Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Power Generation Cycle”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
NASA NTRS2016

Optimized Heat Pipe Backup Cooling System Tested with a Stirling Convertor

In a Stirling Radioisotope Power System (RPS), heat must be continuously removed from the General Purpose Heat Source (GPHS) modules to maintain the modules and surrounding insulation at acceptable temperatures. The Stirling convertor normally provides this cooling. If the Stirling convertor stops in the current system, the insulation is designed to spoil, preventing damage to the GPHS at the cost of an early termination of the mission. An alkali-metal variable conductance heat pipe (VCHP) can be used to passively allow multiple stops and restarts of the Stirling convertor by bypassing the heat during stops. In a previous NASA Small Business Innovation Research (SBIR) Program, Advanced Cooling Technologies, Inc. (ACT) developed a series of sodium VCHPs as backup cooling systems for the Stirling RPS. In 2012, one of these VCHPs was successfully tested at NASA Glenn Research Center with a Stirling convertor as an Advanced Stirling Radioisotope Generator (ASRG) backup cooling system. The prototype; however, was not optimized and did not reflect the final heat rejection path. ACT through further funding has developed a semioptimized prototype with the finalized heat path for testing at Glenn with a Stirling convertor. The semioptimized system features a two-phase radiator and is significantly smaller and lighter than the prior prototype to reflect a higher level of flight readiness. The VCHP is designed to activate and remove heat from the GPHS during stoppage with a small temperature increase from the nominal vapor temperature. This small temperature increase from nominal is low enough to avoid risking standard ASRG operation and spoiling of the multilayer insulation (MLI). The VCHP passively allows the Stirling convertor to be turned off multiple times during a mission with potentially unlimited off durations. Having the ability to turn the Stirling off allows for the Stirling to be reset and reduces vibrations on the platform during sensitive measurements or procedures. This paper presents the design of the VCHP and its test results with a Stirling convertor at Glenn. Tests were carried for multiple on and off cycles to demonstrate repeatability. The impacts associated with the addition of the VCHP to the system are also addressed in terms of mass and additional heat losses due to the presence of the VCHP.

Heat Pipe↗
NASA NTRS2015

Design, Qualification and Integration Testing of the High-Temperature Resistance Temperature Device for Stirling Power System

The Advanced Stirling Radioisotope Generator (ASRG), developed from 2006 to 2013 under the joint sponsorship of the United States Department of Energy (DOE) and National Aeronautics and Space Administration (NASA) to provide a high-efficiency power system for future deep space missions, employed Sunpower Incorporated's Advanced Stirling Convertors (ASCs) with operating temperature up to 840 C. High-temperature operation was made possible by advanced heater head materials developed to increase reliability and thermal-to-mechanical conversion efficiency. During a mission, it is desirable to monitor the Stirling hot-end temperature as a measure of convertor health status and assist in making appropriate operating parameter adjustments to maintain the desired hot-end temperature as the radioisotope fuel decays. To facilitate these operations, a Resistance Temperature Device (RTD) that is capable of high-temperature, continuous long-life service was designed, developed and qualified for use in the ASRG. A thermal bridge was also implemented to reduce the RTD temperature exposure while still allowing an accurate projection of the ASC hot-end temperature. NASA integrated two flight-design RTDs on the ASCs and assembled into the high-fidelity Engineering Unit, the ASRG EU2, at Glenn Research Center (GRC) for extended operation and system characterization. This paper presents the design implementation and qualification of the RTD, and its performance characteristics and calibration in the ASRG EU2 testing.

Deep Space↗
DOE OSTI2025

NewLife Nuclear - An Environmentally and Economically Minded Solution for Fusion Energy Waste Handling

