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DOE OSTI2022

Pellet cladding mechanical interaction as a potential failure mechanism during a control rod drop accident in a boiling water reactor

Boiling water reactors (BWRs) represent approximately one-third of the operating fleet in the United States, contributing significantly towards the global effort in reducing carbon emissions. Even though most of the operating fleet has been in operation for quite some time, continued advancements in new nuclear fuel (such as accident tolerant fuel) or operating regimes (such as power up-rates and higher burnup operation) necessitates similar advancements in modeling and simulation capabilities. Bison, a component of the Virtual Environment for Reactor Applications (VERA), is a high-fidelity fuel performance code able to explore the fuel performance of a wide variety of fuel types in one-, two-, and three-dimensions. Until recently, the code had not been used for analyses of BWRs. Modeling capabilities have been added for Gd-bearing UO{sub 2} and pure zirconium liners. New models have been added based upon the U.S. Nuclear Regulatory Commission (NRC) guide-lines for hydrogen pickup in Zircaloy-2 claddings and pellet-clad mechanical interaction (PCMI) failure during a reactivity insertion accident (RIA), known as a rod drop accident (CRDA) in BWRs. Implementing and/or improving Bison modeling capabilities extended its analytical reach to areas beyond its original intended purpose. This paper demonstrates one of these capabilities as a proof of concept. Recently developed models enable Bison to provide an alternate approach for cladding integrity determination in CRDA evaluations, which currently use bounding conservative estimates. As part of this demonstration, NRC guidance on hydrogen-pickup and PCMI failure models were utilized in this research. Even though more research is needed in establishing right inputs and process in this area, this paper demonstrates Bison's ability to determine cladding integrity in a CRDA evaluation. This first of a kind demonstration is a proof of concept in this area, which could potentially be extended to a number of other areas where a more accurate cladding integrity determination would be needed. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Radiological HEPA Filter 10-year Lifetime Evaluation in Research Facilities

High-efficiency particulate air (HEPA) filters are widely employed by nuclear facilities to remove radiological particulate matter from their effluent exhaust streams. The purpose of this study is to evaluate the relationships between the 10-year HEPA filter lifetime deployment and its other performance indicators. This 10-year-long endeavor to collect and analyze data regarding the service life of HEPA filters at the Pacific Northwest National Laboratory began in 2010. A set of HEPA filters were selected and have been surveyed and analyzed at least annually to verify compliance with permit conditions. The study suggests the frequency of filter replacement should be based on the actual operational requirements, such as fume hood face velocity and/or efficiency test results, instead of on the prescribed filter “age limit” of 10 years from the date of manufacture (e.g., birth date) when operating under dry conditions. The study has now been completed, and over the past decade all the HEPA filters have been replaced, due to either technical issues as listed in this report or the previously recommended filter “age limit” of 10 years as prescribed by the oversight bodies. Experimentally determined failure rates are also determined from the data set and can be used to estimate the chances of HEPA filters surviving 15, 20, or even 30 years.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2024

Dynamics of Intra-Cell Thermal Front Propagation in Lithium-Ion Battery Safety Issues

Thermal runaway (TR), a critical failure mode in lithium-ion batteries (LIBs), poses significant safety risks and hinders wider application of LIBs. TR typically begins at a localized heat source and spreads across the cell. Understanding thermal front propagation (TFP) characteristics, such as front and velocity, is crucial for assessing energy release and temperature distribution for battery hazardous estimation. Recent studies assume that TR within cells propagates at a near-constant velocity, based on the reaction kinetics and thermal properties. Here, in this study, an intra-battery TR model is further proposed and it indicates that TFP velocity stabilizes when the front is distanced from the heat source. Theoretical estimates for propagation velocity and front are developed and validated through numerical simulations and experimental tests from the NREL Battery Failure Databank. The energy release rate during TFP and the impact of preheating based on a point heat source are explored. This work clarifies the long-standing clouds of the thermal font propagation behaviors within the single cell, highlights the power and beauty of mathematics modeling to describe the complicated thermal behaviors, and provides important guidelines for thermal hazardous understanding for next-generation batteries.

