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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Report Summarizing Progress in Digital Image Correlation Analysis of Burst Phenomenon

This report is an update on the progress made towards utilizing in-situ methods to observe and quantify Zry-4 deformation behavior during burst testing. A heavily modified burst test was developed that allows both digital image correlation and infrared thermography to be utilized during testing to quantify strain and axial thermal gradient data during burst. A variety of tests ranging from 6.2-15.9 MPa internal pressure and utilizing both constant and transient pressure conditions were conducted and presently reported on. The test modifications, which are detailed extensively in a previous report, are effectively found to have no discernible impact on burst temperatures when compared to historical Zircaloy burst data. To support future modeling studies, the IR thermography data was used to approximate the thermal gradient down the axial length at burst for all the claddings. Then, DIC is first correlated to thermocouple temperature for a 10.3 MPa Transient pressure test, and then DIC correlated to IR thermal gradient data down the axial length of a 8.2 MPa Transient pressure test at several points.

36 MATERIALS SCIENCE↗
DOE OSTI2021

BISON As-run AGR-3/4 Irradiation Test Predictions

BISON, a nuclear fuel performance application built using the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element library, was used to model the Advanced Gas Reactor (AGR)-3/4 irradiation test using as-run physics and thermal hydraulics data. The AGR-3/4 test consists of the combined third and fourth planned irradiations of the AGR Fuel Development and Qualification Program. The AGR-3/4 test train consists of twelve separate and independently controlled and monitored capsules. Each capsule contains four compacts filled with both uranium oxycarbide (UCO) unaltered “driver” fuel particles and UCO designed-to-fail (DTF) fuel particles. The DTF fraction was specified to be 1×10-2. This report documents the calculations performed to predict the failure probability of tristructural isotropic (TRISO) coated driver fuel particles during the AGR-3/4 experiment on a single compact. This report will demonstrate the capabilities of BISON to model the complex AGR-3/4 irradiation test and identify further development needed to capture the fuel particle failure probability and source term on every compact for further comparison from post-irradiation examination (PIE) data. The calculations include the modeling of the AGR-3/4 irradiation that occurred from December 2011 to April 2014 in the Advanced Test Reactor (ATR) over a total of 10 ATR cycles including seven normal cycles, one low power cycle, one unplanned outage cycle, and one Power Axial Locator Mechanism (PALM) cycle.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Findings on Subtask 3.1 - Bakken Rich Gas Enhanced Oil Recovery Project