Energy demand is rising as a result of innovative and increasingly more energy intensive processes coming to fruition, particularly through the recent interest in the development of AI data centers as well as manufacturing with the push towards increasing domestic manufacturing interest. Fusion energy can provide virtually limitless energy to support this increase in energy demand. Fusion energy concepts, largely classified as magnetic fusion energy (MFE) and inertial fusion energy (IFE) are being pursued, each having unique challenges to overcome before the successful deployment of electricity to the grid. Achieving fusion ignition on the National Ignition Facility, first in December 2022, and eight times since, has demonstrated the scientific viability of the IFE approach. Meanwhile, MFE test stands continue to improve confinement times, making meaningful strides in progressing towards experimental scientific viability. In each of these approaches, an emphasis is placed on generating more power out of the system than what is required to power the system. An under-researched area applicable to both IFE and MFE is handling activated waste coming out of fusion energy systems, both in the course of normal daily operations, as well as in intermittent periods as structural materials may need to be replaced. In the context of an IFE plant system, commonly discussed plant designs suggest targets are ignited within a chamber at a rate of up to one million targets per day. Between each shot, the chamber housing the ignition event will clear a portion of the chamber – resulting in a mixture of vaporized target gas, target debris, and other materials being expelled from the chamber [source]. Additionally, IFE system concepts typically discuss the modularization of plant designs, which are expected to be replaced periodically as the components degrade over time. This would result in the irradiated chamber structure materials, likely metals and alloys, needing to be removed and safely stored. In MFE plant systems, while targets are not ignited at a repetition rate with the frequent chamber clearing as is expected in IFE plant systems, it is anticipated that portions of the confinement area interfacing with the hot plasma will need to be replaced periodically. In each system, without additional investment and research into alternative processing and recycling methods, the result is storing irradiated materials, and other elements in a safe containment area until they are no longer activated. – resulting in significant waste both economic and environmental.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Nuclear data for reactor production of 131 Ba and 133 Ba

The newest radioisotope for brachytherapy treatment of prostate cancer is 131 Cs (t 1/2 = 9.69 d, 100% EC). Generated via electron capture decay of 131 Ba (t 1/2 = 11.6 d, 100% EC), 131 Cs has been used in brachytherapy for prostate cancer since 2004. The 131 Ba parent is produced through neutron capture of enriched 130 Ba in a nuclear reactor. For large-scale production of 131 Ba, an accurate knowledge of production and burnup cross sections of 131 Ba are essential. Here, we report two group cross sections (thermal and resonance integrals) for 130 Ba and 131 Ba and a new measure of the half-life of 131 Ba. Targets consisting of milligram quantities of enriched 130Ba (~35%) were irradiated in Oak Ridge National Laboratory's High Flux Isotope Reactor at thermal and resonance neutron fluxes of (1.9–2.1) × 10 15 and (5.8–7.0) × 10 13 neutrons·cm -2 s -1 , respectively, for durations ranging from 3 to 26 days. In addition, cadmium covered samples of 130 Ba were irradiated for 1 hour at 12.6% full reactor power (10.7 MW). The yield of 131 Ba approaches a saturation value of ~60 GBq (~1.6 Ci) per mg of 130 Ba for 20 days irradiation at a thermal neutron flux of 1.8 × 10 15 n·s -1 ·cm -2 , with a thermal/epithermal ratio of ~30. Under the above experimental conditions, the two group cross sections of 130 Ba are 6.9 ± 0.5 b (thermal, σ 0 ) and 173 ± 7 b (resonance, I 0 ). These values represent the sum of cross sections to metastable and ground states of 131 Ba. For 131 Ba, the empirically measured thermal cross section is 200 ± 50 b assuming an I 0 /σ 0 of 10. This cross section is reported for the first time. Further, the half-life of 131 Ba was remeasured to be 11.657 ± 0.008 d. Lastly, this study also resulted in the co-production of 133 Ba (t 1/2 = 10.52 y, 100% EC). The experimental yield of 133 Ba is ~370 MBq (~10 mCi) per mg of 132 Ba (thin target) for one cycle irradiation in the High Flux Isotope Reactor, and measured two-group 132 Ba cross sections are 7.2 ± 0.2 b and 39.9 ± 1.3 b. These values also represent the sum of cross sections to metastable and ground states of 133 Ba.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Recent Metallic Fuel Data Recovery in FIPD