25 ENERGY STORAGE↗

DOE OSTI2020

Potential impacts of mercury released from thawing permafrost

Abstract Mercury (Hg) is a naturally occurring element that bonds with organic matter and, when converted to methylmercury, is a potent neurotoxicant. Here we estimate potential future releases of Hg from thawing permafrost for low and high greenhouse gas emissions scenarios using a mechanistic model. By 2200, the high emissions scenario shows annual permafrost Hg emissions to the atmosphere comparable to current global anthropogenic emissions. By 2100, simulated Hg concentrations in the Yukon River increase by 14% for the low emissions scenario, but double for the high emissions scenario. Fish Hg concentrations do not exceed United States Environmental Protection Agency guidelines for the low emissions scenario by 2300, but for the high emissions scenario, fish in the Yukon River exceed EPA guidelines by 2050. Our results indicate minimal impacts to Hg concentrations in water and fish for the low emissions scenario and high impacts for the high emissions scenario.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Development and validation of a 30-day mortality index based on pre-existing medical administrative data from 13,323 COVID-19 patients: The Veterans Health Administration COVID-19 (VACO) Index

Background Available COVID-19 mortality indices are limited to acute inpatient data. Using nationwide medical administrative data available prior to SARS-CoV-2 infection from the US Veterans Health Administration (VA), we developed the VA COVID-19 (VACO) 30-day mortality index and validated the index in two independent, prospective samples. Methods and findings We reviewed SARS-CoV-2 testing results within the VA between February 8 and August 18, 2020. The sample was split into a development cohort (test positive between March 2 and April 15, 2020), an early validation cohort (test positive between April 16 and May 18, 2020), and a late validation cohort (test positive between May 19 and July 19, 2020). Our logistic regression model in the development cohort considered demographics (age, sex, race/ethnicity), and pre-existing medical conditions and the Charlson Comorbidity Index (CCI) derived from ICD-10 diagnosis codes. Weights were fixed to create the VACO Index that was then validated by comparing area under receiver operating characteristic curves (AUC) in the early and late validation cohorts and among important validation cohort subgroups defined by sex, race/ethnicity, and geographic region. We also evaluated calibration curves and the range of predictions generated within age categories. 13,323 individuals tested positive for SARS-CoV-2 (median age: 63 years; 91% male; 42% non-Hispanic Black). We observed 480/3,681 (13%) deaths in development, 253/2,151 (12%) deaths in the early validation cohort, and 403/7,491 (5%) deaths in the late validation cohort. Age, multimorbidity described with CCI, and a history of myocardial infarction or peripheral vascular disease were independently associated with mortality–no other individual comorbid diagnosis provided additional information. The VACO Index discriminated mortality in development (AUC = 0.79, 95% CI: 0.77–0.81), and in early (AUC = 0.81 95% CI: 0.78–0.83) and late (AUC = 0.84, 95% CI: 0.78–0.86) validation. The VACO Index allows personalized estimates of 30-day mortality after COVID-19 infection. For example, among those aged 60–64 years, overall mortality was estimated at 9% (95% CI: 6–11%). The Index further discriminated risk in this age stratum from 4% (95% CI: 3–7%) to 21% (95% CI: 12–31%), depending on sex and comorbid disease. Conclusion Prior to infection, demographics and comorbid conditions can discriminate COVID-19 mortality risk overall and within age strata. The VACO Index reproducibly identified individuals at substantial risk of COVID-19 mortality who might consider continuing social distancing, despite relaxed state and local guidelines.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Multi-Variable Parametric Analysis of Prototype Building Energy Performance Using Current and Future Weather Scenarios For Data-Driven Market Transformation Support

This project aimed to develop a public building simulation data set that may be used to inform building code development and guidelines for building innovation. The data set consists of several common building types and many representative locations across the United States. A parametric design of building properties was developed to create a range of building energy models that represent common building design decisions with a particular focus on fenestration options. The US Department of Energy prototype building energy models were altered according to a parametric building design and simulated using both current weather data and future weather estimates derived from global climate models. The resulting data set allows for pertinent exploration of building design parameters, including fenestration, within different environments across the United States in the broader context of climate change.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Terrestrial laser scanning data (Levels 0 and 1) for Pasoh, Malaysia, Sep 2024

