Performance Comparison of Internal and External Reforming for Hybrid SOFC-GT Applications Using 1D Real-Time Fuel Cell Model
Presented by NETL at ASME Turbomachinery Technical Conference, 06/19
Chen, Hao↗
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GT SUITE Fuel Cell Model Validation with Power Module Test Data
The AES Modular Power Systems (AMPS) Fuel Cell team at the NASA Glenn Research Center (GRC) has created a Microsoft Excel model of a regenerative fuel cell (RFC) system for potential lunar and Martian applications. Due to some limitations of this model, including the ability to predict transient effects in the thermal and fluidic performance of the system, a system-level transient thermal/fluids model was desired. GT-SUITE is a transient multi-physics simulation tool that is popular in the automotive industry and has a built in template for proton exchange membrane (PEM) fuel cells. However, terrestrial fuel cells used in the automotive industry vary significantly from aerospace fuel cells. A validation effort was undertaken to compare GT SUITE model results to actual data collected during the 2015 AMPS Power Module Demonstration in which the AMPS team used a PEM fuel cell system to power a 478 kg rover assembly at the Dunes test site located at GRC. The purpose of the model was to evaluate the efficacy of using GT SUITE to model a non-flow through PEM fuel cell system. The validation effort examined electrochemical performance including fuel cell stack voltage and power supplied, the thermal performance of the stack namely stack temperature and heat rejection rates, and the fluidic performance of the system including pressures, reactant consumption, and water production rates.
Gilligan, Ryan↗
On the effect of mixing-driven vaporization in a homogeneous relaxation modeling framework
The homogeneous relaxation model (HRM) is one of the most widely used models to describe the liquid–gas phase transition in multiphase flows due to the occurrence of cavitation. However, in its original formulation, the HRM does not account for the presence of ambient gas species, which generally limits its applicability to the injector's internal flow where ambient gases are negligible. In this work, a mixing-driven vaporization (MDV) model was developed to extend the capability of the HRM in handling the mixing effect in the regions external to the nozzle, where vapor–liquid equilibrium for multi-species mixtures of fuel and ambient gas is considered. Herein, to assess the model performance, simulations of the Engine Combustion Network's Spray G injector were performed with the HRM and the MDV model under both flash-boiling and evaporating conditions. It was found that the MDV model led to a better match against x-ray measurements of fuel density in the near-nozzle region. In contrast to the HRM, the MDV model was able to reproduce the vaporization process in the mixing zone at the edge of the fuel jet, which aligns with the expected physics. This resulted in substantial differences in the prediction of other flow characteristics such as mixture temperature and pressure. Furthermore, this work demonstrates that evaporation timescales have a considerable effect on the MDV model's predictions, as shown by a parametric study in which a time factor was introduced to mimic the effect of different timescales due to different phase change mechanisms.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Nek5000 Large-Eddy Simulations for Thermal-Hydraulics of Deformed Wire-Wrap Fuel Assemblies
A well-characterized validation program has been defined for the thermal-hydraulic CFD modeling of 61-pin hexagonal packed fuel assemblies with helically wire-wrapped pins for both non-deformed beginning-of-life geometries and deformed later-in-life geometries typical of sodium-cooled fast reactors. The motivation for this work is the need to validate CFD codes to accurately model the flow and thermal performance of wire-wrapped fuel assemblies, particularly the deformed later-in-life bundles. The effect of pin and duct deformation is increasingly important as reactor designs look toward longer assembly lifetimes. As a part of a larger collaborative project with TerraPower, Texas A and M University (TAMU), and AREVA, this validation work consists of CFD simulations of four con-current experiments. Pre-test numerical simulations corresponding to each of the experiments has been conducted and are planned to be followed by post-test simulations that will incorporate test information as it becomes available to account for any as-built differences in geometry of test section and its specifications including the differences in boundary conditions of the actual tests. This paper focuses on Nek5000 LES results for the isothermal/TAMU undeformed configuration as a first step toward and initial conditions for the deformed isothermal/TAMU and/or conjugate heat transfer/AREVA cases. (authors)
