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DOE OSTI2021

Evaluation of the Large-Scale and Regional Climatic Response Across North Africa to Natural Variability in Oceanic Modes and Terrestrial Vegetation Among the CMIP5 Models

Hydrologic variability is a serious threat to the poverty-stricken regions of North Africa. Meanwhile, the scientific community struggles to attribute these extreme climatic episodes to specific oceanic and land drivers. Prior modeling studies have not assessed simulated feedbacks over North Africa against an established observational benchmark, so their results are considered as untested, model-specific findings. The Coupled Model Intercomparison Project Phase Five (CMIP5) archive represents the state-of-the-art in climate projections, with most CMIP5 models now containing interactive vegetation phenology; however, there have been no studies to date of their simulated vegetation feedbacks. The capability of CMIP5 models at accurately simulating the forcing of both sea-surface temperature (SST) and regional leaf area index (LAI) anomalies on North African climate needs to be a key consideration in assessing the models’ overall credibility and determining appropriate model weighting for developing climate projections. The Generalized Equilibrium Feedback Assessment (GEFA) is a promising statistical method, which can be applied to either model output or observations, for isolating the local and remote impacts of individual oceanic or terrestrial forcings on regional climate. We performed a combined observational and modeling assessment of land-ocean-atmosphere interactions across the distinct ecological and moisture gradients of North Africa. We evaluated and demonstrated the reliability of the GEFA statistical method over North Africa using the Community Earth System Model (CESM), applied GEFA to observational data to quantify the observed forcing of ocean basin SST anomalies and regional LAI anomalies on North African climate, evaluated the CMIP5 models’ performance in terms of representing these key observed feedbacks, and formulated CMIP5 feedback performance metrics for weighting North African climate projections. Our study represented the first attempt to separate the observed roles of oceanic and vegetation feedbacks across North Africa, the first systematic assessment and intercomparison of land-ocean-atmosphere feedbacks in CMIP5, and the first exploration of vegetation feedbacks among CMIP5 models. The following work was accomplished. (1) The team generated 14 scientific publications and 30 presentations based on the DOE-funded research. (2) A stepwise version of the GEFA statistical method was developed in which unimportant forcings were dropped to increase the reliability of the results. (3) SGEFA was successfully validated through experiments with CESM in terms of its ability to isolate the atmospheric responses to individual oceanic or land forcings. (4) Observational evidence was revealed for the Sahel’s positive vegetation-rainfall feedback on the seasonal to interannual time scale, and it was attributed to a moisture recycling mechanism rather than an albedo mechanism. (5) An approach with SGEFA was developed in which the individual contributions of soil moisture versus vegetation forcings could be separated, revealing that the former forcing outweighed the latter for sub-Saharan Africa. (6) Tropical ocean temperatures were found to be key regulators of pan-tropical vegetation variability, especially for arid and semi-arid regions, including sub-Saharan Africa. (7) The CMIP5 models largely underestimated the importance of land feedbacks across the Sahel. (8) The general consensus among CMIP5 models indicates, for the late 21st century, a diminished seasonal predictability of sub-Saharan African regional climate and an elevated role of the land surface compared to oceanic drivers in regulating regional climate variability. (9) Land-ocean-atmosphere interactions were demonstrated to be key contributors to the seasonal predictability of African wildfire activity. (10) The El Djouf was determined to be of greater importance than the Bodélé depression in terms of providing trans-Atlantic dust transport to the Americas.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Enhancement of EM Pump Performance through Modeling and Testing