This data package contains data from terrestrial laser scanning (TLS) at the Pasoh Forest Reserve, Malaysia. The Pasoh Forest Reserve is a facility of the Forest Research Institute Malaysia, and contains evergreen lowland dipterocarp forest. The Next-Generation Ecosystem Experiments Tropics (NGEE-Tropics) study areas at Pasoh were established to study how different species respond to climatic variation and soil water availability. Two study areas were chosen representing different topography and species. The TLS data archived here were collected to provide detailed, three-dimensional information about forest structure. Specifically, data were collected to allow tree-level characterization of woody structure and leaf area for 12 focal trees with FloraPulse and sap flux sensors, facilitating estimation of woody biomass and leaf area to allow upscaling of water content and transpiration data to the tree-level. Scan positions were not selected to provide consistent data for non-focal trees with the study areas. This data package contains the following data: - High-level files document further details of the campaign and data package: 1_CampaignSummary.csv provides details about the campaign and study site, 2_ScanAreasDetail.csv provides details about each separate scan area (groups of scans post-processed into a single point cloud), 3_TerrestrialLidarSensor.csv provides further technical details about the Riegl VZ-400i TLS sensor, TLS_CSV_dd.csv is a CSV Data Dictionary providing information about the fields in CSV files following the ESS-DIVE CSV File Formatting Guidelines Reporting Format, TLS_flmd.csv is a File Level Metadata file providing information about each file in the data package following the ESS-DIVE File Level Metadata Reporting Format, and README.txt is a text file describing the overall project and file structure. - Level 0 data are the raw data (.PROJ folders) as recorded by the Riegl VZ-400i TLS instrument before scan co-registration and post-processing with the Riegl's proprietary RiSCAN PRO software, which requires a license. - Level 1 data contain post-processed, co-registered data from each scan area. The "PointClouds" folder for each scan area contains a .las file with 1 cm resolution point cloud data exported from RiSCAN PRO. These are the main files likely to be of interest to most users and can be further processed with any software capable of manipulating .las files (e.g. Python, R CloudCompare). The "Project Information" folder contains log files from post-processing in RiSCAN PRO that may be of interest to users who want to see detailed records of post-processing, including all PDF reports generated by RiSCAN PRO. The "ScanPositions" folder contains information about the final position of all TLS scans, after post-processing, in multiple formats. The file ScanPositions_*.csv provides final geo-referenced scan positions, and the file SOP_backup_*.csv can be used in RiSCAN PRO to restore the co-registered scan positions if users wish to re-process raw data (Level 0 .PROJ folders) with RiSCAN PRO software (e.g., subsample to a different resolution, exclude a certain scan position, or apply different filters on reflectance or deviation values) without redoing time-consuming co-registration steps.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Scoping review of Culex mosquito life history trait heterogeneity in response to temperature

Abstract Background Mosquitoes in the genus Culex are primary vectors in the US for West Nile virus (WNV) and other arboviruses. Climatic drivers such as temperature have differential effects on species-specific changes in mosquito range, distribution, and abundance, posing challenges for population modeling, disease forecasting, and subsequent public health decisions. Understanding these differences in underlying biological dynamics is crucial in the face of climate change. Methods We collected empirical data on thermal response for immature development rate, egg viability, oviposition, survival to adulthood, and adult lifespan for Culex pipiens, Cx. quinquefasciatus, Cx. tarsalis , and Cx. restuans from existing literature according to the PRISMA scoping review guidelines. Results We observed linear relationships with temperature for development rate and lifespan, and nonlinear relationships for survival and egg viability, with underlying variation between species. Optimal ranges and critical minima and maxima also appeared varied. To illustrate how model output can change with experimental input data from individual Culex species, we applied a modified equation for temperature-dependent mosquito type reproduction number for endemic spread of WNV among mosquitoes and observed different effects. Conclusions Current models often input theoretical parameters estimated from a single vector species; we show the need to implement the real-world heterogeneity in thermal response between species and present a useful data resource for researchers working toward that goal. Graphical Abstract

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Current and future federal and state sampling guidance for per- and polyfluoroalkyl substances in environmental matrices