Total in-place oil for the Bakken petroleum system (BPS) (which includes the Bakken and Three Forks Formations) has been estimated to be 600 billion barrels (bbl). However, BPS wells have decline rates as high as 85% over the first 3 years of their lives, and primary recovery factors typically range from 3% to 10% of original oil in place. Given the low initial recovery rates, even small incremental productivity improvements could dramatically increase technically recoverable oil in the BPS. One potential solution is enhanced oil recovery (EOR) using gas injection, such as carbon dioxide (CO2) or hydrocarbon (HC) gases. While commonly used in conventional reservoirs, CO2 EOR in unconventional tight oil reservoirs has been limited to pilot tests. EOR using rich gas (mixture of methane, ethane, and propane) has also been employed in numerous pilots in several unconventional plays and has recently been successfully applied in the Eagle Ford play. If successful, large-scale gas-based EOR in the BPS could dramatically increase oil productivity and recovery factors and extend the life of the play for decades. While CO2 may be a technically suitable working fluid for EOR in the BPS, supplies are limited and costs for using CO2 in EOR pilots are prohibitively high. Meanwhile, produced gas flaring has presented challenges for BPS operators in North Dakota. Analysis conducted by the North Dakota Pipeline Authority indicates that the current gas-gathering infrastructure in North Dakota is insufficient to accommodate all of the associated gas that is produced from the BPS. The geographically isolated location of North Dakota relative to large natural gas markets, combined with suppressed natural gas prices, has made it economically challenging for industry to invest capital in expanding gas-gathering infrastructure in the state. These circumstances led to a research program conducted by the Energy & Environmental Research Center (EERC) in partnership with Liberty Resources Management Company LLC (LR) to examine the potential to use rich gas injection for EOR and mitigate flaring. A rich gas EOR pilot test was designed and executed by LR at its Stomping Horse development area in Williams County, North Dakota. From July 2018 through May 2019, a total of 160 million standard cubic feet (MMscf) of rich produced gas was injected into the BPS using five different wells in a sequential injection strategy. LR’s Leon–Gohrick drill spacing unit (DSU) was used as the test site. Regulatory oversight was provided by the North Dakota Industrial Commission (NDIC). Technical support was provided by the EERC through a series of laboratory, modeling, and field-based activities, and additional post-pilot research activities incorporated learnings from the test, developed new laboratory data, improved fracture modeling methods, and developed machine learning and big data analytics. The results from the Stomping Horse rich gas EOR pilot activities indicate that developing an effective, economical EOR approach for the BPS will require more field tests. Another key lesson learned from the Stomping Horse tests is that detailed pre- and posttest data on reservoir conditions and fluids production are essential. Robust reservoir characterization provides information that is crucial to creating realistic geomodels and conducting valid dynamic simulations of potential EOR scenarios. A detailed understanding of the completions and production history of offset wells is also necessary for valid test result interpretations. This knowledge is essential to designing the operational parameters of injectivity tests and interpreting the results. A conformance control strategy is also essential to success. Laboratory-based examinations of rich gas interactions with reservoir fluids and rocks were conducted, with an emphasis on determining the ability to mobilize oil in the tight reservoir rocks and shales of the BPS. Injection fluid composition was shown to have a positive impact on reducing reservoir oil minimum miscibility pressure (MMP), reducing interfacial tension (IFT), and altering wettability. IFT and contact angle measurements demonstrated that wettability can be altered in the presence of rich gas, suggesting the potential to improve oil recovery. Iterative modeling of surface infrastructure and reservoir performance using data generated by the various project activities was conducted. A geologic model of the Stomping Horse area was built; history-matched oil, gas, and water production was used in simulations of various EOR scenarios. Early programmatic modeling results were used to support LR’s design and operation of the EOR pilot and to provide insight regarding optimization of future commercial-scale BPS EOR design and operations. Post-pilot modeling focused on alternative methods of understanding complex fracture networks and accelerating simulation time. These led to improved simulation run times and provide excellent history-matching results. Several of these iterative models were used as the bases for developing algorithms into machine learning and big data analytics. History matching in reservoir simulation is time-consuming and computer processing-intensive. Machine learning algorithms were created, and an automated history-matching tool was developed. A large set of synthetic reservoir simulations were created to generate well responses (oil, gas, and water production, well bottomhole pressure [BHP], and tracer or propane breakthrough) for a set of EOR operating parameters that included offset well status (open or closed), injectate (rich gas or propane), injection rate, and injection well BHP. A user interface was developed to provide real-time visualization. Machine learning-based models were developed to provide rapid forecasting of well performance given a set of user-defined EOR operating parameters. These predictive models allow the user to modify the offset well status, injection rate, and injection well BHP and rapidly forecast future production performance. The combination of real-time visualization tools with real-time forecasting tools provides a framework for real-time control—operational changes that the EOR site operator can enact (e.g., changing gas injection rates) to affect the observed performance and potentially improve the EOR outcome. There is great reason to be optimistic about the future of EOR in the Bakken. The results of the laboratory studies suggest significant potential for high rates of oil mobilization using produced field gas injection under the right conditions. The results of the lab studies, combined with rigorous statistical analysis of well production data and associated modeling efforts, confirm the notion that fluid mobility within the reservoir is controlled by fractures. As more knowledge is gained about the nature and distribution of fracture networks in the Bakken, the industry will be in a better position to predict and, ultimately, influence fluid mobility. New field tests are necessary to develop a more complete understanding of those conditions. Thoughtful and creatively engineered field tests within a well-characterized geologic setting will yield the fundamental knowledge needed to take Bakken oil production to the next level. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission’s Oil and Gas Research Program and Computer Modelling Group.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2021

Evaluation of BISON's Transient Fission Gas Release Model on High-Burnup UO 2 MiniFuel

