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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Investigation of Thermolytic Hydrogen Generation Rate in Tank 44 Dissolved Saltcake Samples

Saltcake core samples collected from Tank 44 in 2006 were dissolved to provide material for HGR measurements applicable to F-Area dissolved saltcake material. Additionally, characterization was performed on the Tank 44 saltcake material. The following are key results from the Tank 44 saltcake characterization. The Tank 44 Upper Saltcake Composite, corresponding to the 171 to 285 inch tank level, contained by mass approximately 69% sodium nitrate, 11% sodium carbonate, 8% sodium nitrite, smaller amounts of other salts and components, and 9% unquantified (which includes water, water of hydration, oxygen/hydrogen content of oxides and hydroxides, and uncertainty). The Tank 44 Lower Saltcake Composite, corresponding to the 76 to 114 inch tank level, contained by mass approximately 49% sodium carbonate, 18% sodium nitrate, smaller amounts of other salts, at least 8% sludge, and 9% unquantified (see above). The dissolved saltcake contained free hydroxide less than quantifiable (<0.01 M) due to the limited quantity of material that could be removed from the Shielded Cells based on the sample radioactivity. Measurement by pH paper provided an approximate pH of 12. The following are key results from the Tank 44 HGR testing. During boiling at 106.7 °C, HGR for Tank 44 dissolved saltcake without added glycolate was 7.2×10 -8 ft 3 h -1 gal -1 . During boiling at 106.9 °C, HGR for Tank 44 dissolved saltcake with 1000 mg/L of added glycolate was 8.2×10 -8 ft 3 h -1 gal -1 . For the test without added glycolate, the first several HGR measurements at 70, 85, and 100 °C gave indication of the release of dissolved hydrogen and should not be used to represent the sustained thermolytic HGR for those temperatures. The measurements at boiling are the best representation of thermolysis in this testing. Carbon dioxide was observed at concentrations up to 6 vol% in the flow-system offgas for the test at boiling. Methane generation was observed at 100 °C and boiling. Methane concentration in the total gas generated during testing remained well below the lower flammability limit for methane in air. The addition of 1000 mg/L of glycolate did not have a significant impact on the hydrogen generation rates measured during this testing. The low hydroxide concentration in the Tank 44 dissolved saltcake likely influenced the relatively low thermolytic HGR and high carbon dioxide release observations in this testing. Based on the observation that methane was generated or released upon heating SRS radioactive Tank 44 waste samples to 100 °C and above, we recommend gaining a greater understanding of the cause and mechanism of its generation. First, the applicable literature should be reviewed to reveal the thermolytic methane generation mechanisms of possible methane generating species in the SRS CSTF. If warranted, a plan should be developed for simulant tests with methylated siloxanes and other applicable compounds in order to gain a better mechanistic understanding of methane generation in the SRS CSTF.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

An Overview of Argonne’s Advanced Mobility Technology Laboratory Vehicle Systems Instrumentation and Evaluation Methodology

This report will provide a general overview of the testing facilities, research equipment, and general testing methodologies Argonne utilizes to conduct vehicle technology evaluations of advanced technology research vehicles. Data is captured from vehicle evaluations that provide powertrain operation and corresponding energy consumption based on a combination of in-depth instrumentation and focused testing. This resulting dataset of hundreds of time-resolved vehicle signals provide a basis for direct analysis and model validation of vehicle specific technologies. Argonne has attempted to standardize the approach to vehicle instrumentation and testing at Argonne, but it should be noted that each vehicle, and the corresponding assessment, remains unique. It is suggested that the reader reference vehicle specific testing reports for an overview of the unique aspects for each test vehicle. Additionally, the datasets for research vehicles are made publicly available through the Advanced Mobility Technology Laboratory’s Downloadable Driving Database (D3) at www.anl.gov/d3.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Structural Health Monitoring of Microreactor Safety Systems Using Convolutional Neural Networks

Microreactors, a class of modular reactors with net power output of less than 20 MWth, have innovative applications in nuclear and nonnuclear industries due to their portability, reliability, resilience, and high capacity factors. In order to operate microreactors on a wider scale, it is essential to bring down maintenance life-cycle costs while ensuring the integrity of operating such systems. Autonomous operations in microreactors using augmented digital-twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. Structural health monitoring (SHM) is a key component of nuclear DT frameworks. Artificial neural networks can be beneficial to detect degradation in the nuclear safety systems, such as piping equipment systems, by monitoring the sensor data obtained from the plant and its corresponding structures, systems and components. In this report, an SHM methodology is presented which uses convolutional neural networks to determine degraded locations and their corresponding degradation-severity levels at various locations of nuclear piping equipment systems. A simple pipe system, subjected to seismic loads, is selected to design the post-hazard SHM framework. The effectiveness of the proposed SHM methodology is demonstrated by obtaining high accuracy in detecting degraded locations as well as the severity levels.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Graduate Fellowships [Slides]