The Metallic Fuels Irradiation and Physics Database (FIPD) [1] is an organized collection of metallic fuel test pin data (U-xPu-yZr, = 0 ~ 28; y = 2 ~ 10) and documentation available to industry. FIPD mainly contains three types of data: (1) Fuel pin fabrication data, including fuel slug diameter, fuel slug length, cladding diameter, smear density, etc. (2) Fuel pin operation conditions, including axial distributions for power, temperatures, fluences, burnup, and isotopic densities, etc. and (3) Fuel pin post-irradiation examination (PIE) data, including fission gas release and gas chemistry, profilometry, and neutron radiography, etc. The operating conditions for pins with PIE data available in FIPD span significant ranges across key parameters. The fuel peak burnup extends from less than 5% up to 20 at%. The cladding peak temperature varies from about 490°C to 660°C. Finally, the cladding peak DPA shows a wide range from less than 5 to 120. These broad ranges reflect the diverse testing conditions and operational parameters captured in the available PIE data. More detail about FIPD can be found in ref. [2]. The database development is an ongoing effort covering metallic fuel experiments from the Experimental Breeder Reactor II (EBR-II) and the Fast Flux Test Facility (FFTF). As reported in the ref. [3, 4], most of the PIE data generated during the IFR program [5] has been collected, reviewed, processed, and integrated into FIPD. The most recently added PIE data can be found in ref. [4], which shows the collection of over 95% of the PIE data by the time of this paper. The recent improvements to the database are summarized in this paper.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Performance of wind assessment datasets in United States coastal areas

The atmospheric dynamics that occur near the intersection of land and water offer exciting and challenging opportunities for wind energy deployment in coastal locations. New models and tools are continually being developed in support of wind resource assessment, and three recent products are explored in this work for their performance in representing characteristics of the wind resource at coastal locations: the Global Wind Atlas 3 (GWA3), the 2023 National Offshore Wind dataset (NOW-23), and the wind climate simulations that are a component of the Wind Integration National Dataset (WIND) Toolkit Long-Term Ensemble Dataset (WTK-LED Climate). These relatively new products are freely available and user-friendly so that anyone – from a utility-scale developer to a resident or business owner – can evaluate the potential for wind energy generation at their location of interest. The validations in this work provide guidance on the accuracy of wind resource assessments for coastal customers interested in installing small or midsize wind turbines (≤ 1 MW in capacity) to support energy needs at the residential, business, or community scale, such as the island and remotely located participants of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. At 23 coastal locations across the United States, dataset performance varies according to different evaluation metrics. All three recent datasets tend to overestimate the observed coastal wind resource. GWA3 produces the smallest annual average wind speed relative errors, whereas WTK-LED Climate is in best agreement in terms of representing diurnal wind speed cycles. NOW-23 is the highest performing of the datasets for representing seasonal and interannual trends in the coastal wind resource. While GWA3 and WTK-LED Climate are relatively insensitive to the dataset output heights selected for wind resource assessment at small and midsize wind turbine hub heights (20–60 m), significant variation in the NOW-23 representation of wind shear across the wind profile in the lowest 100 m of the atmosphere leads to notable differences in wind speed estimates according to the dataset output heights selected for evaluation. GWA3 exhibits challenges in the representation of observed wind speed diurnal cycles at small and midsize turbine hub heights, likely due to the dataset's consistent treatment of hourly wind speed trends regardless of altitude.

17 WIND ENERGY↗
DOE OSTI2024

Quantification of Residual Water in Spent Fuel Dry Storage Canisters Using Mass Spectrometry

The purpose of this report is to document updates on the apparatus to simulate commercial vacuum drying procedures at the Nuclear Energy Work Complex at Sandia National Laboratories. Validation of the extent of water removal in a dry spent nuclear fuel storage system based on drying procedures used at nuclear power plants is needed to close existing technical gaps. Operational conditions leading to incomplete drying may have potential impacts on the fuel, cladding, and other components in the system during subsequent storage and disposal, such as fuel degradation; cladding corrosion, embrittlement, or breaching; and the creation of a flammable environment via radiolysis of water. A general lack of data suitable for model validation of commercial nuclear canister drying processes necessitates well-designed investigations of drying process efficacy and water retention. Scaled tests that incorporate relevant physics and well-controlled boundary conditions are essential to provide insight and guidance to the simulation of prototypic systems undergoing drying processes. This report documents details on the quantification of residual water in the Advanced Drying Cycle Simulator (ADCS), an apparatus built to simulate commercial drying procedures and quantify the amount of residual water remaining in a pressurized water reactor (PWR) fuel assembly after drying. The ADCS was constructed with a prototypic 17×17 PWR fuel skeleton and waterproof heater rods to simulate decay heat. The ADCS is outfitted with thermocouples to measure the thermal response of the ADCS to simulated decay heats and internal helium fill pressures relevant to commercial drying procedures. The ADCS is also instrumented with pressure transducers to measure the pressures and vacuum levels observed during simulated commercial drying. The most unique instrumentation used for quantifying residual water in the ADCS is a Hiden Analytical HPR-30 mass spectrometer (MS), which measures gas compositions of the ADCS internal free volume, based on partial pressures calculated from relative proportions of gas molecules detected by the MS. This report details the methodology used to implement MS measurements in quantifying residual water in the ADCS. This methodology includes the calibration of the HPR-30 MS to a Buck Research Instruments CR-4 chilled mirror hygrometer, which itself is calibrated to a NIST-traceable standard. Data collected by both the MS and the chilled mirror hygrometer from water/helium mixtures ranging from 150 to 500,000 ppmv water in helium were used to generate calibration curves, establishing a source of verification of MS measured water contents. Details regarding water content measurement uncertainties are included in this report, defining the accuracy and verifiability of the HPR-30 MS in measuring residual water content in simulated dry storage canister environments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