BISON's fission gas release (FGR) model has been evaluated and tested over several hypothetical thermal transient cases at high burnup and validated against FGR experimental data from an annealing test at high burnup. Diffusion-controlled FGR is accounted for in the model by including several physical mechanisms such as the diffusion of fission gas to grain faces, grain boundary sweeping, grain growth, and intergranular bubble growth. Under temperature variations, FGR is increased due to the development of micro-cracks at grain faces. This existing transient capability, however, was developed based on transient fuel behavior during power ramps and the associated micro-cracking process. Nevertheless, a fraction of the FGR during temperature transients such as LOCA can ensue from different mechanisms (in particular, fuel fragmentation) and present an important dependency on local burnup and high burnup structure formation. It follows that the performance and necessary developments of the existing BISON fission gas model for the analysis of LOCA-type transients need to be investigated through a systematic assessment study. Such a study is performed in this work. The model shows an overall good qualitative agreement with experimental observations on FGR during annealing tests with different temperature conditions. It also accounts well for microstructural effects on FGR. When quantitatively compared to FGR data from previously irradiated 103 MWd/kgU UO 2 discs under thermal annealing, the model shows a less satisfactory agreement with the experimental data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

FiXR : a framework to reconstruct fiber cross-sections from X-ray fiber diffraction experiments

Ab initio reconstruction methods have revolutionized the capabilities of small-angle X-ray scattering (SAXS), allowing the data-driven discovery of previously unknown molecular conformations, exploiting optimization heuristics and assumptions behind the composition of globular molecules. While these methods have been successful for the analysis of small particles, their impact on fibrillar assemblies has been more limited. The micrometre-range size of these assemblies and the complex interaction of their periodicities in their scattering profiles indicate that the discovery of fibril structures from SAXS measurements requires novel approaches beyond extending existing tools for molecular discovery. In this work, it is proposed to use SAXS measurements, together with diffraction theory, to infer the electron distribution of the average cross-section of a fiber. This cross-section is modeled as a discrete electron density with continuous support, allowing representations beyond binary distributions. Additional constraints, such as non-negativity or smoothness/connectedness, can also be added to the framework. The proposed approach is tested using simulated SAXS data from amyloid β fibril models and using measured data of Tobacco mosaic virus from SAXS experiments, recovering the geometry and density of the cross-sections in all cases. The approach is further tested by analyzing SAXS data from different amyloid β fibril assemblies, with results that are in agreement with previously proposed models from cryo-EM measurements. The limitations of the proposed method, together with an analysis of the robustness of the method and the combination with different experimental sources, are also discussed.

Biochemistry & Molecular Biology↗
DOE OSTI2025

NDMAS

Overview of Current ART-GCR Data: Fuel Fabrication, Irradiation Monitoring (Fuel & Graphite – near real-time for HDG-1), Post-Irradiation Examination (Fuel & Graphite), Graphite Characterization (Baseline and Irradiated), High Temperature Metals Mechanical Tests, Design, Methods, and Validation Data, Japan Atomic Energy Agency’s High Temperature Test Reactor (HTTR), Argonne National Laboratory’s Natural convection Shutdown heat removal Test Facility (NSTF), Oregon State University’s High Temperature Test Facility (HTTF), Generation IV International VHTR Materials Handbook, Additional related data, and Advanced Test Reactor operations (near real-time).

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

A 20 Gbps PAM4 data transmitter ASIC for particle physics experiments

We present the design and test results of a novel data transmitter ASIC operating up to 20.48 Gbps with 4-level Pulse-Amplitude-Modulation (PAM4) for particle physics experiments. This ASIC, named GBS20, is fabricated in a 65 nm CMOS technology. Two serializers share a 5.12 GHz Phase Locked Loop (PLL) clock. The outputs from the serializers are combined into a PAM4 signal that directly drives a Vertical-Cavity-Surface-Emitting-Laser (VCSEL). The input data channels, each at 1.28 Gbps, are scrambled with an internal 2$^{7}$-1 Pseudo-Random Binary Sequence (PRBS), which also serves as a frame aligner. GBS20 is tested to work at 10.24 and 20.48 Gbps with a VCSEL-based Transmitter-Optical-Subassembly (TOSA). The power consumption of GBS20 is below 238 mW and reduced to 164 mW in the low-power mode.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