All fellowship awards and their corresponding internships must be approved by LANL management. Awards and corresponding internship assignments are not guaranteed, even if student has management support.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Evaluation of Electrical Resistivity Tomography to Monitor the Transport of Past Releases Beneath Tank Farms

Underground storage tanks at the Hanford Site, in southeastern Washington State, hold radioactive waste generated from four decades of plutonium production. The 149 single-shell tanks and the 28 double-shell tanks have all exceeded their initial design life of approximately 25 years. At least 67 tanks are assumed to have leaked in the past, resulting in radioactive releases into the vadose zone. Gamma ray logging within dry monitoring wells is currently the primary method for tracking the migration of leaked tank waste through the vadose zone. While this approach provides an accurate assessment of radioactive contamination, that information is only provided near (within ~1m) the borehole, leaving most of the vadose zone unmonitored, particularly the important region directly beneath the tank. This report describes a numerical study that investigates the feasibility and performance of time-lapse 3D electrical resistivity tomography (ERT) for long-term monitoring of a hypothetical tank waste location and migration through the vadose zone. ERT is a method of remotely imaging the bulk electrical conductivity (EC) of the subsurface, which is significantly impacted by the presence of conductive solid and liquid tank waste. The release of liquid tank wastes increases subsurface fluid conductivity and saturation over time, creating a target to use time-lapse ERT for long-term monitoring. Although the presence of metallic infrastructure can cause ERT interference, recent advancements in ERT data processing enable the deleterious effects of buried metallic infrastructure (e.g. pipes, wellbore casings, tanks) to be removed to better determine the liquid tank waste migration over time. Three hypothetical realistic scenarios were simulated in the ERT evaluation. The first two scenarios assume the same leak amount and rate (i.e., between 1/1/1951 and 12/31/1951 at the rate of 347 m 3 per year) but different leaky tanks. Scenario 1 assumes leaks under tank B-102, which is located on the edge of the B-tank farm and surrounded by a few metallic infrastructure including cased pipes/wells/tanks. Scenario 2 assumes leaks under tank B-108, which is located near the center of the B-tank farm and surrounded by larger amount of metallic infrastructure than B-102. Scenario 3 assumes the same metallic infrastructure as B-102, with a more recent contaminant leak that was simulated to have occurred between 1/1/2018 and 12/31/2023 at a rate of 1.89 m 3 per year. The leak time in Scenarios 1 and 2 corresponds to a historical overfill event in 1951 and Scenario 3 corresponds to a recent found tank leak in 2019. In each scenario, a “true” bulk EC model vs. time reflecting contaminant migration was generated. ERT data was simulated from these “true” bulk EC models and a time-lapse ERT inversion produced “imaged” bulk EC vs. time. Three electrode configurations in two, four and eight boreholes surrounding the leak tank were used in the ERT simulations in each scenario. These borehole configurations were considered logistically feasible and cost-effective for monitoring. The hypothetical ERT boreholes are assumed to have non-metallic casing. By comparing the “imaged” bulk EC with the “true” bulk EC, it was demonstrated that the three configurations of wells used (two, four, and eight wells) were able to successfully monitor the migration of tank leaks through the vadose zone, with bulk EC resolution increasing with the number of down borehole ERT arrays for all scenarios. Therefore, the use of eight boreholes to perform ERT monitoring beneath the tanks provided the best spatiotemporal information.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Big Data For Operation and Maintenance Cost Reduction