Liquid sodium has seen development as a nuclear reactor coolant in the United States for over 50 years. While sodium has many desirable characteristics as a heat transfer fluid, its reactivity poses numerous engineering challenges for even the most basic plant components like pumps. Despite the challenges, mechanical pumps have seen success in sodium service albeit at the expense of simplicity and flexibility. In fact, significant failures of these pumps have been recorded which can be attributed to their complexity. Notwithstanding the complexity, mechanical pumps are still widely proposed for the main circuit pump of sodium cooled reactors. However, an alternative to mechanical pumps are Electromagnetic Induction Pumps (EMIPs) which use the principle of the Lorentz Force. EMIPs avoid mechanically moving parts in contact with sodium which simplifies the design by avoiding mechanical wear and seals. Therefore, these pumps are generally safer and more reliable than their mechanical counterparts. That being said, EMIPs suffer from relatively low efficiencies that range from 5% to 45%. Some of the inefficiencies are inherent to the EMIPs pumps; namely, inefficiencies arise in the form of resistive heating losses in the fluid itself. However, other inefficiencies arise from Edge Effects which include Finite Length and Finite Width effects. Due to the significant safety advantages EMIPs have compared to mechanical pumps, there is a strong motivation to integrate these technologies into the design of Generation IV liquid-metal-cooled nuclear power plants. However, EMIPs have low efficiencies partly attributed to Edge Effects. Much of the literature focusing on Edge Effects is dated or simply does not exist in the public domain. More recently, numerical models of EMIPs have shown promising results. However, these models are often custom solvers or use complex software suits which limit their application. This report describes the work completed to understand Edge Effects in two EMIPs designs. These designs are the Annular Linear Induction Pump (ALIP) and Permanent Magnet Induction Pump (PMIP) also known as a Moving Magnet Pump (MMP). The methodology developed during this project aim at measuring key performance parameters (pressure-flowrate and efficiency-slip performance) using high temperature liquid-sodium. Baseline experimental measurements were then used to verify the accuracy of numerical models developed in the software suit ANSYS/FLUENT. On the experimental side, instrumentation was developed to characterize the pressure-flowrate and efficiency-slip performance of both the ALIP and PMIP in high-temperature liquid-sodium. Additionally, instrumentation was developed to measure potential output pressure pulsations and the magnetic field distributions. Moreover, a specialized ALIP was constructed to study the impacts magnetic field tapering, also known as coil grading, on the Finite Length Effect. Lastly, experimental performance curves of the PMIP were collected at 300 oC and 600 oC and compared to analytical theory which accounted for Finite Width Effects. These results showed that the Finite Width Effect and non-uniform magnetic field in a PMIP impact the performance. On the numerical side, models were developed using the software suit FLUENT/ANSYS. These methods were initially compared against a simple MHD flow in a Permanent Magnet Electromagnet Flowmeter (PMEMFM). The results were significant in that they showed agreeance between the numerical model and demonstrated that PMEMFM’s could be calibrated numerically. These modeling methods were then applied to a prototypic PMIP. Results showed that this simple model provided reasonably accurate results of the PMIP p-Q performance.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Mesh Failure Prediction Using Deep Learning Techniques

The goal of this joint effort between SRI and LLNL has been to investigate the use of state-of-the-art deep learning architectures to effectively explore the correlations between a set of mesh zone attributes and manifestations of failure in a given mesh simulation and draw useful conclusions towards a practical failure detection system. Each zone attribute is a scalar feature that is recorded separately for each zone during simulation, resulting in a multivariate time series signal for each zone. Currently, these features are used to manually decide where to apply manual relaxations so that simulations run without any failures. Our long-term vision is to build a machine learning system that is capable of reliably detecting upcoming failures and is able to apply finer-grained targeted relaxations automatically to relevant zones to avoid an expected failure.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

The use of seismic spatial gradients in a single layer neural network for seismic source discrimination: proof of concept

This report describes a proof-of-concept method of seismic source discrimination using seismic gradiometry and a common machine learning technique. The tests described here are purely numerical, using synthetic seismic data and well understood mathematical techniques. The primary innovation described here is the application of a richer seismic data set derived from seismic gradiometry. Seismic gradiometry is a method to estimate the time variable spatial gradient of the wavefield to compute various wavefield attributes such as slowness, dynamic strain, and rotational motions. With the addition of these wavefield attributes, we are afforded up to twenty "compo- nents" of time series data measured at a single point on, or in, the Earth. This is in direct contrast to conventional three-component seismic data collected at several locations using a seismic network. Using the gradiometrically-derived wavefield components directly in a single-layer neural network, I show that it is possible to discriminate between three common seismic source types (earthquakes, explosions, and opening fractures) for various noise conditions and gradiometry configurations.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2020

SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking (FY2020 Progress Report)

3013 corrosion↗

DOE OSTI2020

Voluntary Renewable Energy Procurement Programs in Regulated Utility Markets

Multinational corporations are increasingly purchasing renewable energy (RE) to fulfill international commitments, reduce supply chain emissions, limit environmental impact, and secure stable and affordable electricity. The potential scale of global corporate RE purchasing has been estimated to be nearly 100 GW and growing; however, many markets still lack supportive enabling environments for corporations to access RE through on-site project development or private sector transactions. Even where allowed by law, on-site generation may be insufficient due to space and technical constraints, introducing additional challenges. Utility green pricing and utility green tariff programs backed by renewable energy certificates (RECs), referred to as utility green procurement programs (GPPs) in this report, offer powerful market-based solutions to utilities, regulators, and policymakers to provide corporate and other consumers with RE product options while generating revenue to support RE development. As RE markets expand around the globe, GPPs have proven to be effective mechanisms in market regimes, ranging from fully integrated state-owned utilities to broadly liberalized power markets. GPPs utilize RECs, which are a type of energy attribute certificate and closely resemble guarantees of origin, to track and ultimately monetize RE attributes that corporations and other buyers must procure to demonstrate progress against their RE commitments and make public claims of RE use. Time-tested and transparent REC accounting mechanisms provide market confidence, while at the same time offering flexibility to utilities, regulators, and customers for a range of applications. RECs are used in all types of electricity market structures, as indicated in Figure 1, but REC-based program designs and supporting components differ depending on the type of market. Liberalized markets allow for customers to contract directly with generators for electricity and RECs, while traditionally regulated markets with vertically integrated utility structures may have greater restrictions on generation asset ownership and electricity sales. RE markets can be further defined as being either mandatory or voluntary. Mandatory markets require suppliers to deliver specified amounts of RE to grid customers, such as under a renewable portfolio standard (RPS), while voluntary markets involve no legal mandates, but demand is driven by self-imposed customer goals.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Identification of Critical Process Parameters for Knife Milling and Alternative Communication Strategies

An assessment of knife milling operations was performed to identify parameters that require consideration to model wear and degradation of knife blades. A quality-by-design (QbD) paradigm was followed to identify material and feedstock attributes, and processing parameters that impact the quality attributes of the blades used in the comminution of biomass into forms compatible with downstream operations. Based on analytical and experimental observations of knife and tool wear, a QbD model is proposed that considers three wear mechanisms (erosion, abrasion, and gouging) to predict volumetric wear of knife blades and sharpness. These factors, if properly applied, provide a science-based approach to predict the mechanical efficiency of the mill as a function of feedstock properties, knife blade material properties, and processing parameters (feed rate, speed, and design parameters).