VADER: A Tool for Criticality Safety Validation

The purpose of criticality safety is to prevent any inadvertent criticality from occurring during the handling or storage of fissile material. Calculations are frequently used to demonstrate that a sufficient subcritical margin exists. Validation is a key aspect of the evaluation process, establishing the suitability, accuracy, and associated uncertainty of the computational method and data to be used for the intended application. The validation process is performed by comparing the results of critical experiments with the calculated results from models of the experiments using the computational method to be validated. Laboratory critical experiments are controlled systems that achieve a k eff of approximately 1 in order to investigate the parameters at which such a critical condition is achieved. The validation parameters that are traditionally applied to safety analysis calculations are the bias and the bias uncertainty . The bias is the deviation of the average k eff of the validation suite from unity. The bias uncertainty accounts for the statistical uncertainty in the bias based on the standard deviation, sample size, and distribution of k eff values of the validation suite. The values of bias and bias uncertainty ensure that the systems predicted to be subcritical by the computational method will indeed be subcritical. The bias and bias uncertainty are often combined to determine an upper subcritical limit (USL) or computational margin that can then be applied to safety analysis calculations. Many methods have been developed by different organizations to calculate the bias and bias uncertainty for various types of criticality analyses. Each of these methods typically requires that the validity of various underpinning statistical assumptions be confirmed to demonstrate that the method is appropriate for the analysis of a given validation suite. An example of the validation decision making flow is shown in Fig.1. As shown in Fig. 1, the analyst performing the validation fits a trend line to the data and performs a test to determine if the trend was a statistically better representation of the data than if it were treated as an uncorrelated sample. If the trend line is a better representation of the data, then the analyst uses any one of a number of trending techniques to determine the bias and bias uncertainty. If a trend is not an appropriate representation of the data, then the analyst proceeds to perform a normality assessment for the data. If the normal assumption can be shown to be acceptable, then the analyst calculates the bias and bias uncertainty with the parametric technique. If the assumption of normality cannot be justified, then the nonparametric technique is used. Once the decision flow has been followed and the appropriate technique has been selected, the bias and bias uncertainty is typically combined with an administrative margin to determine a USL below which calculated values of k eff for safety analysis models can be considered subcritical. The calculations used in each decision are often performed with spreadsheets or with small programs available at various sites performing criticality analyses. Expertise in understanding and interpreting the results must be maintained to perform these calculations. This can often be an error-prone process. Oak Ridge National Laboratory (ORNL) is currently developing the Validation and Data Evaluation Resource (VADER) to simplify and automate the criticality safety validation process and to provide a software quality assurance pedigree to the calculational methods used. This paper discusses the use of the Fulcrum user interface with VADER, the anticipated initial capabilities of VADER to perform validation analyses, and the output from the code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Reaction History Uncertainty Propagation from Flux to α and from Time to α [Slides]

We discuss uncertainty propagation in reaction history data from underground nuclear tests. γ reaction history detectors measured flux as a function of time. After data analysis we report α as a function of time. Flux uncertainty and time uncertainty propagation into α uncertainty are discussed in detail. Discretization of the formulas is discussed.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Accurate Effective Stress Measures: Predicting Creep Life for 3D Stresses Using 2D and 1D Creep Rupture Simulations and Data

Operating structural components experience complex loading conditions resulting in 3D stress states. Current design practice estimates multiaxial creep rupture life by mapping a general state of stress to a uniaxial creep rupture correlation using effective stress measures. The data supporting the development of effective stress measures are nearly always only uniaxial and biaxial, as 3D creep rupture tests are not widely available. This limitation means current effective stress measures must extrapolate from 2D to 3D stress states, potentially introducing extrapolation error. In this work, we use a physics-based, crystal plasticity finite element model to simulate uniaxial, biaxial, and triaxial creep rupture. Here, we use the virtual dataset to assess the accuracy of current and novel effective stress measures in extrapolating from 2D to 3D stresses and also explore how the predictive accuracy of the effective stress measures might change if experimental 3D rupture data was available. We confirm these conclusions, based on simulation data, against multiaxial creep rupture experimental data for several materials, drawn from the literature. The results of the virtual experiments show that calibrating effective stress measures using triaxial test data would significantly improve accuracy and that some effective stress measures are more accurate than others, particularly for highly triaxial stress states. Results obtained using experimental data confirm the numerical findings and suggest that a unified effective stress measure should include an explicit dependence on the first stress invariant, the maximum tensile principal stress, and the von Mises stress.