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
SHARP Assembly-Scale Multiphysics Demonstration Simulations
The NEAMS Reactor Product Line effort aims to develop an integrated multiphysics simulation capability for the design and analysis of future generations of nuclear power plants. The Reactor Product Line code suite’s multi-resolution hierarchy is being designed to ultimately span the full range of length and time scales present in relevant reactor design and safety analyses, as well as scale from desktop to petaflop computing platforms. In this report, we describe our efforts to integrate thermal/hydraulics, neutronics, and structural mechanics modeling codes to perform coupled analysis of a representative fast sodium-cooled reactor fuel assembly. Over the past five years, the Reactor Product Line effort has developed high-fidelity single-physics codes for neutron transport modeling, in the PROTEUS code, and Large Eddy Simulation-based thermal/fluid modeling in the Nek5000 code. Both these codes have been exercised on over 100,000 processors of the IBM Blue Gene/P. The Diablo code has been used to perform structural mechanics and thermo-mechanical modeling. MOAB, the Reactor Geometry Generator (RGG), and MeshKit have been developed to generate and manipulate mesh and mesh-based data, in both serial and parallel environments. These tools together form a strong basis on which to build a multi-physics modeling capability. The goal of developing such a tool is to perform multi-physics neutronics, thermal/fluid, and structural mechanics modeling of the components inside a reactor core. While the focus of this report is on modeling a fast sodium-cooled reactor, it is also the goal that this simulation tool be useful for most reactor types. Here we report on the integration of PROTEUS and Nek5000 into the NEAMS framework. Both codes have been modified to read and write data from and to the MOAB library, in both serial and parallel environments. We focused on broad support for as many original physics code options as possible, rather than focusing only on near-term needs. Both codes were demonstrated through this interface on a relatively simple, seven-pin hexagonal fuel assembly. To support coupled analysis, the new multi-physics driver library COUPE was developed; this library provides flexible support for both loose and tight coupling, and can interface flexibly with codes in multiple programming languages. To couple results between the separate meshes used in each physics, the driver, PROTEUS, and Nek5000 all interface with the MBCoupler solution transfer tool provided with MOAB. Together, these tools form the basis of a new, coupled neutronics and thermal/fluid modeling code, that works in both serial and parallel. In a related effort, a driver in the Diablo code was used to access Nek5000-computed results written to a MOAB database, as well as the temperature data used to drive structural mechanics computations.
Tautges, Timothy J.↗
Machine learning-based surrogate models and transfer learning for derivative free optimization of HT-PEM fuel cells
Widespread adoption of high-temperature polymer electrolyte membrane electrochemical systems, such as fuel cells (HT-PEMFCs), requires models and computational tools for accurate optimization and guiding new materials for enhancing performance and durability. In this contribution, knowledge-based modelling and data-driven modelling are combined using Few-Shot Learning and implementing an Automated Machine Learning framework for the generation of Machine Learning-based surrogate models. Applicability of the resulting model for derivative-free optimization is demonstrated. Additionally, a way of considering extrapolation in the optimization task is presented. Results show that although extrapolation is needed to achieve better solutions during optimization, it can be monitored and managed. As a result, tuning the electrode ionomer binder's properties, such as ionic conductivity, in the fuel cell represents a promising pathway for improving HT-PEMFC performance.