High-Fidelity and High-Performance Computational Simulations for Rapid Design Optimization of Sulfur Thermal Energy Storage

Industrial process heating (IPH) accounts for approximately 70% of US manufacturing energy use and is primarily produced by fossil fuel combustion. Approximately 1500 TWht (approximately 60%) of IPH demand is in the temperature range of 100-300. Industrial applications in this temperature range include drying, hydrothermal processing, thermal enhanced oil recovery, food and beverage, bioethanol production, etc. Cost-effective thermal energy storage (TES) that increases the utilization of waste and renewable heat (solar, geothermal, etc.) could provide significant energy savings and reliable heat sources, decrease emissions, and increase US manufacturing competitiveness through reductions in fuel consumption. TES development has historically been dominated by technologies suitable for deployment with concentrating solar power (CSP). State-of-the-art thermal storage deployed commercially with power tower CSP plants uses a 60%/40% NaNO3/KNO3 molten salt and operates between temperatures of approximately 280 degrees Celsius and 570 degrees Celsius using a two-tank configuration. However, these nitrate salts are unsuitable for operation outside of this temperature range due to a high freezing point of approximately 220 degrees Celsius, and limits on high-temperature salt stability and corrosion resistance of containment alloys. Other materials being investigated for TES include those based on: (1) sensible energy storage (various molten salt compositions, inert solid particles, rocks or pebble beds, sulfur, water, concrete, graphite, etc.), (2) latent energy storage in materials that undergo solid-liquid phase change at relevant temperatures (organic materials for low-temperature applications, inorganic salts and/or metals for high-temperature applications), or (3) thermochemical energy storage (hydrides, hydroxides, carbonates, metal oxides, etc.). The application temperature and challenges pertaining to storage material and/or containment cost, energy density, long-term thermal and cyclic stability, and charge/discharge heat transfer effectiveness drive material selection for a given IPH or electricity generation application. Sulfur is a cheap commodity at $80/ton compared to $1100 - 1300/ton for conventional salts. When using a metric of storage cost per kWh, sulfur costs around 2-3 $/kWh. Previous sulfur TES development focused on high temperature (>600 degrees) concentrated solar power applications with sulfur encapsulated in pipes and flow of gaseous HTF (air) in the shell side. However, for lower-temperature IPH applications in the range of approximately 100-300 degrees Celsius Element 16 adopted a compact and scalable TES design with molten sulfur in the shell and HTF pipes submerged in the molten sulfur bath. The low-cost molten sulfur TES for dispatchable IPH has deployment potential for broad applications. The spatial and temporal evolution of the HTF and sulfur temperature is critical to the TES system performance, and thus detailed modeling can improve understanding of the performance and facilitate design improvements. Using high performance computing and computational fluid dynamics (CFD) a low-cost molten sulfur thermal energy storage (TES) system for industrial process heating (IPH) applications was developed. The unique challenges in CFD modeling of sulfur TES are the sharp property changes of sulfur relevant to the working temperatures. Above 159, liquid sulfur undergoes polymerization, and the viscosity of sulfur rapidly increases by several orders of magnitude between 159 degrees Celsius and 188 degrees Celsius, followed by a decrease in viscosity beyond 188 degrees Celsius due to thermal bound dissociation. In addition, various concentrations of H2S impurities can also modify sulfur viscosity. This numerical challenge is especially relevant to transient simulation of the sulfur TES charging and discharging processes as the extreme property variations limit the applicability of traditional heat transfer correlations. Transient CFD simulations including the temperature-dependent sulfur properties and geometric complexity of the TES design were used to predict the effect of natural convection during charging and discharging on the heat transfer process, sulfur temperature uniformity, charge/discharge rates, and performance of the storage devices. The CFD model was validated with experimental results for a full charge and discharge cycle. The work will show 3D and 2D simulation comparisons aimed to facilitate rapid design iterations and a machine learning based design optimization approach.