36 MATERIALS SCIENCE↗
DOE OSTI2023

MODFLOW6 models used to evaluate potential stresses and hydrologic conditions driving water-level fluctuations in well ER-5-3-2, Frenchman Flat, Southern Nevada

The hydrograph for well ER-5-3-2 in Frenchman Flat, southern Nevada, has previously unexplained water-level fluctuations. Four, three-dimensional, groundwater models (MODFLOW 6) were developed to evaluate potential stresses and hydrologic conditions affecting the well ER-5-3-2 hydrograph. Four model scenarios were developed that simulated: (1) wellbore leakage without recharge, (2) wellbore leakage with recharge, (3) shallow (low transmissivity) and deep (high transmissivity) carbonate rocks, and (4) lateral heterogeneity of carbonate rocks. Input and output files for the four model scenarios are in the model and output directories, respectively. Hydraulic conductivity, specific storage, and wellbore-leakage rates (when simulated) were estimated with parameter estimation (PEST) by minimizing a weighted composite, sum-of-squares objective function. The objective function was informed by measurement and Tikhonov regularization observations. Measurement observations included drawdowns from the constant-rate aquifer test and water-level altitudes measured in well ER-5-3-2 from 2001-2021. Tikhonov regularization informed hydraulic conductivity and specific storage parameters that were insensitive to measurement observations, where homogeneity was the preferred relation. Batch files, executables, and MODFLOW 6, PEST, and post-processing utilities are in the ancillary directory. Supplementary data also are included in the ancillary directory, including site information, high-frequency water-level and aquifer-test data, transmissivity estimates, water-chemistry data, and water-temperature analyses. This USGS data release contains data, analyses, and model files for the simulations and analysis results described in U.S. Geological Survey Scientific Investigations Report (https://doi.org/10.3133/sir20225132).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Characterization of flow and transport in a fracture network at the EGS Collab field experiment through stochastic modeling of tracer recovery

Energy extraction from subsurface reservoirs is important for addressing the increasing energy demand and environmental concerns such as global warming. However, the characterization of subsurface reservoirs, particularly reservoirs dominated by fracture networks remains a challenge due to the lack of means to directly observe subsurface processes. This study explores the feasibility and efficacy of characterizing fracture flow and transport processes in an enhanced geothermal system (EGS) testbed through stochastic tracer modeling. There are two enabling factors that allow application of stochastic modeling to characterize a subsurface reservoir. First, an abundance of geological and geophysical measurements enables the development of a high-fidelity and well-constrained fracture network model. Second, high-performance computing (HPC) allows running massive realizations efficiently. Six conservative tracer tests were stochastically modeled and produced satisfactory realizations that successfully reproduce field tracer recovery data from each tracer test. The evolution of flow and transport processes in the fracture network was then analyzed from these satisfactory realizations. This work demonstrates that stochastic tracer modeling on a high-fidelity fracture network model is feasible and can provide important insights regarding flow and transport characteristics in subsurface fractured reservoirs.

58 GEOSCIENCES↗
DOE OSTI2021

Energy calibration of the ProtoDUNE-SP TPC

The single-phase liquid argon prototype at CERN (ProtoDUNE-SP) acts as a validation of the design for the DUNE single-phase far detector. With a total mass of 770 tons, it is the largest monolithic liquid argon single-phase time projection chamber in the world. ProtoDUNE-SP collected test-beam in autumn of 2018 and has been collecting cosmic and special calibration data since the end of 2018. To analyze data from the test-beam, a calibration plan using cosmic muons passing through the detector was developed. An outline of this plan and its impact on the calorimetric measurements of the detector will be discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

