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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 1,747 records · Page 97

An Optical Differentiator Based on a Three-Layer Structure with a W-Shaped Refractive Index Profile

An optical differentiator based on a three-layer resonant structure with a W-shaped refractive index profile is proposed. The differentiation operation is performed in reflection and is associated with the resonant excitation of an eigenmode of the structure localized in the central layer. The presented results of numerical simulations demonstrate the possibility of spatial differentiation of the transverse profile of an incident optical beam, temporal differentiation of the envelope of an incident optical beam, and simultaneous spatial and temporal differentiation with a high quality. The proposed structure can find applications in designing analog optical computing and optical information processing systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

SNL-NJOY-2016

This is a wrapper around the LANL NJOY-2016 code that interfaces with extended code capabilities and minor modification of the LANL NJOY-2016 code that supports modeling radiation damage to materials. The NJOY Nuclear Data Processing System is a modular computer code designed to read evaluated data in ENDF format, transform the data in various ways, and output the results as libraries designed to be used in various applications. The wrapper provided here permits Sandia-specific control parameters to be used in the input data file. The modifications incorporated here enhance the ability of NJOY to address material damage response functions for many materials, e.g. to include the NRT and arc-dpa forms of the damage energy in addition to the default NJOY-2016 implementation of the sharp-threshold Kinchin-Pease threshold energy treatment. All of the modifications provided here are being made available to the GitHub-based NJOY-2016 code. As useful enhancements found here are incorporated into the baseline NJOY-2016 code, they will be eliminated from this version so as to maintain our compatibility with the baseline NJOY-2016 code. SAND2020-13060 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA00035

Griffin, Patrick↗

Building heights and urban canopy parameters for urban modeling

GLObal Building heights for Urban Studies (UT-GLOBUS) is a random forest model based framework that provides a level-of-detail-1 (LoD-1) building height dataset. The primary objective of UT-GLOBUS is not to precisely predict the height and footprint of individual buildings, but rather to offer a functional framework for generating building level information using open-source datasets for modeling applications. Specifically, UT-GLOBUS is tailored to meet the requirements of deriving urban canopy parameters (UCPs) for the multi-layer model within the Weather Research and Forecasting (WRF) model and building heights for the SOLWEIG and SUEWS model. Building-level data is accessible in vector file format (GeoPackage: .gpkg), which can be converted into raster file format (geoTIFF). The vector files employ the Universal Transverse Mercator (UTM) projection. The vector files are compatible with GIS platforms like QGIS and ArcGIS, and can be imported for analysis using programming languages such as Python. We are also providing UCPs required by the multi-layer urban model in the urban WRF in binary file format. Additionally, we provide the urban fractions calculated using ESA world cover dataset (https://esa-worldcover.org/en) for WRF model in binary file format. These binary files can be directly incorporated into the WRF pre-processing system (WPS).

54 ENVIRONMENTAL SCIENCES↗

A Wrapper to Use a Machine-Learning-Based Algorithm for Earthquake Monitoring

Seismology is one of the main sciences used to monitor volcanic activity worldwide. Fast, efficient, and accurate seismicity detectors are crucial to assess the activity level of a volcano in near–real time and to issue timely warnings. Traditional real–time seismic processing software uses phase onset pickers followed by a phase association algorithm to declare an event and estimate its location. The pickers typically do not identify whether the detected phase is a P or S arrival, which can have a negative impact on hypocentral location quality and complicates phase association. We implemented the deep–neural–network–based method PhaseNet to identify in real time P and S seismic waves on data from one– and three–component seismometers. We tuned the Earthworm binder_ew associator module to use the phase identification from PhaseNet to detect and locate the events, which we archive in a SeisComP3 database. We assessed the performance of the algorithm by comparing the results with existing catalogs built to monitor seismic and volcanic activity in Mayotte and the Lesser Antilles region. Our algorithm, which we refer to as PhaseWorm, showed promising results in both contexts and clearly outperformed the previous automatic method implemented in Mayotte. As a result, this innovative real–time processing system is now operational for seismicity monitoring in Mayotte and Martinique.