CFD↗
DOE OSTI2020

Microreactor Agile Nonnuclear Experimental Testbed Test Plan

Microreactors are an attractive technology option for kick-starting nuclear innovation if they can be operated at high temperature, yielding high power conversion thermal efficiencies comparable or better than in commercial light water reactors. Microreactors are currently the smallest variation of Small Modular Reactors (SMRs). SMRs are “newer generation reactors designed to generate electric power up to 300 MWe and whose components and systems can be shop-fabricated and then transported as modules to the sites for installation as demand arises.” (IAEA, 2016). Vendors are developing microreactor designs to provide an affordable, potentially mobile source of electricity - see Fig. 1 for an example of a microreactor on a semi-truck. Various microreactor designs are possible including heat pipe- and gascooled options, which are the focus of the nonnuclear testing described in this document. In heat pipe microreactors, high-temperature heat pipes using liquid sodium or potassium working fluid transport fission heat from the core to a heat removal section which in turn transfers heat to the power conversion system working fluid. In a gas-cooled design, He or other gas will flow through a solid monolith of material and transfer heat as the temperature of the gas increases through a heat exchanger to a power conversion unit. The logistics of all these processes will be examined and tested through a series of articles at the nonnuclear test bed at Idaho National Laboratory (INL), the Microreactor Agile Nonnuclear Experiment Testbed (MAGNET) facility. Microreactors designed to produce power of 0.1-20 MWt offer the potential for more affordable nuclear energy for a range of applications. In a heat pipe microreactor, heat pipes, fuel rods, and/or moderator are intermixed in the reactor core assembly. Heat pipes extend from the core region into the heat removal section where the power conversion unit working fluid flows through holes or channels, transferring heat from the heat pipes to the working fluid. In a gas-cooled microreactor, gas flows through the solid monolith region and up into the heat exchanger region, transferring heat to the working fluid. For initial testing, the heat removal working fluid can be a low pressure gas for testing that addresses thermal stresses. In the final application, heat addition to the power conversion working fluid typically occurs at high pressure, supporting operation of an air-Brayton, supercritical CO 2 (SCO2), or He-recuperated Brayton cycle. Various stages of the steps above will be demonstrated through the tests described in this report.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Initial demonstration of automated fuel performance modeling with 1977 EBR-II metallic fuel pins using BISON code with FIPD and IMIS databases

Using the BISON fuel performance code, simulations were conducted using an automated process to read initial and operating conditions from the Fuels Irradiation and Physics Database (FIPD) and Integral Fast Reactor materials information system (IMIS) database, which contains metallic fuel data from the Experimental Breeder Reactor-II (EBR-II). This work demonstrates use of an integrated framework to access the vast majority of EBR-II experimental fuel pin data to support rapid development of fuel performance models for next-generation metallic fuel systems. With this capability, validation for fuel qualification can be performed rapidly. Between IMIS and FIPD, there is enough information to conduct 1977 unique EBR-II metallic fuel pin histories from 24 different experiments, at varying levels of detail between the two databases. Each of these histories includes a high-resolution power history, flux history, coolant channel flow rates, and coolant channel temperatures. Fission gas release (FGR), cumulative damage fraction (CDF), fuel axial swelling, cladding profilometry, and burnup were all simulated in BISON. The results were compared to post-irradiation examination (PIE) results for the initial demonstration of automated BISON modeling. BISON simulations conducted with IMIS and FIPD were in rough agreement with PIE measurements and calculations. Cladding profilometry, FGR, and fuel axial swelling were found to be in rough agreement with PIE measurements, depending on the physics used within the BISON input files. Here, the mechanical contact solver chosen was found to significantly impact axial fuel swelling and cladding strain predictions. CDF values were assessed to see whether pin failure may have been predicted (CDF ≥ 1). This work suggests that continued development of an automated tool for BISON should focus on inclusion of the Fast Flux Test Facility (FFTF) experimental data for a larger database for metallic fuel, improved physical models to better capture fuel performance, such as fuel-cladding interactions, and a more detailed comparison with available PIE data to further the BISON model development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Small Reactors in Microgrids: Technology Modeling and Selection (Net-Zero Microgrid Program Project Report)