2021 Smoky Mountains Conference Data Challenge Synthetic-to-Real Domain Adaptation for Autonomous Driving Dataset

The dataset is comprised of both real and synthetic images from a vehicle's forward-facing camera. Each camera image is accompanied by a corresponding pixel-level semantic segmentation image (all files are .png files). In total, the dataset contains 5600 images in the training/validation set and 1400 images in the testing set. The training dataset contains mostly synthetic RGB images collected with a wide range of weather and lighting conditions using the CARLA simulator [1]. In addition, the training data also includes a small pre-selected subset of data from the Cityscapes training dataset – which is comprised of RGB-segmentation image pairs from driving scenarios in various European cities [2]. The testing data is split into three sets. The first set contains synthetic CARLA images with weather/lighting conditions that were not present in the training set. The second set is a subset of the Cityscapes testing dataset. Finally, the third set is an unknown testing set which will not be revealed to the participants until after the submission deadline. [1] Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V. (2017, October). CARLA: An open urban driving simulator. In Conference on robot learning (pp. 1-16). PMLR. [2] Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., ... and Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3213-3223).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Added Value for Integrated Marine Energy Data Systems (February 2021)

Launched in 2019, the Portal and Repository for Information on Marine Renewable Energy (PRIMRE) provides centralized access, standardization, community building, and integration of United States (U.S.) databases, tools and codes, and other resources that cover a range of marine energy information. The PRIMRE universe contains a series of Knowledge Hubs that represent data and information on testing of marine energy devices (MHKDR); environmental effects (Tethys); engineering and technical papers (Tethys Engineering); descriptions of marine energy companies and technologies (Marine Energy Projects Database); codes and models (Marine Energy Software); and guidance on testing and measurements (Telesto). Content is added to PRIMRE by applying a set of Guidelines and Best Practices (PRIMRE Guidelines). An aggregate search across the PRIMRE site enables users to find data and information from all the PRIMRE Knowledge Hubs simultaneously, using a single entry-point (PRIMRE Search). In addition to providing access to a range of data and information on marine energy development, testing, and effects, PRIMRE allows for the development of valueadded products and processes that will help move the marine energy industry forward. The PRIMRE team has recently launched two key initiatives in the U.S.-Signature Projects and Lessons Learned. Outputs and outcomes from these two initiatives will be highlighted in this paper. The Signature Projects initiative is intended to bring focus to a selection of ongoing and completed marine energy projects funded by the U.S. Department of Energy's Water Power Technologies Office (WPTO), and to inform the marine energy community of what investigations have been undertaken, what tools are available, and where gaps in information persist. Each Signature Project tags papers, reports, and data from large marine energy research projects, providing easy access and attention to all the output and associated products from each project. The Lessons Learned initiative is intended to ensure that hard-won achievements are recognized and available for those who come later, that missteps and unfortunate outcomes can be prevented in future, and that efficiencies and effective shortcuts can be publicized and used as the marine energy industry moves forward. This initiative builds off the Knowledge Hubs and reaches out to members of the marine energy community, particularly technology developers and researchers, to integrate experience in the development, deployment, assessment, success, challenges while creating this new industry and field of study.

data sharing↗
DOE OSTI2021

Added value for integrated marine energy data systems

Launched in 2019, the Portal and Repository for Information on Marine Renewable Energy (PRIMRE) provides centralized access, standardization, community building, and integration of United States (U.S.) databases, tools and codes, and other resources that cover a range of marine energy information. The PRIMRE universe contains a series of Knowledge Hubs that represent data and information on testing of marine energy devices (MHKDR); environmental effects (Tethys); engineering and technical papers (Tethys Engineering); descriptions of marine energy companies and technologies (Marine Energy Projects Database); codes and models (Marine Energy Software); and guidance on testing and measurements (Telesto). Content is added to PRIMRE by applying a set of Guidelines and Best Practices (PRIMRE Guidelines). An aggregate search across the PRIMRE site enables users to find data and information from all the PRIMRE Knowledge Hubs simultaneously, using a single entry-point (PRIMRE Search). In addition to providing access to a range of data and information on marine energy development, testing, and effects, PRIMRE allows for the development of value added products and processes that will help move the marine energy industry forward. The PRIMRE team has recently launched two key initiatives in the U.S.—Signature Projects and Lessons Learned. Outputs and outcomes from these two initiatives will be highlighted in this paper. The Signature Projects initiative is intended to bring focus to a selection of ongoing and completed marine energy projects funded by the U.S. Department of Energy’s Water Power Technologies Office (WPTO), and to inform the marine energy community of what investigations have been undertaken, what tools are available, and where gaps in information persist. Each Signature Project tags papers, reports, and data from large marine energy research projects, providing easy access and attention to all the output and associated products from each project. The Lessons Learned initiative is intended to ensure that hard-won achievements are recognized and available for those who come later, that missteps and unfortunate outcomes can be prevented in future, and that efficiencies and effective shortcuts can be publicized and used as the marine energy industry moves forward. This initiative builds off the Knowledge Hubs and reaches out to members of the marine energy community, particularly technology developers and researchers, to integrate experience in the development, deployment, assessment, success, and challenges while creating this new industry and field of study.