Per- and polyfluoroalkyl substances (PFAS) are a class of emerging contaminants composed of an estimated 5000 to 10,000 human-made, fluorinated, organic chemicals. Due to the complexity of PFAS, the need for multiple environmental matrix considerations and the absence of a promulgated federal standard for environmental sampling and analysis, U.S. states have begun developing health-based regulatory and/or guidance values for a limited number of PFAS in environmental matrices. As there is a growing body of science to inform PFAS sampling guidance standard development, it is important to understand which U.S. states are implementing sampling guidelines and how they plan to handle emerging PFAS. This critical review discusses the current and impending federal and state sampling guidelines for PFAS in environmental matrices, the data gaps surrounding PFAS sampling guidance in U.S. states, and the future impacts of impending guidance documents and regulations. Ten federal guidance documents are available for PFAS sampling guidance and analysis. The maximum number of PFAS covered in these guidance documents is 25 analytes spanning across 8 unique media. While the EPA has developed several different sampling and analytical guidelines for PFAS, there is no formal regulation of PFAS or requirements of states to enforce these guidelines. Consequently, only 31 states have informally adopted sampling guidelines, while the other 19 states have no guidance documentation in place for PFAS. The introduction of new PFAS sampling guidelines by the EPA, as well as updated analytical guidelines that target more PFAS or total organofluoride, is expected to continuously shift the landscape of federal and state guidance for PFAS sampling moving forward.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Electricity consumption variation versus economic structure during COVID-19 on metropolitan statistical areas in the US

The outbreak of novel coronavirus disease (COVID-19) has resulted in changes in productivity and daily life patterns, and as a result electricity consumption (EC) has also shifted. In this paper, we construct estimates of EC changes at the metropolitan level across the continental U.S., including total EC and residential EC during the initial two months of the pandemic. The total and residential data on the state level were broken down into the county level, and then metropolitan level EC estimates were aggregated from the counties included in each metropolitan statistical area (MSA). This work shows that the reduction in total EC is related to the shares of certain industries in an MSA, whereas regardless of the incidence level or economic structure, the residential sector shows a trend of increasing EC across the continental U.S. Since the MSAs account for 86% of the total population and 87% of the total EC of the continental U.S., the analytical result in this paper can provide important guidelines for future social-economic crises.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Revisiting the guidelines for ending isolation for COVID-19 patients

Since the start of the COVID-19 pandemic, two mainstream guidelines for defining when to end the isolation of SARS-CoV-2-infected individuals have been in use: the one-size-fits-all approach (i.e. patients are isolated for a fixed number of days) and the personalized approach (i.e. based on repeated testing of isolated patients). We use a mathematical framework to model within-host viral dynamics and test different criteria for ending isolation. By considering a fixed time of 10 days since symptom onset as the criterion for ending isolation, we estimated that the risk of releasing an individual who is still infectious is low (0–6.6%). However, this policy entails lengthy unnecessary isolations (4.8–8.3 days). In contrast, by using a personalized strategy, similar low risks can be reached with shorter prolonged isolations. The obtained findings provide a scientific rationale for policies on ending the isolation of SARS-CoV-2-infected individuals.

Jeong, Yong Dam↗

DOE OSTI2026

A mathematical design framework for membrane pre-concentration in energy-efficient recovery of fermentation products

Due to the dilute nature of products manufactured via fermentation and cell-free bioprocessing, dewatering is a common unit operation in downstream processing (DSP) for bioproduct recovery, but it is typically energy intensive. To improve DSP energy efficiency for bio-based small molecules, integrating high-pressure membrane pre-concentration is a promising process option. However, this approach is typically constrained by a tradeoff between concentration factor (CF) and product recovery (PR), namely increasing the CF typically results in greater product loss, and vice versa. Here we developed a model that enables process design guidelines to: (i) identify scenarios in which the additional energy consumption and product loss from membrane pre-concentration are justified for use in DSP, and (ii) determine the optimal CF that minimizes process specific energy consumption. We compared the energy consumption of high-pressure membrane-integrated processes to evaporation-only processes and applied the model to an experimental case study for the separation and purification of butyric acid from Clostridium tyrobutyricum fermentation using an in situ product recovery (ISPR) process. The model estimated that integrating a tangential-flow reverse osmosis (RO) pre-concentration unit could reduce process energy consumption up to 45%. The use of advanced membrane pre-concentration technologies, such as negative rejection membranes and organic solvent reverse osmosis (OSRO), have the potential to further reduce the overall process specific energy consumption up to 96%, projected based on modeling. Overall, membrane pre-concentration, especially when strategically integrated prior to an evaporation step with optimized process conditions, holds significant potential for improving DSP energy efficiency, particularly in applications requiring substantial solvent removal for product recovery from dilute mixtures.

