗
DOE OSTI2020

Where Is the Provenance? Ethical Replicability and Reproducibility in GIScience and Its Critical Applications

As replicability and reproducibility (R&R) crises develop within emerging convergent inquiry, ethical use of provenance information is central to the establishment and preservation of trust in critical applications of GIScience and geospatial technologies. Today large volumes of geospatial data are generated at high velocity from satellite sensors and unmanned aircraft systems, citizen sensors, geolocation-based data services, global navigation satellite systems, and so on. The extensive use of these data for applications such as disaster and humanitarian response raises the issue of R&R from competing perspectives of location privacy and geospatial data quality. Although geospatial data can be integrated and linked with contextual information to identify individuals’ movements, steps taken to ensure privacy can complicate the multiuser development of high-quality geospatial workflows. Provenance information as digital records of historical (retrospective) and potential future (prospective) geospatial processes is often overlooked, misunderstood, or inadequately addressed. We explore the relationship between provenance information, location privacy, and geospatial data quality in the context of R&R with a focus on disaster analytics. Here, we argue that in the era of big data and deep learning, GIScientists and associated institutions bear greater responsibility both for geospatial workflow quality and for location privacy. Given vastly heterogenous computational landscapes, we provide practical recommendations for ethically driven provenance and R&R research and development within the GIScience community and beyond.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

On learning what to learn: Heterogeneous observations of dynamics and establishing possibly causal relations among them

Abstract Before we attempt to (approximately) learn a function between two sets of observables of a physical process, we must first decide what the inputs and outputs of the desired function are going to be. Here we demonstrate two distinct, data-driven ways of first deciding “the right quantities” to relate through such a function, and then proceeding to learn it. This is accomplished by first processing simultaneous heterogeneous data streams (ensembles of time series) from observations of a physical system: records of multiple observation processes of the system. We determine (i) what subsets of observables are common between the observation processes (and therefore observable from each other, relatable through a function); and (ii) what information is unrelated to these common observables, therefore particular to each observation process, and not contributing to the desired function. Any data-driven technique can subsequently be used to learn the input–output relation—from k-nearest neighbors and Geometric Harmonics to Gaussian Processes and Neural Networks. Two particular “twists” of the approach are discussed. The first has to do with the identifiability of particular quantities of interest from the measurements. We now construct mappings from a single set of observations from one process to entire level sets of measurements of the second process, consistent with this single set. The second attempts to relate our framework to a form of causality: if one of the observation processes measures “now,” while the second observation process measures “in the future,” the function to be learned among what is common across observation processes constitutes a dynamical model for the system evolution.

Sroczynski, David W.↗
DOE OSTI2023

Real-time quantum error correction beyond break-even

The ambition of harnessing the quantum for computation is at odds with the fundamental phenomenon of decoherence. The purpose of quantum error correction (QEC) is to counteract the natural tendency of a complex system to decohere. This cooperative process, which requires participation of multiple quantum and classical components, creates a special type of dissipation that removes the entropy caused by the errors faster than the rate at which these errors corrupt the stored quantum information. Previous experimental attempts to engineer such a process faced an excessive generation of errors that overwhelmed the error-correcting capability of the process itself. Whether it is practically possible to utilize QEC for extending quantum coherence thus remains an open question. We answer it by demonstrating a fully stabilized and error-corrected logical qubit whose quantum coherence is significantly longer than that of all the imperfect quantum components involved in the QEC process, beating the best of them with a coherence gain of G=2.27±0.07. Here we achieve this performance by combining innovations in several domains including the fabrication of superconducting quantum circuits and model-free reinforcement learning.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

A Review of Online Monitoring within Used Nuclear Fuel Recycling Processes

The processing of used nuclear fuels and related materials is often complex and variable. The ability to quickly optimize conditions to the material being processed can aid in increasing efficiency and safety, but requires very quick determination of the conditions present in the feedstock, the process, and the product. Furthermore, accurate quantification of materials such as enriched uranium and plutonium aids in maintaining material accountancy and avoiding nuclear proliferation risks. Traditional analytical methods require process samples to be collected and analyzed in a laboratory, which often takes days to weeks. Online monitoring is suitable for collecting this information nearly instantaneously, enabling much faster optimization of the process or detection of material diversion. Online monitoring is also beneficial as it is typically based on robust and nondestructive analytical methods, so no material is removed as samples. This review examines online monitoring relevant to used nuclear fuel processing for the determination of both chemical and physical parameters. The chemical parameters include quantities such as concentration, isotopic composition, and speciation. These values are often well suited to spectroscopic or spectrometric measurements as they are fast, nondestructive, and easily implemented in an online manner. Physical quantities are often more varied and include temperature, pressure, tank fill levels, and others. Due to the specificity of these quantities, specialized instrumentation is often used. However, this instrumentation is often amendable to online monitoring.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Real-time GIS Programming and Geocomputation