This report demonstrates the capabilities of the net-zero microgrid (NZM) Xendee platform for modeling an SR module with electricity, heat extraction and thermal storage in microgrids configurations. The model effectively captures the most important technical and economic considerations for SR technology specific analysis: cost and operational characteristics of SR technology and financial costs and incentives. The model can analyze multiple scenarios to establish metrics for cost-competitive and zero-carbon microgrids connected to the grid or completely isolated. The model is fully integrated within the Xendee platform for modeling and analysis of clean energy microgrids with storage and generation from renewable energy sources. The model captures the capabilities, constraints, and nuances of SR by incorporating parameters related to plant economics, design efficiency and performance, plant operation and component and fuel lifespan. The cost and operational parameters modeled in the SR module are specific to the technology selected for integration in the microgrid. Cost parameters recognize advanced nuclear technology for modular production and installation based on economies of scale from factory manufacture and related commissioning, and cost reduction through technology maturation—first-of-a-kind (FOAK) and nth-of-a-Kind (NOAK). The cost parameters include installation, operations and maintenance (O&M), fuel refueling cycle, and reactor life. Installation cost reflects economies of scale due to unit sizing at scale and colocation. O&M economies of scale for both fixed- and variable-cost fuel life-cycle costs are incurred at every refueling interval, with separate front- and back-end fuel costs, as well as waste-handling and disposition costs. This report investigates key characteristics of different SR technologies suitable for microgrid applications, including design principles, sizing, coolant properties, temperature ratings, fuel structures, and life-cycle considerations. This also includes fuel technologies applicable to these SR systems, alongside strategies for nuclear-waste and spent-fuel management and approaches to address safety, security, and proliferation challenges. Four primary groups of SR technologies are examined: water-cooled, liquid-metal-cooled, high-temperature gas-cooled, and molten-salt-cooled systems. In this report, an initial guideline for technology selection is established, aligning the characteristics of the technologies with the requirements of microgrids. The selection of technology in a microgrid is influenced by various factors, including financial capacity, location and accessibility, demand type and characteristics, reliability and resilience requirements, area constraints, and the lifespan of the microgrid. The types of electrical and non-electrical applications within the microgrid also play a significant role in technology selection. The characteristics of SRs, such as their smaller size, modularity, transportability, long refueling interval, improved safety features, ability to operate in autonomous or semi-autonomous mode, and provision of high-grade heat, are particularly appealing for microgrids. Furthermore, a list of considerations for implementing SRs in microgrids is outlined. The SR model is created to be continuously improved with the acquisition of actual data on investment and operational costs, experience with supply chains, production at scale, and field deployments. In the near term, performance data on applications in microgrids will become available from lessons learned from laboratory tests, such as those planned for the Microreactor Applications Research Validation and Evaluation Project (MARVEL), led by Idaho National Laboratory (INL). The SR model incorporates scenario data and known SR design specifications, enabling technoeconomic analysis for SR deployment in microgrids. It specifically considers the distinctive attributes of SRs as generators in technoeconomic studies. SRs can be modeled and analyzed with generation from renewable-energy sources, energy storage, and flexible loads over a range of functionality and applications. This offers a comprehensive tool for feasibility studies, scenario development, and sensitivity analysis for “what-if” consideration of any range of assumptions about SRs in microgrids and other aggregations of distributed-energy resources, including virtual power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Film Cooling Performance Prediction for Air and Supercritical CO 2

The current study explores the possibility of cooling the vanes and blades of a direct-fired sCO 2 turbine using film cooling. The operating conditions of a direct-fired sCO 2 cycle and thermophysical properties of the fluid at those conditions can alter the flow field characteristics of the coolant jet and its mixing with the mainstream. Very little information is present in the literature regarding the performance of film cooling geometries employing supercritical CO 2 . The objective of this study is to estimate the resulting film cooling effectiveness while also capturing the effects of the crossflow-to-mainstream velocity ratio on the coolant jet. Herein, a computational fluid dynamic model is used to study the coolant jet exiting a cylindrical hole located on a flat plate, with the coolant fed by an internal channel. Steady-state Reynolds-averaged Navier–Stokes equations were solved along with the (shear-stress transport) SST k–ω model to provide the turbulence closure. The operating conditions for the direct-fired sCO 2 turbine are obtained using an in-house Cooled Turbine Model. Numerical predictions revealed that the crossflow effects and jet lift-off were more pronounced in the case of sCO 2 when compared to air. Spatial distribution of flow field and cooling effectiveness are presented at different operating conditions.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1995