The purpose of this research is to develop a first-of-a-kind framework for integrating Big Data capability into the daily activities of our current fleet of nuclear power plants. Big Data is traditionally defined as data sets with high volume, velocity, and heterogeneity, and the existing Big Data analytics capabilities are now widely popular in fields such as finance, weather, e-commerce, healthcare and sports. In the nuclear industry, while the volume and velocity of data may present computational challenges for existing analytics capabilities, data heterogeneity are seen to present the major challenge. This research project mainly focuses on incorporating the wide range of data heterogeneities in nuclear power plants into an integrated Big Data Analytics capability. The primary end-product of this project is a Big Data framework that is capable of dealing with the large volume and heterogeneity of the data found in nuclear power plants to extract timely and valuable information on equipment performance. The framework can generate system insights that are actionable relations between measurable impacts and the corresponding maintenance action plans and enable optimization of plant operation and maintenance based on the extracted information. The developed framework is capable of handling heterogeneous data including both image data and time-series sensor data. Specifically, this developed framework includes the following components. The first component is an overarching maintenance ontology which includes system insights required by maintenance optimization. The maintenance ontology interacts with other components in the developed framework. The second component handles Piping & Instrumentation Diagram (P&ID) data. It can be used to extract system components and their relations automatically from the P&IDs. This extracted information is stored in the first component, i.e., maintenance ontology, and is also used as input to the third component, i.e., a tool for generating the fault tree for the corresponding system. The generated fault tree in turn is stored in the ontology for assessing risk that is used as a criterion in maintenance policy optimization. The fourth component is a tool for inferring the parameters in the Markov degradation model for a nuclear system. It uses basic information from the ontology. The fifth component is a tool for assessing the degradation level using sensor measurement data, for example, pressure, flowrate. This tool can be used for determining corrective maintenance actions. The results obtained from components four and five are returned to the ontology. The sixth component of the framework is a tool for optimizing the maintenance policy for a nuclear system of interest. It takes certain basic information from the ontology, e.g., costs of maintenance actions and system failures, as input, and returns the optimal maintenance policy to the ontology. This tool can be used for determining predictive maintenance actions. A set of experiments have also been conducted to verify the algorithms developed in this project for nuclear system degradation monitoring. The experiments are based on four solenoid valves, similar to the ones used in nuclear power plants. The analyses based on the experimental data using two algorithms, i.e., the Randomized Window Decomposition (RWD) algorithm and the particle filtering algorithm, and the results are introduced in the report. The Big Data framework developed in this project can be used as a support tool in daily activities of plant operation and maintenance and will reduce current costs while maintaining or improving safety levels. Overall, the project will not only benefit existing reactors, however it will open new frontiers to realize the long overdue value of Big Data Analytics in the nuclear sphere.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Wood Decomposition: Understanding Processes Regulating Carbon Transfer to Soil Carbon Pools Using FACE Wood at Multiple Scales

Dead wood comprises approximately 20% of forest biomass. While it is widely recognized for its role in ecosystem functions relative to habitat and nutrient cycling, there are considerable uncertainties associated with its role in the forest carbon (C) cycle. The principal uncertainty is whether dead wood contributes to the soil C pool. With little supporting evidence, the presumption is that C in wood is respired back to the atmosphere through decomposition processes. The inability to attribute forest soil C to its source components has been a barrier to addressing this important forest carbon cycle question. The U.S. Dept. of Energy’s Free Air CO 2 Enrichment (FACE) experiment produced wood that has a distinct δ 13 C signature that can be used to trace its fate through organic matter decomposition processes. Using two hardwood species (aspen, birch) from the Rhinelander FACE site and pine from the Duke FACE site, the FACE Wood Decomposition Experiment (FWDE) was established in 2011 to provide the foundation for addressing wood decomposition and its associated processes at the continental scale. Plots were installed on nine experimental forests representing a range in bioclimatic forest conditions within the continental U.S. Each identical plot has logs of the three species incubating on the soil surface and pine logs suspended in the air, representing standing dead wood. Abiotic conditions are monitored continuously, and soil and forest conditions characterized. Logs on the sites have been measured every two years to assess decay. This project was initiated in 2016 with specific objectives to: (a) determine whether dead wood C contributes to the soil C pool, (b) assess the fungal community composition mediating the decay, (c) assess the interactions of termites with microbial decay, and (d) develop a mechanistic model to simulate wood decomposition. The principal field sampling was conducted in 2017; correspondingly, the results reported here represent six years of decomposition. Wood decomposition varied from 13 to 75 % loss across the bioclimatic gradient. Sites in the southeastern U.S. coastal plain and piedmont had fastest decomposition rates for each of the species. In contrast sites in the Rocky Mountains had the slowest decay rates, which were associated with cold temperatures. Standing deadwood decomposes at a much slower rate than logs on the soil surface; a response due to differences in wood moisture content. Wood carbon was measured in the mineral soil across each of the sites; accordingly, this is the first study to affirm that a portion of the wood C does become part of the soil C pool. Averaged across the FWDE sites, approximately 1.4% of the soil C in the upper 10 cm of the mineral soil directly beneath the log was derived from the deadwood; within 50 cm of the log, wood C accounted for an average of 2.1%. These results demonstrate that the contribution of deadwood to the soil C pool is a soil volume surrounding the log; and it’s likely a larger volume than what we sampled. We also hypothesized that there would be more wood C in the mineral soil when termites were present. However, that hypothesis wasn’t supported by measurements on the Santee Experimental Forest termite exclusion (SEF-TX) plots under FACE logs that incubated for 6 years. Across all the sites, the majority of the wood C was transferred to the atmosphere. Approximately 7,000 fungal species were sequenced. The fungal communities were different among each of the sites suggesting local colonization; interestingly, within sites the three species also exhibited differences in fungal community composition. White rot was the dominant fungal form in pine and hardwood, which yields less lignin in the wood residue. Brown rot occurred in approximately 20% of the pine and aspen logs, and it did not occur in birch logs. The occurrence of brown rot was only weakly correlated to site factors. We conducted two additional experiments to assess the effects of termites on wood decomposition and the associated interactions with the fungal community. Pine and birch logs were incubated on the Santee Experimental Forest termite exclusion plots in upland and riparian settings. There was no difference in decay between areas where termites were able colonize and plots where they had been excluded. Highly variable foraging by termites was a primary contributor the result. Correspondingly, fungal communities did not vary on logs incubated among the two treatments, but they did reflect site and species differences. The second experiment was conducted on each of the nine FWDE sites where pine and birch wood blocks were incubated for two years with and without a wrapping designed to exclude termites and other arthropods. The effects of termites on wood decomposition could be confirmed on two of the four sites known to have subterranean termites. On the two southeastern sites, termites contributed approximately 8% to mass loss in pine blocks over two years. A major factor clouding the role of termites in wood decomposition is the inability to assess the time, duration, and intensity of the foraging with the logs. Another uncertainty is related to whether a piece of wood will be foraged. A process-based wood decomposition model was developed representing biological processes mediated by fungi, bacteria, termites, beetles, as well as physical and chemical processes. The Coarse Wood Decomposition Model (CWDM) was parameterized based on a thorough review of the literature and it was validated using data from the FWDE. In the initial setup, the model considers softwoods and hardwoods groups, as opposed to individual species. Sensitivity analyses incorporating climate data from 89 locations in North America demonstrated that CWDM was stable from the tropics to the boreal zone. Model calibration and validation showed it to perform very well. The CWDM is poised for additional testing with subsequent FWDE data as well as other data sets, and it is available for incorporation into forest biogeochemical models or DOEs Earth System Modeling framework.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Automation of FARM from Alpha Phase to Beta Phase