Copping, Andrea E.↗
DOE OSTI2021

Added value for integrated marine energy data systems

Launched in 2019, the Portal and Repository for Information on Marine Renewable Energy (PRIMRE) provides centralized access, standardization, community building, and integration of United States (U.S.) databases, tools and codes, and other resources that cover a range of marine energy information. The PRIMRE universe contains a series of Knowledge Hubs that represent data and information on testing of marine energy devices (MHKDR); environmental effects (Tethys); engineering and technical papers (Tethys Engineering); descriptions of marine energy companies and technologies (Marine Energy Projects Database); codes and models (Marine Energy Software); and guidance on testing and measurements (Telesto). Content is added to PRIMRE by applying a set of Guidelines and Best Practices (PRIMRE Guidelines). An aggregate search across the PRIMRE site enables users to find data and information from all the PRIMRE Knowledge Hubs simultaneously, using a single entry-point (PRIMRE Search). In addition to providing access to a range of data and information on marine energy development, testing, and effects, PRIMRE allows for the development of value added products and processes that will help move the marine energy industry forward. The PRIMRE team has recently launched two key initiatives in the U.S.—Signature Projects and Lessons Learned. Outputs and outcomes from these two initiatives will be highlighted in this paper. The Signature Projects initiative is intended to bring focus to a selection of ongoing and completed marine energy projects funded by the U.S. Department of Energy’s Water Power Technologies Office (WPTO), and to inform the marine energy community of what investigations have been undertaken, what tools are available, and where gaps in information persist. Each Signature Project tags papers, reports, and data from large marine energy research projects, providing easy access and attention to all the output and associated products from each project. The Lessons Learned initiative is intended to ensure that hard-won achievements are recognized and available for those who come later, that missteps and unfortunate outcomes can be prevented in future, and that efficiencies and effective shortcuts can be publicized and used as the marine energy industry moves forward. This initiative builds off the Knowledge Hubs and reaches out to members of the marine energy community, particularly technology developers and researchers, to integrate experience in the development, deployment, assessment, success, and challenges while creating this new industry and field of study.

Copping, Andrea E.↗
DOE OSTI2022

Application of BISON to UO 2 MiniFuel fission gas release analysis

There has been a recent push to accelerate fuel qualification by developing revolutionary capabilities to reduce irradiation periods, and thereby, reduce the time required to qualify a new fuel system. One such capability is the MiniFuel irradiation capsule designed to miniaturize fuel samples and irradiate “mini” fuel samples under isothermal temperature conditions. MiniFuel allows steady-state irradiations to decouple the traditionally coupled fission rate (i.e., power) and temperature parameters to understand and generate microstructures observed in fuel operated in a commercial reactor. Furthermore, this process offers the possibility to gather in situ data as well as postirradiation or transient data such as thermal conductivity, specific heat, fission gas diffusion and release, etc. However, accelerating fuel qualification is not solely reliant on generating large amounts of data but also on developing an informed test matrix designed to rapidly generate impactful data. Additionally, this process is reliant on fuel performance codes, such as BISON, to evaluate MiniFuel irradiations using existing material models. This process pinpoints model/data gaps, identifies desired irradiation conditions, and subsequently supports model validation and development. This work describes the use of BISON to perform a number of sensitivity studies designed to understand conditions that lead to fission gas release (FGR) under steady-state isothermal irradiation conditions and temperature transient conditions. The model is applied to a UO 2 MiniFuel example and shows an overall good qualitative agreement with experimental FGR annealing tests under different temperature conditions. It also accounts well for microstructural effects on FGR. When quantitatively compared with FGR data from previously irradiated 103 MWd/kgU UO 2 discs under thermal annealing, the model shows a less satisfactory agreement with the experimental data. Finally, a UO 2 MiniFuel test matrix is proposed to help to extend the model's operational range and validate the new FGR model capabilities to higher burnups and transient conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