08 HYDROGEN↗
Review of the Oak Ridge National Laboratory (ORNL) Neutronic Calculations Regarding the Conversion of the High Flux Isotope Reactor (HFIR) to the Use of Low Enriched Uranium (LEU) Fuel
In awareness of the risk caused by the proliferation of nuclear materials, the international community initiated in 1978 the Reduced Enrichment for Research and Test Reactors (RERTR) Program. The goal of the RERTR program is to seek solutions to convert the reactors using High Enriched Uranium (HEU) fuel ( 235 U/U ≥ 20 wt. %) to the use of Low Enriched Uranium (LEU) Fuel ( 235 U/U < 20 wt. %). Among the 200 reactors worldwide currently in the scope of the program, about 70 have been converted or shut down prior to conversion. In the U.S., six high reactors remain to be converted. One of the U.S. reactors is the High Flux Isotope Reactor (HFIR) located at Oak Ridge National Laboratory (ORNL). This multipurpose reactor is mainly used for neutron scattering experiments and isotope production. It has two fuel elements made of involute-shaped fuel plates. The ORNL Research Reactor Division (RRD) has prepared during FY2011 two series of calculations, one neutronic and one thermal-hydraulic (TH), in support of the conversion activities for HFIR. The neutronic calculations cover mainly the evaluation of the matrix of performance (cycle length and flux), the distribution of power in the fuel elements, kinetics parameters and reactivity coefficients. As defined by the RRD procedures, the calculations have to be reviewed. Per internal decision, the RRD proposed an external reviewer, Argonne National Laboratory (ANL). The results of the Argonne neutronic review are presented in this report. The results of the TH review are presented in a separate report. The main conclusion of the ORNL reactor physics analyses is that the proposed reference HFIR LEU core (using the so-called monolithic UMo fuel) could maintain the current level of performance (obtained with a HEU core at 85MW) if it is operated at 100MW. However, to operate within the same safety limits as the current HEU core, the thermal hydraulic analyses show that the peaking of the power distribution occurring at the bottom of the fuel plates has to be reduced. The RRD has found a potential solution by reducing the fuel thickness at the bottom of the fuel plate. This would require addition of an axial grading to the already radially-graded fuel. Thus, the review has been focused on independently recalculating all of the ORNL calculations and making an as fair as possible comparison between the current 85MW HEU core and the reference 100MW LEU core design. ORNL and ANL have used the same computational tools to perform the work. The codes MCNP and ORIGEN have been used to perform the steady state and depletion calculations, respectively. The communication between the two codes has been carried out by the code VESTA. Even though the code MCNP is able to handle complex geometries, it cannot model the involute shape. Nonetheless, ANL has developed an innovative methodology to model the involute shape with MCNP with an approximation that can be made as accurate as desired. Thus, ANL has been able to perform the calculations using an explicit (or discrete) description of the fuel elements. The current modeling used by ORNL consists in homogenizing the different fuel element components (cladding, fuel, coolant…). Although this method is easier to implement, it may also affect the reactor physics which may have an impact on the calculated results. Even though some differences in the physics parameters (flux for instance) have been detected between the HEU explicit (ANL) and homogenized (ORNL) models, they are small enough to not significantly impact the performance parameters. However, this is not the case for the LEU core where the differences in the physics parameters lead to significant differences in the performance parameters, especially the cycle length (found to be considerably longer in the explicit model). Nonetheless, the others parameters calculated by ANL (power distribution, kinetics parameters…) are in good agreement with those calculated by ORNL. Some inconsistencies have been detected in the original ORNL inputs that have been corrected in the reference ones used by ANL for the review and, therefore, the cycle length difference between the LEU homogenized and explicit models has been amplified. Consequently, the ANL comparison of the HFIR HEU and LEU cores has given different results than the ORNL results. The ORNL matrix of performance for the 100MW LEU reference design shows that the reactor cycle length would be as long and the flux as high as the current 85MW HEU core. In contrast, the ANL matrix of performance shows that the 100MW LEU design would have a significantly longer cycle length (~33days) than the current 85MW HEU core (~25days). In addition, it has been shown that, if the reactor is operated for 33days, the neutron fluence would be high enough to increase the overall reactor performance by a significant factor. If the reactor is forced to shut down after 25days, calculations show that the neutron flux would be high enough to maintain the performance of the neutron scattering experiments but losses are predicted to occur in several irradiation facilities. Increasing the cycle length may not be possible for other reasons. For instance, a longer cycle length can increase the cumulative oxide layer thickness growth on the plate. The increase in the neutron fluence may affect the lifetime of the reactor vessel and, by reducing the time for maintenance, pose operational challenges. An assessment of these as well as other potential questions associated with a 33 day cycle length is needed. Since such an assessment has not been carried out, ANL cannot conclude that the ORNL reference LEU core should or could operate at 100MW. The conclusions of the ANL review are different than those of the ORNL investigation mainly because the explicit and homogenized methodologies used to model the core are different. In this report, an attempt is made to justify and explain as clearly as possible why the ANL methodology is thought to be better suited for HFIR analyses. It is acknowledged that because the ANL and ORNL LEU models have not been benchmarked against experiments, it is possible that doubts could be raised concerning the ability of the models to correctly capture all of the physics behavior of the reactor.