Integrated energy systems (IES) combine different energy technologies in synergistic ways to achieve a more secure and economical energy supply. The RAVEN-based HYBRID framework and the RAVEN plugin for grid and capacity optimization (HERON) are used to find the optimal installed capacity and the optimal economical dispatch of each component of the IES, by respecting the limits on the production variables and the corresponding rates of variation (explicit constraints). Besides, there are other process variables whose evolution needs to be bounded to avoid damaging the components (e.g., condensers, heat exchangers, steam generators, etc.) or degrading the process efficiency (e.g., electrolysis in the hydrogen production process). To avoid violating these latter limits (implicit constraints), a proof-of-concept HERON validator based on Feasible Actuator Range Modifier (FARM-Alpha) was developed by Argonne National Laboratory in January 2021. This FARM-Alpha validator calculates the evolution of process variables on whom the implicit constraints are placed, and then provides feedback to HERON dispatcher to adjust the power setpoints of three IES components, i.e., Balance of Plant, Secondary Energy Source, and Thermal Energy Storage, so as to meet both the explicit and implicit constraints. FARM-Alpha was designed to assess the performance of FARM as a HERON validator only, i.e., the list of components and implicit operational constraints were hard-coded within the source code. The lack of flexibility of the corresponding software structure does not allow the deployment in production environment. This report describes the development and the implementation of an enhanced version of the FARM-based validator (FARM-Beta), which ensures more flexibility for the end user in modeling multiple IES configurations and scenarios. Several test cases of the power dispatch problem were then selected to demonstrate the capabilities offered by FARM-beta. The test cases illustrate the efficiency of the closed-loop optimization scheme and the capability to calculate set-point trajectories satisfying both explicit and implicit constraints.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Topics in String Theory, Quantum Field Theory And Quantum Gravity

This is the final technical report for DOE Award No. DE-SC0021528 - Topics in string theory, quantum field theory and quantum gravity. The goals of the research program were in three areas - understanding quantum gravity and the internal description of quantum systems using the gauge/gravity correspondence, aspects of entanglement in eld theory, gravity and string theory, and the characterization of the behavior of strongly interacting quantum eld theories. The primary results were on the microscopic understanding of the ER=EPR correspondence as a string duality, and on a matrix model description of the addition of end of the world branes and propagating matter to two dimensional Jackiw-Teitelboim gravity. The former led to the completion of the work shortly after the end of the reporting period and continuing work investigating the properties of the associated stringy black hole microstates. The latter led to the work, which also appeared shortly after the end of the reporting period, and a continuing project to nd a matrix model formulation of the eigenstate thermalization hypothesis and JT gravity with propagating matter.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Mechanisms of Retention and Transport of Fission Products in Virgin and Irradiated Nuclear Graphite