58 GEOSCIENCES↗

Los Alamos National Laboratory Nuclear Material Management and Storage Program: Storage Health Metrics

The Strategic Materials Sustainment Program (SMSP) supports activities needed to manage the National Nuclear Security Administration’s (NNSA) inventory of strategic materials. As part of the SMSP, the Nuclear Material Management and Storage (NMM&S) program at the Los Alamos National Laboratory (LANL) is responsible for: (1) safe and secure storage of the SMSP strategic materials and others, (2) analysis, forecasting, and management of accountable nuclear materials; and (3) disposition of nuclear material through the Office of Nuclear Materials Integration (ONMI). The nuclear material management (NMM) mission is to ensure efficient nuclear material management lifecycle activities at LANL. The storage program function includes the management of storage capabilities across the site, supply chain management of nuclear materials, storage requirements integration, and nuclear container management (i.e., procurement, compliance, and safety). This report identifies key performance indicators (KPI) and describes the criteria and metrics used to assess the health of the storage component of the NMM&S program. “Health” is the ability of a process, system, facility, or program to safely and reliably accomplish its required mission. A health assessment provides timely identification of significant risks to enable mission performance. Significant risks identified are most effectively mitigated, avoided, or eliminated by evaluating and coordinating with stakeholders to specifically target, document, and often proceduralize strategies directed towards risk reduction. An effective health assessment involves status measurements of specific metrics that are tied directly to representative KPIs for a program. This document will baseline the KPI, criteria, and specific metrics that will be used to measure the “health” of the LANL Storage Program (i.e., NMM is not included).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Pit Realization Process Manufacturing Readiness Assessments

This document defines a manufacturing readiness assessment process within a Product Realization Process (PRP) for pit manufacturing at Los Alamos National Laboratory (LANL) having the following objectives. Meet National Nuclear Security Administration (NNSA) Defense Programs Business Process System (DPBPS) PRP requirements. Use language established within LANL’s pit manufacturing program. Enable a disciplined approach to evaluate pit manufacturing readiness. Provide effective tools and metrics to assess manufacturing system maturity.

42 ENGINEERING↗

Process Image Analysis using Big Data, Machine Learning, and Computer Vision

Within the DOE complex, 3013 canisters are used to store Pu-bearing waste. Wall penetration due to corrosion, specifically stress corrosion cracking, is considered to be the most likely cause of failure over the 50-year lifetime of the canisters. This project has 2 objectives: The first is the development of machine learning algorithms to identify the presence of corrosion from a very large set of images generated by laser confocal microscope scanning of 3013 canister used to store Pu oxides. This portion of the LDRD constitutes image analysis of a metal surface for the presence of corrosion. The image processing algorithms developed for this project will provide a suitable basis for analysis of other types of corrosion data, which can be produced in vast quantities using modern devices. The second component of the LDRD consists of the development of machine learning algorithms that obtain molecular mechanics force-fields from ab-initio Density Functional Theory (DFT) calculations for corrosive attack by chlorides on 304L or 316L stainless steel. The goal of this latter component is to determine means for mitigating corrosion on a fundamental level, including coatings, welding methods, metal composition, etc. Force-field modeling is necessary for this endeavor because the incipience and progression of corrosion is the governed by molecular structures, grain boundaries, dislocations and surface structures represented by large numbers of atoms. Although DFT calculations are extremely adept at describing molecular scale processes, they are computationally expensive making them ill-suited for calculations of more than several hundred atoms, especially for the repeated applications inherent in material design. Fortunately, force-field methods provide an avenue for viable molecular-scale calculations involving the numbers of atoms involved in corrosion processes. The accuracy of the force-field calculations depends strongly on the accuracy of the force-field model, which is extremely difficult and time-consuming to derive from either data or ab-initio calculations. The use of machine learning algorithms has the potential to make the calculation of force-fields much more efficient but has not been explored significantly for corrosion processes. If shown to be viable, the technique would have wide -ranging impact on design of corrosion resistant materials and on the mitigation of corrosion in existing process systems.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Access Control: A Review of 2015-2019 Events (SNL/NM)

Due to a number of recent events where access control into or out of a hazardous area or operation was a factor, the Environment, Safety, and Health (ES&H) director requested a review of access control events occurring at Sandia National Laboratories from 2015 to January 2019. The purpose of the review was to determine the extent of access control as an ES&H issue, draw preliminary conclusions from the data, and identify recommendations for improvement, if appropriate. Using the Occurrence Reporting and Processing System (ORPS) database, nine events from 2015 through January 2019 were identified as situations involving access control of personnel into or out of a hazardous area or operation. Supplemental records from the Assurance Information System (AIS) database and the occurrence management team's repository in Electronic Integrated Management System (EIMS) were sought to confirm this dataset of access control events and to gather details concerning the causal factors associated with each event.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Influence of Glycolate Anion on Aqueous Corrosion in DWPF and Downstream Facilities – SCC Testing