Analysis of shadowing effects on MIR photovoltaic and solar dynamic power systems

The NASA Lewis Research Center is currently working with RSC-Energia, the Russian Space Agency, and Allied Signal in developing a flight demonstration solar dynamic power system. This type of power system is dependent upon solar flux that is reflected and concentrated into a thermal storage system to provide the thermal energy input to a closed-cycle Brayton heat engine. The solar dynamic unit will be flown on the Russian Mir space station in anticipation of use on the International Space Station Alpha. By the time the power system is launched, the Mir will be a spatially complex configuration which will have, in addition to the three-gimbaled solar dynamic unit, eleven solar array wings that are either fixed or track the Sun along one axis and a variety or repositionable habitation and experiment modules. The proximity of arrays to modules creates a situation which makes it highly probable that there will be varying solar flux due to shadowing on the solar dynamic unit and some of the arrays throughout the orbit. Shadowing causes fluctuations in the power output from the arrays and the solar dynamic power system, thus reducing the energy capabilities of the spacecraft. An assessment of the capabilities of the power system under these conditions is an important part in influencing the design and operations of the spacecraft and predicting its energy performance. This paper describes the results obtained from using the Orbiting Spacecraft Shadowing Analysis Station program that was integrated into the Station Power Analysis for Capability Evaluation (SPACE) electrical power system computer program. OSSA allows one to consider the numerous complex factors for analyzing the shadowing effects on the electrical power system including the variety of spacecraft hardware geometric configurations, yearly and daily orbital variations in the vehicle attitude and orbital maneuvers (for communications coverage, payload pointing requirements and rendezvous/docking with other vehicles). The geometric models of the MIR with a solar dynamic power unit that were used in performing shadowing analyses are described. Also presented in this paper are results for individual orbits for several flight attitude cases which include assessments of the shadowing impacts upon the solar dynamic unit and the solar arrays. These cases depict typical MIR flight attitudes likely to have shadowing impact. Because of the time varying nature of the Mir orientation with respect to the Sun and the lack of knowledge of the precise timing of the attitude changes, strategies must be devised to assess and depict the shadowing impacts on power generation throughout the year. To address this, the best, nominal and worst impacts of shadowing considering a wide possible range of parameter changes for typical mission operation period are shown.

Fincannon, James↗
DOE OSTI2021

Utilizing a Storm-Generating Hotspot to Study Convective Cloud Transitions: The CACTI Experiment

The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign was designed to improve understanding of orographic cloud life cycles in relation to surrounding atmospheric thermodynamic, flow, and aerosol conditions. The deployment to the Sierras de Córdoba range in north-central Argentina was chosen because of very frequent cumulus congestus, deep convection initiation, and mesoscale convective organization uniquely observable from a fixed site. The C-band Scanning Atmospheric Radiation Measurement (ARM) Precipitation Radar was deployed for the first time with over 50 ARM Mobile Facility atmospheric state, surface, aerosol, radiation, cloud, and precipitation instruments between October 2018 and April 2019. An intensive observing period (IOP) coincident with the RELAMPAGO field campaign was held between 1 November and 15 December during which 22 flights were performed by the ARM Gulfstream-1 aircraft. A multitude of atmospheric processes and cloud conditions were observed over the 7-month campaign, including numerous orographic cumulus and stratocumulus events; new particle formation and growth producing high aerosol concentrations; drizzle formation in fog and shallow liquid clouds; very low aerosol conditions following wet deposition in heavy rainfall; initiation of ice in congestus clouds across a range of temperatures; extreme deep convection reaching 21-km altitudes; and organization of intense, hail-containing supercells and mesoscale convective systems. These comprehensive datasets include many of the first ever collected in this region and provide new opportunities to study orographic cloud evolution and interactions with meteorological conditions, aerosols, surface conditions, and radiation in mountainous terrain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Characterization of Aluminum Metal Powders for Plutonium-238 Program