Streaming data generated continuously from sensor networks, mobile devices, social media platforms and other edge devices have posed significant challenges to existing computing platforms for achieving both high throughput and low latency data processing in addition to scalable computing. This entry introduces a real-time computing and programming platform for time-critical GIS (Geographic Information System) applications. In this platform, advanced streaming data processing software, such as Apache Kafka and Spark Streaming, are integrated to enable data analytics in real-time. This computing platform can also be extended to integrate GeoAI (Geospatial Artificial Intelligence) based machine learning models to leverage both historical and streaming data to achieve real-time prediction and intelligent geospatial analytics. Two real-time geospatial applications in terms of flood simulation and climate data visualization are introduced to demonstrate how real-time programming and computing can help tackle real-world problems with important societal impacts.

Liu, Yan↗
DOE OSTI2021

Characterizing major and trace element compositions in fallout melt glass from a near-surface nuclear test

We report the chemical and isotopic compositions of fallout melt glasses from nuclear tests contain a range of information constraining the physical conditions within the fireball and the mechanisms of fallout formation but historic studies tended to exclude the behavior of stable major and trace elements. Here, we present a large study specifically focused on major and trace element relationships within a population of macroscale fallout samples from a single event. We interpret these data to better constrain how fallout melt glass formation in near surface environments is influenced by that environment and demonstrate how major and trace element abundances can provide useful insights into chemical processes within the fireball. Data confirm that the uranium in the fallout glass population derives from two isotopically distinct endmembers: isotopically enriched uranium (presumably from the weapon), and natural composition uranium that may be a combination of anthropogenic and environmental materials from within the blast zone. The similarity between major and trace element concentrations in fallout and corresponding local soils from the event site confirm the local soils as the most probable source of entrained material into the fireball and the source of carrier material into which the bomb vapor was incorporated. The lack of correlation between major and trace element abundances with size indicates that volatility driven processes, such as condensation from the fireball, do not control the composition of macroscale fallout melt glass. Although the fallout has major and trace element chemical characteristics broadly similar to those of the local, associated soils, some systematic differences are observed between the two populations. Fallout melt glass is depleted in volatile elements such as K, Na, Tl and Pb, consistent with heating to temperatures above ~1000 °C for 3-10 s. This is supported by the results of laser heating experiments performed on rhyolitic soil at temperatures (1600-2200 °C) and timescales (1-120 s) that are broadly relevant to fallout formation conditions. Relative enrichments of metals such as Cu and Co do not correlate with the abundance of uranium, suggesting that fallout also records input of near field anthropogenic materials. Our observations suggest that major chemical features can be related to processing in the fireball and used to inform the thermal-chemical evolution of the system. Ultimately, these data are consistent with a fallout formation mechanism that involves rapid melting of surface materials to form carrier material melts with minor incorporation of bomb vapor and a degree of volumetric volatile loss due to heating.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

HBMax: Optimizing Memory Efficiency for Parallel Influence Maximization on Multicore Architectures

The goal of influence maximization is to select k most-influential vertices or seeds in a network, where influence is defined by a given diffusion process. The problem has a number of important applications such as viral marketing, information spread, and epidemic control. Although computing optimal seed set is NP-Hard, due to the submodular nature of the problem efficient approximation algorithms exist. However, even state-of-the-art parallel implementations are limited by a sampling step that incurs large memory footprints. This in turn limits the problem size reach and approximation quality. In this work, we study the memory footprint of the sampling process collecting reverse reachability information in the IMM algorithm over large real-world social networks. We present an adaptive and memory-efficient optimization approach for a state-of-the-art multi-threaded parallel influence maximization algorithm. Our approach,HuffMax, uses a portion of the reverse reachable (RR) sets collected by the algorithm to learn the characteristics of the graph. Then, it compresses the intermediate reverse reachability information with Huffman coding, and queries directly on the compressed data to preserve the memory savings obtained through compression. We also propose an efficient sampling strategy based on the distribution of RR sets, which can further reduce the computation time for typical social networks with long-tail distributions. Considering a NUMA architecture, we scale up our solution on 128-core CPUs and reduce the memory footprint by up to 45.7% with negligible time overhead (or even faster) and without perceivable loss of accuracy.