Glycolic acid is being evaluated as an alternate reductant in the preparation of High Level Waste for the Defense Waste Processing Facility (DWPF) at the Savannah River Site (SRS). During processing, the glycolic acid may not be completely consumed with small quantities of the glycolate anion being carried forward to waste facilities. The SRS liquid waste contractor requested an assessment of the influence of the glycolate anion on the corrosion of the materials of construction (MoC) throughout the waste processing system since this concern had not been previously evaluated. The influence of glycolic acid on the occurrence of stress corrosion cracking (SCC) had not been evaluated specifically in the previous testing. The present testing used electrochemical and coupon immersion testing to assess this influence. Additionally, the primary guidance on corrosion for operating DWPF with a formate-based flowsheet is based on testing that utilized test solutions with a small number of constituents. Recent testing used more complex solutions based on flowsheet development for DWPF. Additional electrochemical tests were also conducted to assess the influence of this difference on test results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Materials and Fuels Complex Operations Management Improvement Strategy

The Materials and Fuels Complex (MFC) has experienced substantial growth in terms of staff, research, and production in recent years. MFC operational performance has effectively kept pace with this growth. However, to capture the continuous improvement actions needed to improve effectiveness and increase the efficiency of our management systems, a broad operations management strategy is necessary. The MFC Operations Management Improvement (OMI) Strategy is complementary to the MFC Five-Year Mission and Investment Strategies and the MFC Management Plan. The OMI strategy is structured to address the management systems outlined in the Nuclear Facility Management Standard Operations Model. Each management system is evaluated independently in a chapter that describes the prior 5 years of performance improvement, a description of improvement actions for the MFC staff that directly perform within or contribute to the management system, process improvements, and any needed equipment improvements. Additional chapters are added to address subject areas not formally described in the standard operations model. The OMI strategy actions are structured to have cross cutting impacts for improvements pertaining to the entire directorate or a single division. All division improvement agendas are aligned to the OMI strategy.

99 GENERAL AND MISCELLANEOUS↗

A science paradigm shift is needed for Earth and Environmental Systems Sciences (EESS) to integrate Knowledge-Guided Artificial Intelligence (KGAI) and lead new EESS-KGAI theories

The focal area of this white paper is learning from complex data through the use of AI techniques and AI-derived model components. Specifically, we advocate for research programs to develop knowledge-guided AI (KGAI) in the Earth and Environmental Systems sciences (EESS) as a basic research paradigm that is separate from (but supports) any specific Earth system model, modeling components, and modeling workflows, and even separates from specific hypothesis-driven questions about individual Earth system processes.

54 ENVIRONMENTAL SCIENCES↗

Engineering-Scale Batch Purification of Ternary MgCl 2 -KCl-NaCl Salt Using Thermal and Magnesium Contact Treatment

High-temperature molten chloride salts are a desirable heat transfer and energy storage media. The NaCl-KCl-MgCl 2 is of particular interest because of its low melting point and relatively inexpensive constituents. Moisture and hydroxides must be removed from the salt to control corrosion at high temperatures. Previous studies have developed and demonstrated various techniques for bench-scale salt purifications. This paper details the system, process, and insight gained in the purification of a 200 kg scale batch of NaCl-KCl-MgCl 2 salt at Oak Ridge National Laboratory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

GraphAlign: Graph-Enabled Machine Learning for Seismic Event Filtering

This report summarizes results from a 2 year effort to improve the current automated seismic event processing system by leveraging machine learning models that can operated over the inherent graph data structure of a seismic sensor network. Specifically, the GraphAlign project seeks to utilize prior information on which stations are more likely to detect signals originating from particular geographic regions to inform event filtering. To date, the GraphAlign team has developed a Graphical Neural Network (GNN) model to filter out false events generated by the Global Associator (GA) algorithm. The algorithm operates directly on waveform data that has been associated to an event by building a variable sized graph of station waveforms nodes with edge relations to an event location node. This builds off of previous work where random forest models were used to do the same task using hand crafted features. The GNN model performance was analyzed using an 8 week IMS/IDC dataset, and it was demonstrated that the GNN outperforms the random forest baseline. We provide additional error analysis of which events the GNN model performs well and poorly against concluded by future directions for improvements.

58 GEOSCIENCES↗

Illuminating the pathways to carbon liberation: a systems approach to characterizing the consequential unknowns of carbon transformation and loss from thawing permafrost peatlands (Final Report)