To fulfill the design and licensing requirements for advanced reactors, the U.S. Department of Energy (DOE) is specifically interested in targeted research to quantify the mechanisms of fission product transport and retention in graphitic grades that will likely be used in next-generation very/high temperature reactors, fluoride salt-cooled high temperature reactors and molten salt reactors. We have examined an integrated experimental-computational approach to determine the diffusivities of selected fission products – ruthenium (Ru) and silver (Ag) in five nuclear graphite grades – POCO AXF-5Q, POCO ZXF-5Q, PCEA, IG110, and NBG-18. Experimental investigation for diffusion of other fission products (iodine, cesium, and krypton) in graphite could not be carried out due to extreme evaporation due to high vapor pressure, despite several attempts incorporating various experimental schemes. Experimental investigation with irradiated graphite could not be carried out because necessary approval to transport irradiated graphite could not be obtained due to procedural reasons at ORNL and national policy change in UK. An effort was made to “produce” by ion sputtering, but it was discontinued because the outcome did not adequately represent irradiated graphite. Thin film and “cup-and-cap” methods were employed to deposit fission products, diffusion annealed were carried out in an argon atmosphere, and concentration profiles were determined by dynamic secondary ion mass spectroscopy depth profiling technique. Regardless of graphite type, anomalous diffusion behavior of Ru (i.e., lower than expected based on Arrhenius temperature dependence) at an intermediate temperature, around 600 to 700 °C, was observed. Diffusion behavior of silver on the other hand agreed well to the Arrhenius temperature dependence although the excessive evaporation of silver hampered the accurate determination of diffusivities at temperature higher than 900 °C. In general, silver was observed to diffuse faster than Ru in the temperature range compared from 500 to 900 °C, had consistently had higher pre-exponential factor corresponding to higher jump frequency. To understand the abnormal diffusional behavior of Ru in graphite in intermediate temperature range, a detailed microstructural analysis was carried out. Clustering of Ru into particles on the surface of graphite was observed around 700 °C corresponding to de-wetting and spheroidization. This change would influence the boundary condition of diffusion, although features of clustering was much smaller (<1 mm) than the SIMS raster size (120 x 120 mm). Raman spectroscopy also demonstrated that Raman peak intensity for Ru decreased significantly for samples annealed higher than 700 °C and presence of Ru in graphite altered the crystallinity of graphite. To further elucidate the abnormal diffusion behavior observed in intermediate temperature investigated, the vibrational properties of graphene, bilayer graphene and Kr atoms in bilayer graphene were investigated with molecular dynamics. Krypton, which is a fission product, was introduced between a bilayer graphene system to evaluate (i) whether the phonon dispersion curves change with high temperature in bilayer graphene (ii) whether the addition of Kr atoms between the bilayer introduces any significant change in the vibrational properties, and (iii) whether any change in the vibrational properties can be correlated to the diffusive behavior of Kr atoms. The results show that the Kr atoms indeed exhibit an anomalous diffusive behavior at 550°C. Phonon analysis shows that a few phonon frequencies are enhanced as much as 20 to 30 % with the addition of just 0.2 at.% Kr. So, this may explain the unexpected self-diffusivity of fission products.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Current and Future Work on Wound Contamination by Alpha Emitters at Los Alamos [Slides]

Our team calculated ‘clinical decision levels’ at which treatment may be warranted (i.e., bioassay and wound measurements which correspond to specific dose thresholds). The actual risk to the patient corresponding to this dose threshold is uncertain – the CED may not tell the whole story. We recently reviewed LANL medical records to characterize adverse events associated with treatment. Future work should include medical records from other institutions and a review and analysis of data to quantify benefit of treatment with a focus on the question, "What factors predict whether treatment will be beneficial?"