Chen, Xinyu↗
DOE OSTI2022

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Abstract In a manufacturing system, production control‐related decision‐making activities occur at different levels. At the process level, one of the main control activities is to tune the parameters of individual manufacturing equipment. At the system level, the main activity is to coordinate production resources and to route parts to appropriate workstations based on their processing requirement, priority indices, and control policy. At the factory level, the goal is to plan and schedule the processing of parts at different operations for the entire system in order to optimize certain objectives. Note that the results of such activities at different levels are closely coupled and affect the overall performance of the manufacturing system as a whole. Therefore, it is important to systematically integrate these control and optimization activities into one unified platform to ensure the goal of each individual activity is aligned with the overall performance of the system. In this paper, we develop a simulation‐based virtual testbed that implements dynamic optimization, automatic information exchange, and decision‐making from the process‐level, system‐level, and factory‐level of a manufacturing system into an integrated computation environment. This is demonstrated by connecting a Python‐based numerical computation program, discrete‐event simulation software (Simul8), and an optimization solver (CPLEX) via a third‐party master program. The application of this simulation‐based virtual testbed is illustrated by a case study in a machining shop.

Sun, Yuting↗
DOE OSTI2025

Integrated Hydro-terrestrial Modeling 2.0: Progress and Path Forward on Building a National Capability

It is the role of the U.S. federal government and its supporting agencies, including academia and future scientists, to ensure that its people have sustained and equitable freshwater services, as well as the critical knowledge necessary to make decisions about the future as it relates to freshwater services. Clear and consistent information and guidance from federal agencies is critical. Integrated Hydro-Terrestrial Modeling (IHTM), as a United States (U.S.) national capability, focuses on understanding, quantifying, and managing the replenishment of water supply through hydrologic cycle processes and their governing forces. To provide that information, we need enhanced IHTM capabilities that capitalize on the strengths of each U.S. governmental agency and its core mission. The first IHTM workshop was held in 2019, and its subsequent report was published in 2020. The U.S. Global Change Research Program (USGCRP) and member agencies held a second IHTM workshop (IHTM 2.0) from October 31 to November 2, 2023 in Reston, Virginia. The IHTM 2.0 workshop focused on the need to support a multiscale framework to accelerate research insights, better integrate operational and planning perspectives, and bridge national-to-regional capabilities to address major interdependent societal water challenges. The workshop was organized according to a “WHAT” and “HOW” framework, with the common underlying “WHY” being the integrated water resource challenges and the “WHO” defined through interagency and cooperating academic partners. The report provides a summary of plenary presentations and breakout discussions, and a road map that focuses on near-term activities.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Evaluation of Nuclear Spent Fuel Disposal in Clay-Bearing Rock - Process Model Development and Experimental Studies (M2SF-21SN010301072)

The DOE R&D program under the Spent Fuel Waste Science Technology (SFWST) campaign has made key progress in modeling and experimental approaches towards the characterization of chemical and physical phenomena that could impact the long-term safety assessment of heatgenerating nuclear waste disposition in deep-seated clay/shale/argillaceous rock. International collaboration activities such as heater tests, continuous field data monitoring, and postmortem analysis of samples recovered from these have elucidated key information regarding changes in the engineered barrier system (EBS) material exposed to years of thermal loads. Chemical and structural analyses of sampled bentonite material from such tests as well as experiments conducted on these are key to the characterization of thermal effects affecting bentonite clay barrier performance and the extent of sacrificial zones in the EBS during the thermal period. Thermal, hydrologic, and chemical data collected from heater tests and laboratory experiments has been used in the development, validation, and calibration of THMC simulators to model near-field coupled processes. This information leads to the development of simulation approaches (e.g., continuum and discrete) to tackle issues related to flow and transport at various scales of the host-rock, its interactions with barrier materials, and EBS design concept.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Review of Onsite Temperature and Solar Forecasting Models to Enable Better Building Design and Operations

Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data. Traditionally, most studies utilize airport weather information as the decision inputs. However, most buildings are in environments that are quite different than those at the airport miles away. Tree cover, adjacent buildings, and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature, humidity, solar irradiance, and wind that are large enough to influence design and operation decisions. In order to overcome this challenge, there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales. Additionally, this paper reviews and complies knowledge on common weather data resources, data processing methodologies and forecasting techniques of weather information. Commonly used statistical, machine learning and physical-based models are discussed and presented as two major categories: deterministic forecasting and probabilistic forecasting. Finally, evaluation metrics for forecasting errors are listed and discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Integrating Energy Efficiency Strategies with Industrialized Construction for Our Clean Energy Future: Preprint

NREL’s Industrialized Construction Innovation Team has developed an ambitious plan to accelerate the integration of energy efficiency (EE) strategies with Industrialized Construction (IC). The United States (U.S.) construction industry is beginning to use IC methods to build multifamily apartment buildings to address affordability and labor shortages. Apart from reducing cost of construction and delivery times, the IC method of permanent modular construction has the potential to facilitate the integration of a wide range of EE strategies and advanced controls into such buildings. While there may be unintended EE benefits to IC such as a tighter envelope due to higher construction quality, the process has not been leveraged specifically to enhance EE. NREL aims to claim this missed opportunity and integrate IC benefits with EE as well as advanced controls, distributed energy resources, and grid-friendly design strategies. The paper proposes an ‘IC Assessment Framework’ to achieve affordable zero-energy modular multifamily buildings. Through the selection criteria of Design for Manufacturing and Assembly, the framework aims to distill a broad range of proven EE strategies for site-built into a set of strategies that qualify as easy to integrate for off-site. The output is a Factory Information Model (FIM) that represents a process-based digital twin to enable advanced time-and-motion study, plugs into open source building energy modeling platform (EnergyPlus), and serves as a vital tool facilitating wider adoption of EE integration. Conclusively, the paper delineates next steps for upcoming pilots with NREL’s IC partners towards developing a transformational pathway for our Clean Energy Future.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Direct observation of strong momentum-dependent electron-phonon coupling in a metal

Phonon scattering in metals is one of the most fundamental processes in materials science. However, understanding such processes has remained challenging and requires detailed information on interactions between phonons and electrons. We use an ultrafast electron diffuse scattering technique to resolve the nonequilibrium phonon dynamics in femtosecond–laser-excited tungsten in both time and momentum. We determine transient populations of phonon modes which show strong momentum dependence initiated by electron-phonon coupling. For phonons near Brillouin zone border, we observe a transient rise in their population on a timescale of approximately 1 picosecond driven by the strong electron-phonon coupling, followed by a slow decay on a timescale of approximately 8 picosecond governed by the weaker phonon-phonon relaxation process. We find that the exceptional harmonicity of tungsten is needed for isolating the two processes, resulting in long-lived nonequilibrium phonons in a pure metal. Our finding highlights that electron-phonon scattering can be the determinant factor in the phonon thermal transport of metals.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Surface Functional Groups Affect Iron (Hydr)oxide Heterogeneous Nucleation: Implications for Membrane Scaling

Because of its favorable thermodynamics and fast kinetics, heterogeneous solid nucleation on membranes triggers early-stage mineral scaling. Iron (hydr)oxide, a typical membrane scale, initially forms as nanoparticles that interact with surface functional groups on membranes, but these nanoscale phenomena are difficult to observe in real time. In this study, we utilized in situ grazing incidence small angle X-ray scattering and ex situ atomic force microscopy to examine the heterogeneous nucleation of iron (hydr)oxide on surface functional groups commonly used in membranes, including hydroxyl (OH), carboxyl (COOH), and fluoro (F) groups. We found that, compared to nucleation on hydrophilic OH- and COOH-surfaces, the high hydrophobicity of an F-modified surface significantly reduced the extents of both heterogeneously and homogeneously formed iron (hydr)oxide nucleation. Moreover, on the OH-surface, the high functional group density of 0.76 nmol/cm 2 caused faster heterogeneous nucleation than that on a COOH-surface, with a density of 0.28 ± 0.04 nmol/cm 2 . The F-surface also had the highest heterogeneous nucleation energy barrier (26 ± 0.6 kJ/mol), followed by COOH- (23 ±0.8 kJ/mol) and OH- (20 ± 0.9 kJ/mol) surfaces. The kinetic and thermodynamic information provided here will help us better predict the rates and extents of early-stage scaling of iron (hydr)oxide nanoparticles in membrane processes. Finally, this work provides both kinetic and thermodynamic information about iron (hydr)oxide nucleation controlled by membrane-related functional groups (OH, COOH, and F).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