09 BIOMASS FUELS↗

DOE OSTI2020

Anthropogenic Markers in Alta Irrigation District Water Supply Wells

In July 2016, LLNL produced a report for the Kings River Watershed Coalition Authority (KRWCA) entitled “Groundwater Age and Nitrate Isotopic Composition in Alta Irrigation District Water Supply Wells” (Esser et al., 2016). That report summarized groundwater age and nitrate isotopic composition data produced by LLNL for Alta Irrigation District (AID) groundwater wells sampled by KRWCA in a pilot project to test the utility of advanced isotopic methods for constraining the timing and source of nitrate in groundwater at selected sites in the Kings River service area. One section of that report made recommendations for follow-on study. The first and strongest recommendation was to measure a wider suite of source tracers including co-contaminants such as herbicides, pesticides, pharmaceuticals, personal care products, and food constituents and additives. KRWCA followed up on this recommendation in Phase II of the pilot study by re-sampling the original set of wells and submitting the samples to a commercial laboratory for analysis of a limited range of co-contaminants (including pharmaceuticals, personal care products , and food constituents and additives, but not including herbicides or pesticides). They have asked that LLNL review and interpret these data in the context of previous LLNL analyses and to evaluate the utility of such co-contaminant analyses in providing additional constraints on the source of groundwater nitrate contamination in Alta Irrigation District water supply wells. The goal of the second phase of this pilot project is to evaluate the utility of co-contaminant occurrence for constraining nitrate source in groundwater at selected sites in the Kings River service area, with special reference to the use of such data in conjunction with advanced isotopic methods such as nitrate isotopic composition and groundwater age. Nitrate isotopic composition alone cannot distinguish nitrate derived from animal manure from nitrate derived from human septic waste. The presence or absence of agricultural chemicals (herbicides, pesticides, veterinary pharmaceuticals) or chemicals associated with people (human pharmaceuticals, artificial sweeteners) can help make this distinction. Co-contaminant analyses using multi-analyte LC-MS-MS protocols are now commercially available and reasonably priced. The feasibility and utility of the using co-contaminants for nitrate source attribution can be evaluated by 1) determining the degree to which co-contaminants occur in distinct and easily attributable groupings (e.g. agricultural chemicals vs personal care products and food additives); 2) determining the degree to which co-contaminant occurrence is consistent with nitrate isotopic composition and groundwater age (e.g. no co-contaminant occurrence in very old groundwater; co-contaminants consistent with animal manure or human septic waste occurring in groundwaters having nitrate isotopic composition also consistent with animal manure or human septic waste); and 3) determining the degree to which co-contaminants provide additional constraint on nitrate source over and beyond nitrate isotopic composition and groundwater age (e.g. distinguishing between animal manure and human septic waste).

58 GEOSCIENCES↗

DOE OSTI2021

New Capability at ORNL: High Precision Uranium Titration

Destructive analytical measurements establish a nuclear facility’s nuclear material inventory and inventory differences for Nuclear Material Accountancy and Control. A nuclear laboratory’s ability to perform accurate high-precision analytical measurements is key for tracking large inventories within a facility’s material balance areas and during production to track material movement through dynamic processes. For uranium, these measurements are made by using several established high-precision measurement protocols. These include isotope dilution mass spectrometry, gravimetry, and potentiometric titrations. Whatever measurement technique a nuclear lab chooses to use, reference materials (RMs) with certified attributes and accompanying uncertainties are used to calibrate measurement systems, and are the cornerstone for accurate results. In addition to calibration, RMs provide for metrological traceability, are used for method development and validation, and thus provide critical evaluations of the appropriateness and performance of analytical processes used. Evaluations may include validation that a method is fit-for-purpose, quantification of systematic and random biases, and the evaluation of long-term and short-term performance metrics. High-precision measurement techniques require that measured attributes be certified to a high degree of precision in the RMs used—ultimately, to a higher degree than that of the measurement technique itself. The US authority on the production of special nuclear material Certified Reference Materials is the NBL Program Office (NBLPO), formally known as New Brunswick Laboratory (NBL). The NBLPO is responsible for the sales and distribution of existing NBL certified reference materials (CRMs) and for the production of the next generation of nuclear RMs. To accomplish its mission, NBLPO is establishing key base capabilities within the DOE laboratory complex that formerly existed at the NBL laboratory. The Nuclear Analytical Chemistry (NAC) section within the Chemical Sciences Division (CSD) at Oak Ridge National Laboratory (ORNL) is currently working with NBLPO to set up laboratory and measurement capabilities to provide measurements and capabilities for production and/or recertification of existing and future CRMs for uranium assay. The NBL-developed high-precision titration (HPT) method is a critically-evaluated, extremely precise and accurate primary method utilized for the determination of uranium content in a variety of uranium materials. The HPT method, combined with detailed balance weighing protocols, provides for an analytical methodology that is unsurpassed in precision and one in which all sources of error have been evaluated, a requirement of CRM certification. HPT capability within the United States was lost with the closure of the labs at NBL. The NAC has been collaborating with NBLPO to stand up and demonstrate the capability to perform uranium assay via HPT at ORNL. HPT can produce results with an expanded uncertainty of approximately 0.01% for pure uranium compounds, with typical precisions of <0.006%. The major tasks required to stand up the method at ORNL were the refurbishment of a dedicated lab and equipment setup, procedure development, analyst training, establishing method-specific quality assurance, and qualification of an analyst to perform the method. This report summarizes these tasks, outlines the documents drafted, and gives the outcome of the qualification titrations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2021