As part of radioisotope thermoelectric generator (RTG) production for power sources in deep space exploration, Oak Ridge National Laboratory (ORNL) produces plutonium-238 ( 238 Pu). To produce 238 Pu, neptunium-237 ( 237 Np) targets are fabricated at ORNL and subsequently irradiated at the High Flux Isotope Reactor (HFIR) at ORNL and the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). Specifically, current flowsheets utilize neptunium dioxide ( 237 NpO 2 ) targets. Aluminum (Al) powder is blended with 237 NpO 2 target material to improve thermal conductivity during irradiation. The current composition of 237 NpO 2 /Al pellets (i.e., cermets, or ceramic–metallics) is 20% 237 NpO 2 , 70% Al, and 10% void space. Al powders utilized by the 238 Pu program are high-fired under vacuum before blending into targets. The primary objective of this work is to measure the physical, chemical, and thermal properties of Al before and after the high-firing process. Properties of interest include the morphology, surface texture, particle size, crystal phase(s), and thermal conductivity of the material. These properties are measured with existing materials characterization equipment at ORNL, including powder x-ray diffraction (pXRD), scanning electron microscopy (SEM) with energy-dispersive spectroscopy (EDS), laser flash analysis (LFA), and thermogravimetric analysis (TGA). A secondary objective of this work is to compare the Al powders before and after the high-firing procedure to determine the effects of high-firing on chemical, physical, and thermal properties and to determine the efficacy of the high-firing process.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Sister Rod Destructive Examinations (FY23) Appendix B: Segmentation, Defueling, Metallographic Data and Total Cladding Hydrogen

As a part of the DOE-NE High Burnup Spent Fuel Data Project, Oak Ridge National Laboratory (ORNL) is performing destructive examinations (DEs) of high burnup (HBU) (>45 GWd/MTU) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called sister rods or sibling rods are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4 (Zirc-4), low-tin (LT) Zirc-4, ZIRLO ® , and M5 ® . The DEs are being conducted to obtain a baseline of the HBU rod’s condition before dry storage and are focused on understanding overall SNF rod strength and durability. Both composite fuel and defueled cladding will be tested to derive material properties. Although the data generated can be used for multiple purposes, one primary goal for obtaining the post-irradiation examination data and associated measured mechanical properties is to support SNF dry storage licensing and relicensing activities by (1) addressing identified knowledge gaps and (2) enhancing the technical basis for post-storage transportation, handling, and subsequent disposition of the SNF. This report documents the status of the ORNL Phase 1 DE activities related to: Rough segmentation (RS), Defueling (DEF), DE.02 optical microscopy (MET), and DE.03, cladding total hydrogen measurements. It is a cumulative update to the FY22 status report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Simulation of a TRISO MiniFuel irradiation experiment with data-informed uncertainty quantification

An irradiation experiment using tristructural isotropic (TRISO) fuel particles and the miniature fuel (MiniFuel) irradiation vehicle was performed in Oak Ridge National Laboratory’s High Flux Isotope Reactor (HFIR) to support development of the Kairos Power fluoride salt–cooled, high-temperature reactor (KP-FHR). Here, this paper describes modeling predictions of temperatures and fuel burnup for the as-built experiment. An uncertainty quantification (UQ) analysis was performed to determine the effect of TRISO particle volume and position on the temperature predictions at various fuel heat generation rates (HGRs). This UQ study utilized fuel kernel position and volume measurements previously collected using X-ray computed tomography (XCT) techniques and Monte Carlo sampling methods to generate fuel compact cases that were then analyzed using a finite element thermal model. The UQ analysis indicated that uncertainty in calculated temperatures caused by varying TRISO particle arrangement is relatively small, even at high fuel HGR. Final predictions of particle temperatures throughout the irradiation are shown to be relevant to KP-FHR normal and off-normal operating conditions and to previous TRISO irradiation experiments. The combination of XCT with UQ analyses will inform post-irradiation examination (PIE) of the irradiated fuel compacts, and these analyses can be used to develop fuel performance models for coated particle fuel forms. Both PIE of separate-effects irradiation data and enhanced fuel performance modeling support accelerated qualification of TRISO fuels for a broad range of advanced reactor applications. The novel approach demonstrated here of measuring TRISO particle configurations with XCT methods and generating representative fuel compacts for finite element modeling and UQ analysis could be leveraged by the broader particle fuel community in the development of other TRISO fuel experiments in which these variables may have a significant impact on key outcomes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