Bergeron, A.↗
Design and validation of a gamma-ray scanning system for measuring irradiated nuclear fuel
We report a prototype gamma-ray scanning system was designed to perform post-irradiation measurements of nuclear fuel at Idaho National Laboratory (INL). The system is composed of a coaxial high-purity germanium (HPGe) detector, a collimator, and mechanical positioning stages that translate the fuel sample across the front of the collimator. This paper documents the design process, modeling and the laboratory characterization performed to validate the system. The modeling of the system was done using the Monte Carlo N-Particle code with vendor-supplied design specifications, dimensional measurements, and x-ray radiographs of the HPGe detector. The characterization included measurements of calibrated radiation sources in different geometries, and calculations for absolute efficiency and dead time. Benchmark measurements were performed scanning an irradiated fuel rodlet containing ten pellets of 4.9% enriched UO2 in zirconium alloy. These results were compared to simulated spectra to further help characterize the detector model. One goal of the system is to determine the number of fissions per gram of UO2 in the fuel; comparison of the results from this prototype system's assay of the irradiated fuel rodlet are in agreement with previously estimated results.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Advanced two-phase subchannel method via non-linear iteration
A fast-running, robust two-phase flow, sub-channel model is presented based on non-linear solution of the steady-state subchannel fluid flow equations. The drift-flux model solves for conservation of liquid and vapor mass, mixture energy, and axial and transverse mixture momentum as part of an efficient planar marching scheme and nonlinear, nested outer and inner iteration. Here, models based on mechanistic subcooled boiling, two-phase turbulent void mixing, and drift are included. Solution verification and mesh convergence studies were performed for modern GE 10 × 10 fuel geometry and are shown to have excellent convergence behavior. Run time performance for a 50 axial mesh model showed 2.2 seconds on a single CPU core to tightly converge all 3D distributions (flow, void, pressure) for the GE 10 × 10 fuel geometry, supporting its efficient use within the Virtual Environment for Reactor Applications boiling water reactor framework.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Derivation of effective fission gas diffusivities in UO2 from lower length scale simulations and implementation of fission gas diffusion models in BISON
This report summarizes the development of new fission gas diffusion models from lower length scale simulations and assessment of these models in terms of annealing experiments and fission gas release simulations using the BISON fuel performance code. Based on the mechanisms established from density functional theory (DFT) and empirical potential calculations, continuum models for diffusion of xenon (Xe) in UO 2 were derived for both intrinsic conditions and under irradiation. The importance of the large X eU 3 O cluster (a Xe atom in a uranium + oxygen vacancy trap site with two bound uranium vacancies) is emphasized, which is a consequence of its high mobility and stability. These models were implemented in the MARMOT phase field code, which is used to calculate effective Xe diffusivities for various irradiation conditions. The effective diffusivities were used in BISON to calculate fission gas release for a number of test cases. The results are assessed against experimental data and future directions for research are outlined based on the conclusions.
36 MATERIALS SCIENCE↗
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↗
Predicting Large Deflections of Multiplate Fuel Elements Using a Monolithic FSI Approach
As part of the Global Threat Reduction Initiative, the Oak Ridge National Laboratory is evaluating conversion of fuel for the High Flux Isotope Reactor (HFIR) from high-enriched uranium to low-enriched uranium. Currently, multiphysics simulations that model fluid-structure interaction phenomena are being performed to ensure the safety of the reactor with the new fuel type. A monolithic solver that fully couples fluid and structural dynamics is used to model deflections in the new design. A classical experiment is chosen to validate the capabilities of the current solver and the method. Here, a single-plate simulation with various boundary conditions as well as a five-plate simulation are presented. Finally, use of the monolithic solver provides stable solutions for the large deflections and the tight coupling of the fluid and structure and the maximum deflections are captured accurately.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Development of Machine-Learned Interatomic Potentials to Predict Structure, Transport, and Reactivity in Platinum-Based Fuel Cells
Machine-learned interatomic potentials (MLIPs) have rapidly progressed in accuracy, speed, and data efficiency in recent years. However, training robust MLIPs in multicomponent systems remains a challenge. In this work, we train an MLIP to describe hydrated Nafion ionomers and platinum catalysts, which are important components of fuel cells, by constructing a diverse training set to describe the bulk polymer and interfacial catalyst–polymer interactions well. We use our trained MLIP to study the properties of the platinum–Nafion system, including polymer structure, proton mobility in a bulk Nafion polymer and near a platinum-Nafion interface, and reactions near and far from the interface, finding excellent results for structure and reactions contained within our training set. Transport seems to be well described, with both vehicular transport and Grotthuss hopping captured, although converged calculations of diffusivities were not computed because they require calculations of tens of nanoseconds that are challenging with current state-of-the-art MLIPs. The combined insights that this model provides can be leveraged to optimize fuel cell performance, and the approach can be applied to other chemical processes and devices where structure, transport, and reactivity all contribute to the overall observed performance.