An Economics-by-Design Approach Applied to a Heat Pipe Microreactor Concept

Microreactors present a potential paradigm shift in the nuclear industry. Emphasis thus far has been on large-scale multi-billion-dollar projects that cater solely to grid electricity market. These projects can be challenging to finance and execute. On the other hand, microreactors are intended to target a wide variety of smaller niche markets and are expected to be factory-fabricated and more readily deployable. While diseconomies of scale for microreactors may tend to raise their costs per energy output (MWh) relative to large nuclear plants, offsetting gains can be expected from standardization, simplification, passive safety, lower radionuclide inventories, factory fabrication, fast installation, and low financing costs. To adequately assess these contributions, designers should have a different perspective on cost drivers than for large nuclear plants and can utilize novel approaches for systematic cost reduction. To account for these important aspects of microreactors, this report proposes an economics-by-design approach that places economic considerations at the center of the design process. The methodology builds on existing frameworks such as design-to-cost and value engineering, expanding them to new markets (beyond the grid), new attributes (beyond costs alone), and introducing the approach at earlier points in the design cycle. Design parameters and technical specifications are systematically evaluated until costs meet market entry points, while also providing the high-priority performance attributes of the particular use case. Determining first-order estimates for different components early in the process enables designers to focus R&D efforts on the biggest overall cost contributors and components with the most cost uncertainty. The analysis is always guided by market needs and threshold prices. In addition to microreactors, the approach is expected to be useful for other classes of nuclear reactors as well. The analysis was applied to a concept found in the open literature (the Design A heat-pipe reactor). A comprehensive bottom-up estimate was generated by leveraging a new microreactor-specific code of accounts and a range of cost equations. The initial estimate for levelized cost of electricity (LCOE) unsurprisingly exceeded market ranges since the use case had prioritized technological readiness over economic considerations in design choices. An alternate concept was then proposed, with various assumptions/targets made to reduce the largest cost contributors. Changes in the neutron spectrum, the power output, and building structures were found to make even the first-of-a-kind of this modified concept competitive with diesel generation in some remote communities. Learning rate (LR) assumptions indicated cost reductions achieved from sequential unit deployments could expand the range of competitiveness to include additional markets as deployments proceed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

When should we care about new technology in the nuclear domain? Assessing emerging technologies impacts on stability

As new and modernized systems are fielded by the U.S. and Russia, and as China expands its nuclear stockpile with 21st century technology, it is important to ask when do new technologies in the nuclear domain actually matter? When do emerging capabilities and replacements of existing systems change the military realities of the world’s nuclear powers and the lived experiences of the people in these countries? Specifically, it is important to consider what are the attributes of emerging weapon systems that may impact nuclear strategic stability, or in even narrower terms, which attributes of newly fielded military systems may make nuclear conflict more likely or less likely? It is through this understanding that policy makers, voters, and the broader nuclear weapons community can evaluate when and how to respond to emerging technology while reducing the likelihood of nuclear escalation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2021

Optimized Carbon Fiber Intermediate Development to Enable High-Volume Manufacturing of Lightweight Automotive Composites