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Bay Area Regional Energy: Network Integrated Commercial Retrofits (BRICR) Project. Final Report

The BRICR project applied large-scale building energy modeling concepts with the aim of reducing the cost of energy efficiency targeting, design, and project development, and measurement of energy savings for energy efficiency programs implemented by local governments that serve small and medium commercial buildings (SMB). The project leveraged the services and resources of existing local government energy programs serving disadvantaged and hard-to-reach SMB customers. In contrast to programs run by utilities, local government programs generally do not have direct access to energy billing records for an entire class of customers in a geographic area, which prior research demonstrated useful for large-scale building energy model baseline development and calibration. , However, local governments are rich in public records that offer important clues about physical attributes and uses that, along with behavior, determine energy use. Relying only on public records, BRICR demonstrated development of credible baseline energy models for 3,792 office, retail, and hotel buildings. Publicly disclosed annual energy use data from a local energy benchmarking program and anonymized data from the Building Performance Database, the nation’s largest dataset about energy-related characteristics of buildings, were utilized to validate and calibrate energy models via an innovative method comparing distributions of energy intensity by fuel type for portfolios of buildings of similar size, vintage, and use. Portfolio calibration does not provide certainty that an energy model fits an individual building; the method is useful when billing data is not accessible – a common situation for researchers, energy service providers and ESCOs, local governments, and any party other than a utility. A software component was developed, the BRICR gem, which automates simulation when relevant data is added or edited by the user to a file saved in the standardized BuildingSync XML schema for energy audit data. The component was demonstrated as a simplified means to generate a mass of energy models corresponding to public records containing basic attributes such as building scale, location, use, year built, and aspect ratio in combination with building energy code prototype data corresponding to use and vintage. The component was also demonstrated as a simplified means to automate energy simulation when attributes are revised; the intention was to enable iterative improvement of the baseline model and energy savings estimates for common energy conservation measures as users revise relevant attributes based on their observations. In the context of institutional change and uncertainty for the participating local government energy programs, 13 whole building retrofits were completed. Impacts were measured by applying the CalTRACK2.0 methods to standardize measurement of normalized metered energy consumption. The GRIDMeter methods of stratified sampling and individual load shape analysis were applied to adjust for impacts of the effect of COVID-19 on retrofitted buildings in the context of all local buildings of similar size and use. Excluding impacts of the pandemic, retrofitted buildings demonstrated between 1.6% and 25.1% reduction in energy use. The project contributed use cases and feedback that helped inform evolution of the software tools and data formats that were combined for the first time in the BRICR project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Submersible Multistage Centrifugal Pump for Versatile Test Reactor Cartridge Test Loop

Submersible multistage centrifugal pumps are ideal for pumping in narrow confined spaces and achieving necessary head pressures and flow rates. Once a diameter is determined then manipulation of the number of stages and motor speed are all that are required to meet desired flow conditions. The Versatile Test Reactor (VTR) closed loop cartridge systems will need forced convection cooling independent of the main reactor. A multistage centrifugal pump can meet the necessary flow rates and pumping pressures while minimizing space taken. The pump considered for this work was based off a deep well submersible pump, a variation of a multistage centrifugal pump. We experimented with two pump sizes, 5 cm (2 inch) and 7.5 cm (3 inch) diameters. These diameters were chosen to fit into the inner diameter of standard 5 and 7.5 (2 and 3 inch) Schedule 40 pipe, respectively. This made the design for the test loop both simpler and less expensive as the need for an engineered pump housing was eliminated. Initial test cartridge planning indicated space for only a 5 cm (2 inch) diameter pump, though early testing of this size showed the need for an abnormally high-speed and high-power motor. Fine tuning of the cartridge design allowed a pump size increase to 7.5 cm (3 inches), which was the pump size most extensively tested in this work. The test loop is composed of various sizes of PVC and aluminum piping components in a loop configuration. The pump is driven by a Pittman 250 W (1/3 horsepower) electric motor with maximum speed of 3,450 RPM. Testing consisted of running the pump at a constant motor speed while varying a control valve to restrict flow through the loop, with differential pressure and flow rate recorded. This was done for one and two stage configurations for the 5 cm (2 inch) diameter impeller design and one, two, and three stage configurations for the 7.5 cm (3 inch diameter) impeller design, respectively. Due to pumping power requirements, two and three stage 7.5 cm (3 inch) diameter impeller testing at higher flowrates lowered the motor speed substantially. In regions where motor speed could not be maintained constant, the data were discarded. The test loop was also reconfigured to allow for the pump to be tested for pressure drop in a stalled or inoperable (0 RPM) flow condition. Demonstration of adequate natural convection cooling of the test cartridge fuel type is necessary under accident conditions, and this will depend upon the flow resistance through the impeller assembly when the pump is not operating. Thus, accurate knowledge of the effective impeller assembly loss coefficient is important for safety evaluations. The test loop was modified to provide water inlet and outlets on either side of the pump impeller stack, and a metered flow of lab water was provided in order to measure the pressure drop across the cartridges as a function of flowrate. Data from the pump head curve testing developed as part of this work and supported by analysis using pump head affinity laws indicates that a three stage 7.5 cm (3 inch) pump impeller design will meet target requirements for coolant flow within the VTR cartridge sodium cartridge at full power conditions [1] with margin; this corresponds to a flowrate of 45 l/min (12 gpm) at a pressure drop of 6.1 m (20 feet) of water head. The results of the pressure loss measurements across the impeller assembly when the pump is stationary (i.e., at 0 RPM) indicate that the pressure loss coefficient is 0.921 for a two impeller stack configuration; this value is calculated based on the flow velocity through the minimum available flow area within a single stage of the impeller which corresponds to 1.4 cm2.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Bayesian Optimization Framework for Imperfect Data or Models