The IsoGenie3 Project delivered new systems-level insights into carbon cycling in thawing permafrost landscapes, with an emphasis on methane and carbon dioxide emissions. From >200 samples from the site collected over a decade, co-analyzed for geochemistry and microbiology, the team recovered ~1,500 assembled microbial genomes and ~1,900 viral population genomes, revealing appreciable genetic novelty - from a new highly abundant bacterial phylum, to novel methane consumers and their activities, to rampant viral novelty. IsoGenie3 linked these organisms to carbon compound transformations (which define the cycling of organic matter in soils, and the loss of the greenhouse gases carbon dioxide and methane), and saw that the microbes at each stage of permafrost thaw had different genetic potential to degrade categories of compounds, expressed that genetic potential differently, and actually transformed carbon compounds into greenhouse gases in different ways. IsoGenie 3 identified that some of the thaw-stage differences were due to plant-microbiome relationships; the plant species across the thaw gradient contributed different carbon compounds into the soil, and hosted distinct microbiota (differing among parts of plants as well as species). Lastly, microbes in the saturated post-thaw conditions appeared likely to contribute to the mobilization and toxification of mercury released during thaw. In parallel with ongoing field sampling and analysis, hypotheses arising from field observations were tested via lab incubation experiments. When communities are taken out of their native habitats, they behave differently, and the team first rigorously quantified the magnitude of this effect on microbiome composition and functional capacity, organic matter composition, and gas production; overall the main system processes were maintained in the lab incubations under the conditions tested. Further, the microbial data could inform geochemical reaction network models of those processes. Then, the team ran experiments with additions of compounds, varying temperature, and “live” vs. “dead” peat (the latter having been gamma irradiated, with a few additional variants to control for methodological artifacts). From these, we (a) determined the importance of plant-derived soluble phenolic compounds in bogs’ extraordinary recalcitrance of organic matter, and carbon gas emissions skewed to carbon dioxide; (b) proposed an abiotic ‘tanning’ mechanism, which could contribute to Sphagnum’s inhibitory effect on anaerobic decomposition through alteration of N availability. IsoGenie3 illuminated longer-term and landscape-scale interactions of permafrost thaw and carbon cycling, advancing knowledge of the drivers of methane dynamics not only across in the permafrost-associated peatland (where hydrology and plant communities dictate microbiomes) but also their interconnected lakes (where sediment carbon quality and resident microbiota are determined by position within lake, and lake features). By leveraging observations of site methane dynamics extending well before this project, the team was able to construct a 44-year portrait of the interplay of permafrost thaw, hydrology, vegetation dynamics, and carbon gas emissions, and the doubling of the fully-thawed fens over this time. From the detailed study of this focal site, IsoGenie3 also aimed to improve model representation of these kinds of sites and processes. To improve predictions of methane transformations, we incorporated acetate and isotope dynamics into the ‘DNDC’ biogeochemistry model. In addition, recovered genomes were grouped into ‘functional groups’, i.e. the genomes that perform a specific function of interest, then used to parameterize maximum growth rate and optimum growth temperature (via signatures in their sequence composition) for the BioCrunch model. The BioCrunch model was then in turn used to test the impact of increasing functional resolution of the microbes, on the carbon gas emissions. Lastly for modeling, the ecosys model was parameterized from the microbial and other data, and used to evaluate drivers of e.g. change in methane emissions. Finally, this project also led to the development of a range of new methods and tools, a new metric of organic matter decomposability, as well as a graph-database solution to multidisciplinary data storage and querying. This project’s ongoing analyses at our focal site also contributed to broader advancements in understanding elements of genetic plasticity and methane metabolism, climate change microbiology and community assembly, global peatland geochemistry and Arctic lakes’ roles in climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗

An Evaluation of Recent Events Involving Hazardous Energy at INL Facilities

Idaho National Laboratory (INL) Facilities and Site Services (F&SS) has experienced 14 reportable events relating to work performed under lockout / tagout (LOTO) during the period of March 4, 2019 through August 18, 2020. The 14 events are described in Appendix A and were numerous enough and similar enough in nature to be indicative of an adverse trend that warranted further evaluation. A qualified cause analyst was assigned to lead a team in the review of these non-compliances, to determine commonalities, and to provide recommendations to the sponsoring organization. The team focused on both individual and institutional behaviors as well as the LOTO process and procedure. The non-compliances were reported in the Department of Energy (DOE) Occurrence Reporting and Processing System (ORPS) and/or the INL issues tracking system.

99 GENERAL AND MISCELLANEOUS↗

Rational sub-nanometer manipulation of polymer morphology for efficient chemical separations

The ability to control polymer morphology on the sub-nanometer length scale has broad implications for chemical separations. To achieve such control on easily processable systems, this proposal focuses on the synthesis and characterization of polymers containing appended labile moieties that are easily detached by thermolysis or UV irradiation deep within the glassy state. Once liberated, these moieties can diffuse from the polymer matrix as gaseous products, leaving behind templated pathways for selective diffusion and sorption of small molecules. With a specific target of creating polymeric membrane materials with unprecedented diffusion and (ad)sorption characteristics for chemical separations, synthesis of new materials will be complemented with advanced metrologies, simulations, and evaluation of thermodynamic and transport theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