The ongoing pursuit of improved fuel economy and reduced greenhouse gas emissions has resulted in sustained interest for lightweight materials technologies. In this context, carbon fiber composites have captured the imagination of automotive engineers due to the potential to achieve substantial mass reduction when compared to traditional steel construction. That stated, the use of carbon fiber composites in automotive has been limited for the most part to premium supercars and other derivative platforms. In these cases, manufacturing costs are less of an obstacle to implementation, and the performance benefits of carbon fiber have enabled production of structures offering more than 50% weight savings. In practice, translating these low-volume demonstrations onto high-volume vehicle platforms has remained challenging. This can be attributed to several factors, with the absence of suitable high throughput production methodologies being a key impediment. To date, structural, crash critical components have relied upon manufacturing techniques born out of the aerospace industry. This has created a disconnect between automotive production systems that are accustomed to manufacturing multiple parts per minute and the aerospace technologies that have cycle times in the order of hours. Consequently, the focus of this project is the development of manufacturing process technology for carbon fiber composites that can support a mainstream vehicle program at an assumed throughput of 100,000 vehicles per year. In practice, this translates to a part-to-part cycle time of less than 3 minutes. Project participation included contributions from a broad range of academic, industrial and national lab partners. The primary scope of work, being the development of new carbon fiber epoxy compounds that are stable at room temperature and suited to high throughput automated processing. For project management, the work streams were divided into six key areas, with the lead organization in parentheses. • Carbon fiber/epoxy materials formulation development and scale up (Dow) • Simulation of discontinuous near isotropic meso-structure intermediates (Purdue) • Simulation of mechanical performance of compression molded components (Purdue) • Meso-Scale morphological analysis and correlation with structural performance (UTK) • Paint and adhesion durability analysis (MSU) • Demonstrator part design, prototype production, and validation testing (Ford). The primary goal at the commencement of the project was development of a chopped carbon fiber sheet molding compound (SMC) that offered a three times improvement in tensile modulus over a comparable glass-based SMC. In addition to meeting mechanical performance targets, the resin kinetics were modified to achieve a processing cycle time of less than 3 minutes. Other critical-to-quality (CTQ) specifications were also stipulated to account for a broad range of materials and processing characteristics. To achieve the above, staff scientists at Dow Chemical created an extensive series of new epoxy blends for testing and validation. Throughout this development, a key challenge was attaining material performance goals without impacting processing behavior and paintability of finished components. The latter required a new internal mold release system being developed by Dow that was designed to complement the kinetics of the rapid cure epoxy. As a complement to work studies at the industrial partners, the teams from academia executed a series of analytical and experimental studies to investigate potential factors influencing CF-SMC performance. Unit cell models were developed to capture the meso-scale representations of the fiber matrix architecture. Results of this analysis and subsequent morphological investigations led to the design of a novel composite derivative comprising carbon fiber platelets embedded in an epoxy matrix; the platelet size and aspect ratio playing significant role in final composite properties. This approach was a departure from previous research in CF-SMC development whereby bulk filamentization or disassembly of the carbon fiber rovings had been considered the most effective means of achieving both fiber wet through and wet-out. As the course of the academia studies progressed, the aspect ratio of the fiber constituents was further optimized before finalizing material attributes and processing conditions. For the purposes of technology validation, the Ford team led a work stream devoted to the design, fabrication and testing of demonstration components. The carbon fiber SMC material has the potential to displace numerous stampings and castings on an automotive structure but ultimately vehicle closure applications were selected to showcase the abilities of the CF-SMC to achieve both mass reduction and business case for large complex structures. Using target properties established by the Dow staff scientists, the complete closure system for a full-size sedan decklid and a mid-size wagon liftgate were engineered. Prototypes for both applications were fabricated using production representative processing methods to allow for physical testing and performance validation of the CF-SMC structures. Following completion of a testing program that concluded with a FMVSS301 55 mph offset rear crash, the CF-SMC formulation was declared by the Ford team to have met all engineering requirements. To summarize, the joint development activities during this project led to significant technical breakthroughs and achievement of all milestones. The result was the development of a novel, tack-free carbon fiber molding compound that is suited to automated processing. This combined room temperature stability, fast cure kinetics, and internal mold release system facilitates cycle times that are conducive to high-volume production. The VORAFUSE M6400 successfully passed technology validation at Ford and is now eligible for consideration on future production commercial vehicle programs.