Conventional Bayesian optimization methods implicitly assume that the data and model being optimized are “perfect.” This assumption leads to inaccurate posterior probability distribution functions (PDFs) when applied to “imperfect” data or models. The new Bayesian optimization framework presented in this report provides a way to parameterize the effect of imperfections usually encountered in a prior PDF of generalized data or a model on the posterior PDF. The effects of imperfections are parameterized by a set of constraints imposed on the posterior expectation values of deviations between the data and the model and on their covariance matrix elements. A particular set of values for these constraints conveys an evaluator’s best estimate of the effect of imperfections on the corresponding posterior expectation values. When a prior PDF of generalized data is assumed to be normal, an expression for a posterior PDF satisfying an arbitrary set of constraints is derived analytically for linear models. An analogous iterative algorithm is given for nonlinear models. The corresponding posterior PDF should be used to estimate any posterior expectation values in the presence of imperfections parameterized by that set of constraints. A posterior PDF of a conventional Bayesian optimization method is recovered analytically when all evaluator-specified constraints are set to zero (i.e., in the absence of any imperfections). The analytical expressions derived in this report for normal PDFs and linear models were verified numerically by a Metropolis–Hastings Monte Carlo method. The methods presented herein could be applied to any kind of data or models, including differential cross-section data or integral benchmark experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Automated Control for Nuclear Thermal Propulsion Start-Up using MOOSE-based Applications

This report presents a Griffin/Bison/RELAP-7 numerical model of a prototypical NTP system that features fuel assemblies arranged in rings, and which was designed to simulate rapid startup transients. The physics modeled include full-core neutronics, assembly-wise heat conduction, and conjugate heat transfer, with the balance of plant mainly imposed through boundary conditions. In addition, various forms of automated reactivity control were deployed by using the MOOSE to autonomously drive the model and simulate the reactor transitioning from assumed initial conditions to nominal power in a fraction of a minute. To generate the cross-sections of the neutronics model, and in an effort to simultaneously account for the tremendous axial temperature gradients in the reactor and to limit the number of state points required for cross-section generation, the average component temperatures and hydrogen densities in the cooling channels were correlated to the average fuel and moderator temperatures, and fixed axial profiles were derived for nominal conditions and then used during the transient. With this approximation, a tractable cross-section library tabulated with fuel/moderator temperatures and CD angles was generated using Serpent. The full-core SPH correction procedure and the CD decusping technology in Griffin, respectively, ensure preservation of the multiplication factor and reaction rates at state points, along with a reasonably accurate reactivity worth between tabulated CD angles, despite using a coarse mesh. Feedback from other physics was calculated by modeling one representative fuel assembly per ring, along with the corresponding fuel and moderator cooling channels. To limit power overshoots during startup, another layer of multiphysics coupling was added to the model in order to automatically control the drums. Two different technologies presented herein showed outstanding performance in this regard: (1) a novel hybrid PID controller based on both power and reactivity signals, and (2) a PGC that relies on kinetics parameters and reactivity coefficients to predict future behavior and adjust the desired signal accordingly. A challenging benchmark was devised, featuring a power demand curve that exponentially increases by a factor of 500 within 30 seconds, then levels out after that. Both control approaches create a simulated power curve that closely follows the power demand curve and limits power overshoots to 1% or less. While the former approach requires more tuning of the internal parameters, the latter requires additional knowledge of the reactivity feedback coefficients and rates of change of the corresponding variables, including fuel and moderator temperature, which could be difficult to dynamically measure for a real NTP system. Fortunately, some inaccuracy in these quantities will not drastically degrade the PGC performance. Subsequently, a more realistic startup sequence was considered, in which the mass flow rate and outlet pressures are ramped up to model bootstrap and thrust build-up phases prior to reaching steady-state conditions, demonstrating the ability of the hybrid PID and PGCs to handle such transients, with both types of controllers exhibiting very similar behavior. Nevertheless, a significant chamber temperature overshoot was observed, caused by the demanded power signal and assumed mass flow rate. This issue could be mitigated by deploying a reactor controller that follows the chamber temperature signal and actuates both the control valves and drums (rather than using a power signal based solely on the drums to control reactivity). Enhancement of the hydrogen fluid properties available in MOOSE, as well as a better understanding of prototypical initial conditions, are also needed to further enhance this startup model. Finally, a study was performed to model decay heat post-shutdown, and to prepare for extending this model to predict shutdown behavior and post-shutdown pulsed cooling requirements.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Producing ENDF/B-quality Evaluations of 239 Pu(n,f) and 235 U(n,f) Average Prompt Neutron Multiplicities using the CGMF Model