09 BIOMASS FUELS↗

DOE OSTI2024

Woody Feedstock 2022 State of Technology Report

The U.S. Department of Energy promotes production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement in this mission, Idaho National Laboratory completes an annual SOT report for n th -plant and 1 st -plant woody biomass feedstock logistics. The purpose of the SOT is to provide the status of feedstock supply system technology development for woody biomass to biofuels relative to technical targets and cost goals from specific design cases, based on data and experimental results. Conventional feedstock supply systems need to be modified to meet the demands of conversion pathways, specifically to have the ability to adjust the quality of the raw biomass materials. Advanced systems incorporate innovative methods of material handling, preprocessing and supply chain configuration. In advanced designs, variability of the raw biomass can be reduced to produce feedstocks of a uniform format, moving toward biomass commoditization. Against this backdrop, the 2022 Woody SOT for low-ash woody feedstocks utilizes feedstock fractionation by incorporating technologies that can separate the biomass into its anatomical fractions (wood, bark, needle, and extrinsic ash) to reduce impurities and attempt to maximize the retention of usable fractions that satisfy downstream quality considerations. By using a series of air classification steps, this strategy can reduce the extrinsic ash in forest residues, separate out a majority of the incoming needles (which can be supplied to alternate markets), and maximize the retention of whitewood in the usable fraction. The fractionated forest residues are then mixed with clean-pine chips in a 50-50 blend to prepare the feedstock for the desired conversion pathway. The n th -plant analysis estimated the delivered cost for the feedstock at $\$$69.23/dry ton (2016$\$$) which represents a $\$$6.64/dry ton decrease compared to the cost estimate of the 2021 Woody SOT supply system for low-ash woody feedstocks. The quality requirements in the 2022 Woody SOT were identical to those of the 2021 Woody SOT at = 1.00 wt % ash and = 50.51 wt% carbon. The cost savings derive primarily from reductions in dry matter losses during air classification. The GHG emissions for the n th -plant analysis were estimated at 178.39 kg CO2e/dry ton compared to 178.71 kg CO 2 e/dry ton in the 2021 Woody SOT, a decrease of 0.32 kg CO2e/dry ton. The small change stems from an increase in emissions attributed to preprocessing and slightly larger savings in emissions from transportation. In the 1 st -plant analysis of the 2022 Woody SOT system, the average throughput was estimated to be approximately 2,128 dry tons/day or 96.51% of the name plate capacity. During the simulation the daily throughput ranged from 1,090 dry tons/day to 2,200 dry tons/day, or 49.43% to 99.75% of the daily nameplate capacity. After the year of operation 722,403 tons of processed feedstock were produced in total without regard to quality considerations (99.64% of the annual nameplate capacity). The variability in throughput was primarily caused by equipment failures in the system. Regular failures, downtime caused by routine maintenance per manufacturer guidelines, contributed to a majority 62.50% of failures and 62.60% of downtime. Failures due to wear were the other cause of disruption within the system, impacting the rotary shear and orbital screen and accounting for 37.50% of the failures and 37.40% of the total downtime. Ultimately the system was on stream for 87.84% during the simulation period, which is only 2.16 percentage points below the nth-plant assumption for on-stream time. The production cost of the system averaged $\$$71.66/dry ton. The costs ranged from a minimum of $\$$71.23/dry ton to a maximum of $\$$2,115.30/dry ton. When dry matter losses (disposed low-quality fractions as well as other losses such as in grinders) were considered the costs increased to an average of $\$$75.11/dry ton with a minimum of $\$$74.69/dry ton and a maximum of $\$$2,136.86/dry ton...

09 BIOMASS FUELS↗

DOE OSTI2022

Effects of random forest modeling decisions on biogeochemical time series predictions

Abstract Random forests (RF) are an increasingly popular machine learning approach used to model biogeochemical processes in the Earth system. While RF models are robust to many assumptions that complicate deterministic models, there are several important parameterization decisions for appropriate use and optimal model fit. We explored the role that parameter decisions, including training/testing data splitting strategies, variable selection, and hyperparameters play on RF goodness‐of‐fit by constructing models using 1296 unique parameter combinations to predict concentrations of nitrate, a key nutrient for biogeochemical cycling in aquatic ecosystems. Models were built on long‐term, publicly available water quality and meteorology time series collected by the National Estuarine Research Reserve monitoring network for two contrasting ecosystems representing freshwater and brackish estuaries. We found that accounting for temporal dependence when splitting data into training and testing subsets was key for avoiding over‐estimation of model predictive power. In addition, variable selection, the ratio of training to testing data, and to a lesser degree, variables per split and number of trees, were significant parameters for optimizing RF goodness‐of‐fit. We also explored how model parameter decisions influenced interpretation of the relative importance of predictors to the model, and model predictor‐dependent variable relationships, with results suggesting that both data structure and model parameterization influence these factors. Because much of the current RF literature is written for the computational and statistical science communities, the primary goal of this study is to provide guidelines for aquatic scientists new to machine learning to apply RF techniques appropriately to aquatic biogeochemical datasets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2020