36 MATERIALS SCIENCE↗

DOE OSTI2021

FY21 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

The development of algorithms for machine learning and data analysis for the 3013 Surveillance Program is a collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). For corrosion detection, Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS) data is extracted from large binary files, with software written to convert the data to physical attributes (e.g., height, color and grayscale values; all as functions of a location in a plane projection). A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data, flag significant features, execute Machine Learning (ML) algorithms, output parameters for trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Features can be called out by user-specified thresholds, manual labeling or machine learning algorithms when they have been completed. The ability to rapidly label data is important because of the volume of data required for training machine learning algorithms. The GUI has the flexibility to allow addition of improved ML algorithms, methods for data visualization, and statistical computations. Statistical analyses via the GUI include areas of pits within a defined range of pit depths, correlations between Red-Green-Blue (RGB) or grayscale intensity and relative surface height, covariances between values associated with features, and feature histograms. The development of supervised machine learning algorithms, however, has been hindered by a lack of training data. The machine learning algorithms for crack identification are being refined but require improvements to the true positive rate for crack detection. This shortcoming is an artifact of the limited training data currently available, perhaps more so than the structure of the neural networks. At present, the best results are had from a consensus over an ensemble of randomly generated Deep Neural Network (DNN) or Convolutional Neural Network (CNN) algorithms. Although the consensus accuracy method has yielded optimum true positive and true negative rates in excess of 80%, additional validation testing is necessary. In addition to the suite of LCM data that was initially used, and which represents the majority of the work presented in this report, WAMS image data was also reviewed at a preliminary level. The review included a comparison between image resolution and dynamic range for each method. WAMS (ZON file) image data was found to have a pixel pitch of 3.69μm compared to 1 μm for the LCM (vk4 file) data, which implies a lower resolution for the WAMS images. Conversely, the ratio of dynamic range of the WAMS data to the LCM data was approximately 41:20 for height data, suggesting that information from WAMS should more accurately determine the depth of pits. At present, the significance of the greater dynamic range of the WAMS data relative to the LCM data has not yet been evaluated.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

LANL Regular Employee Population and Demographics, FY15-21

This document summarizes an effort to compile and analyze the LANL regular employee population’s demographic attributes and trends between FY15-FY21 using MicroStrategy, a web-based data analytics and visualization tool. LANL’s HR Division collected and prepared data for the Weapons Program’s annual reporting activities for the 2017 through 2023 NNSA Stockpile Stewardship and Management Plan (SSMP). Los Alamos workforce data represent the fiscal year-end snapshot of the permanent employee population who are categorized by the Common Occupational Classification System (COCS). This work enables further analyses, including trends for all workforce attributes presented in the SSMPs as well as a definition of a crosswalk between COCS categorization and the internal laboratory job classification hierarchy. Data for other sites and the federal workforce, as shown in the SSMPs, are included and corresponding figures can be visualized within the MicroStrategy tool. A portable, Excel-based version of the tool was developed and shared with the multi-site workforce working group. This summary highlights selected aspects of the LANL regular workforce. The data analyses and visualizations communicate insights within three major themes regarding permanent LANL employees: 1) demographic transformation, 2) attrition, and 3) skills. Regarding demographic transformation, and consistent with the general trend across the enterprise, early career regular employees are the fastest growing group, with growth ranging between 12-34% year-to-year over the seven-year period analyzed. The data also reflect effects of internal and external factors on attrition; the large spike in separations in FY18 aligns with the contract transition from LANS to Triad and the dip in attrition between FY20-FY21 is likely a result of the global pandemic. The population of regular employees in general management, engineer, and operator COCS categories have the fastest growth rates amongst all the occupational categories. The MicroStrategy and Excel tools provide additional details and trends.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Feedstock-Conversion Interface Consortium: Failure Mode and Effects Analysis Summary Report (FY2022)

This report provides an overview of the development of failure modes and effects analysis (FMEA) and its implementation as a systematic criticality and risk assessment tool supporting a quality by design (QbD) approach for FCIC research. This report also provides a high-level overview of the results for the FMEA evaluation of two feedstock preprocessing system configurations: (1) generation of pine residue materials for high-temperature pyrolysis conversion and (2) generation of corn stover materials for low-temperature conversion using deacetylation and disc mechanical refining pretreatment for fermentation to hydrocarbons. For the results presented in this report, our FMEA interviews included two approaches. The first approach was to perform FMEA interviews for the entire system of unit operations giving a wholistic system level view. The second approach consisted of detailed interviews for each individual unit operation within the system allowing for a “deep dive” into the specific failures for the individual components within the configuration. These two approaches provide different resolutions of information. The FMEA results of this report were focused on failures associated with meeting critical quality attributes (CQAs) identified for the target conversion processes for each processed feedstock type. The information gathered through the FMEA interviews include estimations of risk scores for meeting each given CQA specification, identification of the impacts for not meeting a CQA specification, capturing causes associated with material attributes and process parameters for each failure, identification of current detection methods, and speculation of potential mitigation strategies for decreasing a failure’s risk score. The complete results of all FMEA interviews are provided in the Appendices of this report.