This report documents evaluations of the neutron-induced 239 Pu and 235 U average prompt neutron multiplicities, $\overline{v}_p$, using the CGMF model developed at LANL. These evaluations are not updates of existing ENDF/B-VIII.0 nuclear data, but were both re-done from "scratch". That is, all experimental data were extracted from EXFOR, re-normalized to the newest nuclear data representing monitor observables, and detailed uncertainties were estimated from information in EXFOR as well as from templates of expected measurement uncertainties. We also included experimental data that were not yet available for ENDF/B-VIII.0, for instance, the data of Marini et al. for 239 Pu(n,f) $\overline{v}_p$ or the data of Khoklov et al. for 235 U(n,f) $\overline{v}_p$. Finally, we include the CGMF model; it computes $\overline{v}_p$ based on model parameters that at the same time calculate fission fragments as a function of mass, Y (A), the average total kinetic energy as a function of incident-neutron energy, $\langle TKE \rangle$(E inc ), etc. Such a detailed fission model that ties together many fission quantities has not been used to date for any $\overline{v}_p$ evaluation in ENDF/B. Here, we show that we can get evaluated 235 U(n,f) and 239 Pu $\overline{v}_p$ that not only correspond well to experimental $\overline{v}_p$, but that the associated evaluated model parameters also yield parameterizations of Y (A), $\langle TKE \rangle$(E inc ), etc., that correspond well to their respective experimental data. The new evaluated 239 Pu(n,f) $\overline{v}_p$ was also combined into a 239 Pu test file with the newest nuclear data for the 239 Pu prompt-fission neutron spectrum and fission cross sections. This new 239 Pu file performed reasonably well in predicting PU-MET-FAST assemblies, reaction rates in Jezebel and Flattop and three LLNL pulsed spheres. Due to that, the new 239 Pu(n,f) $\overline{v}_p$ evaluation presented here is being considered for ENDF/B-VIII.1. Hence, we can show here that CGMF is able to produce ENDF/B quality $\overline{v}_p$ nuclear data.

239Pu, 235U, Average Prompt-fission Neutron Multip↗
DOE OSTI2022

Additive Manufacturing Flaw Assessment Methodology

An evolution fatigue data and flaw tolerance of components produced using the Powder Bed Fusion (PBF) Additive Manufacturing (AM) process is documented in this report. Initial differences in fatigue data for AM components compared to smooth bar fatigue data indicated a need for a more detailed analysis of AM data available in technical literature. The investigation was initiated to support the development of a fatigue analysis methodology for AM components to support the of codification of AM technology for pressure equipment. The project was initiated to collect and analyze stainless steel 304L and 316L AM fatigue test data and corresponding process and quality information to develop S-N and E-N based fatigue data representation. Additional AM fatigue test data including Inconel Ti-6-4 and aluminum alloys were also considered for comparison purposes Metallic AM parts tend to contain various forms of defects distributed throughout the part. If an AM part is subjected to fatigue loading in service, a fatigue analysis needs to be performed during the design process to ensure an acceptable service life for the part. Post-process machining and polishing do not to improve fatigue resistance in any significant degree. The low cycle fatigue regime is of particular interest to this project in support of flaw acceptance criteria currently under development by ASME’s BPTCS/BNCS. Internal defects become exposed as external surface defects during machining for the machining of the AM part to final dimensions. This implies that as long as inherent AM defects are within a controlled limit in terms of both size and distribution characteristics, the corresponding fatigue test data in terms of either S-N (stress life) or E-N (strain life) can be investigated and characterized to establish fatigue properties of AM parts for design and fatigue evaluation purposes. The resulting S-N or E-N curves and their scatter bands can be used to derive fatigue design allowable stress values by capturing the effects of distributed discontinuities within an acceptable limit.

36 MATERIALS SCIENCE↗