Tribal Renewable Energy: Bishop Paiute Tribe, Residential Solar Program Phase II (Final Report)

The project consisted of the design, installation, inspection, and interconnection of 35 grid-tied, solar electric systems, totaled 123 kW rated capacity, on qualified existing low-income single-family homes located within the Bishop Paiute Reservation, which provide at least 65-79% savings in displaced electricity. Tribal job trainees were hired for each installation, gaining valuable job experience. Additionally, each homeowner was educated on energy efficiency and renewable energy. The overall project goal was to deploy clean energy systems in order to achieve the Bishop Paiute Tribe’s long-term goals of energy self-sufficiency, environmental protection, and better lives for our Tribal members and community. The energy displaced was 82.4 percent of total electricity used (exceeding estimated 65-79%)or over 170,000 kWh/year generating approximately $1.29 million worth of power for low-income families over their lifespans, while eliminating an estimated 2,640 tons of greenhouse gas emissions. This reduction makes a significant difference on the reservation giving more families money to spend on other essential items, while reducing their carbon footprint in this beautiful mountain community. Also, implementing these 35 systems provided a approximately 132 hours of paid solar installation work for tribal members. Training and good paying jobs are scarce on the reservation and solar is the fastest growing industry in CA and this training offered our members a real chance to learn and then get paid. The program is very significant to the family who qualified, as most families fall below the federal poverty guidelines, and many are living paycheck to paycheck. Overall, the triple impact of the Bishop Paiute Tribe Residential Solar Program Phase II— affordable energy for low-income families, on-site clean energy production, and hands-on solar installation jobs for local workers—these all help build the Tribe’s energy, economic, environmental, and social self-sufficiency and sovereignty amongst the most needy on the Reservation

14 SOLAR ENERGY↗

DOE OSTI2024

Methodological guidelines on concrete degradation based on predictive models and the release of MOSAIC for industry use

In light-water reactors (LWRs), the concrete biological shield (CBS) designates the concrete structure directly facing the reactor pressure vessel (RPV). The primary function of the CBS is to protect equipment and personnel from the neutron and gamma radiation exiting the RPV. Most CBSs in operation in the United States also provide structural support to the RPV. Based on a long-term structural analysis, the structural integrity of the CBS to transfer the in-service passive load to the foundation, dynamic loading during an earthquake, and thermal loading during a loss-of-coolant accident (LOCA) must be maintained. Based on test reactor data for accelerated conditions, the mechanical properties of irradiated concrete are affected when exposed to neutron fluence higher than ≈ 10 19 n.cm -2 (E > 0.1 MeV). At 80 years of operation, the fluence estimates at the surface of the CBS range between 1×10 19 and 7×10 19 n.cm -2 (E > 0.1 MeV): the fluence value depends mostly on the design. Although fluence attenuates through the CBS by virtue of the shielding properties of concrete, about 5% to 10% of the CBS wall depth is affected by high fluence. The main degradation mechanism is the so-called radiation-induced volumetric expansion (RIVE) caused by the amorphization of aggregate-forming minerals. RIVE causes large deformation of up to 18% in pure quartz, and it also reduces the structural properties of concrete. The structural effects of long-term exposure to fast neutron irradiation in LWRs was studied in the report entitled “Assessment of the Effect of the Irradiation-Induced Degradation on the In-Service Structural Performance of the Concrete Biological Shields” (M3LW-23OR0403013). This report illustrates that (1) irradiation-induced damage expands further than the region subject to a cumulative fluence of 10 19 n.cm -2 (E > 0.1 MeV) to reach the steel reinforcement location, and (2) the irradiation-induced damage region shows no substantial residual bearing capacity and should not be accounted for in the integrity assessment of the CBS.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