09 BIOMASS FUELS↗

DOE OSTI2021

Nondestructive Assay Technical Infrastructure Program Mission and Vision

The Nondestructive Assay Program (NDAP) mission and vision is achieved by identifying and accomplishing a set of programmatic goals that correspond with eight broad technical program elements. The NDAP was created as a result of Defense Nuclear Facility Safety Board Recommendation 2007-1, entitled “Safety-Related In Situ Nondestructive Assay of Radioactive Materials,” which emphasized the need to improve in situ measurements of radioactive material at US Department of Energy (DOE) defense nuclear facilities. An NDAP five-year plan will define tasks to accomplish specific goals identified in the NDAP mission and vision. This mission and vision is applicable for eight technical program elements: Hardware/Software Development, Algorithm Development and Nuclear Data, Uncertainty Quantification, Nuclear Materials, Staffing, Personnel, And Training, Data Management, Requirement and Standards, and Information Preservation and Dissemination. An additional program element is technical support, which provides daily execution management support for the NDAP, site scope, and deliverable tracking via site task managers, along with technical advisement and support from the technical support group. The NDAP mission and vision provides attributes with specific goals for each program element, and some goals benefit multiple program elements. The goals and attributes defined herein are implemented via an NDAP five-year execution plan that defines site work scope, budget, and deliverables, all of which are updated annually. The NDAP is designed to benefit nondestructive assay (NDA) needs to support DOE nuclear criticality safety programs, ensuring that NDA technology is sufficiently capable of guaranteeing the safety of those who handle, store, process, or transport fissionable materials in the complex. Especially important for the NDAP is to maximize capabilities to identify, characterize, and manage in situ fissile material deposits in process equipment to ensure nuclear criticality safety at processing facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Impact of modifier-rich coatings on the ionic transport of glasses [Poster]

Ionic conduction in silicate glasses is mainly influenced by the nature, concentration, and mobility of the network-modifying (NWM) cations. The electrical conduction in SLS is dominated by the ionic migration of sodium moving from the anode to the cathode. An activation energy for this conduction process was calculated to be 0.82eV and in good agreement with values previously reported. The conduction process associated to the leakage current and relaxation peak in TSDC for HPFS is attributed to conduction between nonbridging oxygen hole centers (NBOHC). It is suggested that ≡Si-OH = ≡Si-O - + H 0 under thermo-electric poling, promoting hole or proton injection from the anode and responsible for the 1.5eV relaxation peak. No previous TSDC data have been found to corroborate this mechanism. The higher activation energy and lower current intensity for the coated HPFS might be attributed to a lower concentration of NBOHC after heat treatment (Si-OH + OH-Si = SiO-Si + H 2 O). This could explain the TSDC signal around room temperature for the coated HPFS. Another possible explanation could be a redox reaction at the anode region dominating the current response.

36 MATERIALS SCIENCE↗

DOE OSTI2022

FY22 Progress Report: SRNL Analysis of ICCWR LCM and WAMS Data for Corrosion and Cracking

Algorithms for machine learning and data analysis for the 3013 Surveillance Program are being developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). To detect the presence of corrosion and cracking, data is collected from large binary files generated by a Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS). Software is being developed to use the physical attributes in the data files (e.g., height, color, and grayscale values; all as functions of a location in a plane projection) to detect the presence of surface corrosion and cracking. A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data for training ML algorithms, flag significant features, execute Machine Learning (ML) algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Surface defects can be called out by setting user-specified thresholds, feature based analysis or machine learning algorithms. Enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the very large volume of data required to train ML algorithms.

3013 Corrosion↗