33 ADVANCED PROPULSION SYSTEMS↗
Parametric Optimization Problem Formulation for Connected Hybrid Electric Vehicles using Neural Network based Equivalent Model
The dynamics of powertrain control systems are complicated and involve both nonlinear plant model and control functionalities, albeit they are well defined and formulated using first principle approaches. This constitutes difficulties in exploring implementable optimal tuning rules for some selected control parameters using vehicle-to-vehicle (V2V) communications. This paper presents a way to use neural networks (NN) to represent the problem of parameter tuning for optimizing fuel consumption. For this purpose, physical modelling and validation have been firstly performed for the closed loop powertrain system of the concerned vehicle for some given driving cycles. This is then followed by the sensitivity analysis that selects most influential control parameters to optimize. Using the data generated from the obtained physical models, an equivalent NN formulation has finally been obtained that gives simple yet unified objectives and constraints ready to be used to solve the optimization problem that produces optimal tuning rules for the selected control parameters to minimize fuel consumption.
Parameter optimization, V2X, Powertrain modeling↗
Thermal Modeling of the TN-32B Cask for the High Burnup Spent Fuel Data Project
This report describes thermal modeling done for the High Burnup Dry Storage Research Project that is being performed by the U.S. Department of Energy’s Spent Fuel and Waste Science and Technology (SFWST) research and development (R&D) program. The purpose of this project to investigate the performance of high-burnup spent nuclear fuel in dry storage. Part of this project is a demonstration test being performed with a storage module in the North Anna Nuclear Power Station’s Independent Spent Fuel Storage Installation (ISFSI). The storage module selected for this demonstration is an Orano TN-32B High Burnup cask (Fig. S-1) which has been loaded with fuel assemblies from the North Anna spent fuel pool. The main goals of this test are to provide confirmatory data for model validation and potential improvement, support license renewals and new licenses for ISFSIs, and support transportation licensing for high burnup spent nuclear fuel (EPRI 2014).
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Analyzing machine learning models to accelerate generation of fundamental materials insights
Abstract Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data.
36 MATERIALS SCIENCE↗
Evaluation of flow-induced plate deflection for University of Missouri research reactor low-enriched uranium fuel element
The University of Missouri Research Reactor (MURR), located on the campus of the University of Missouri in Columbia, Missouri, is one of the six United States (U.S.) High Performance Research Reactors (USHPRR), including one critical facility, that are actively collaborating with the U.S. Department of Energy (DOE) National Nuclear Security Administration (NNSA) Office of Material Management and Minimization (M3) Reactor Conversion Program to convert from highly enriched uranium (HEU, ≥20 wt% U-235) fuel to low-enriched uranium (LEU, <20 wt% U-235) fuel. A new type of very high-density LEU fuel based on a monolithic alloy of uranium and 10 wt% molybdenum (U-10Mo) is expected to allow conversion of some USHPRR, including MURR. In the design of its fuel elements, MURR is using thin parallel curved fuel plates separated by coolant channels. In this work, fluid-structure interaction (FSI) analysis of the MURR LEU fuel element is performed at the element level (as compared to the plate level analysis), which models all components of the LEU fuel element, including fuel plates and the supporting structures. Therefore, the effect of supporting structures on the flow distribution within the element and the fuel plate deflection are evaluated. In addition to the element nominal flow rate and dimensions, the tolerances in the geometry of the coolant channel and plate thickness, the effect of a comb on plate deflection, and the uncertainty of the flow rate per element are evaluated. For the LEU fuel plates, which are thinner than the current HEU plates, the predicted plate deflection is found to be small compared to the fabrication and assembly tolerances. Thus, the FSI-induced deflections are not expected to noticeably reduce the coolant flow rate or predicted safety margins in the limiting channels for the MURR LEU fuel element. In addition to the simulation work, a hydraulic performance test of the MURR LEU fuel element is currently being planned to support conversion to the use of LEU fuel.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
A Fast Reactor Irradiation Experiment Design in the ATR
Modern modeling techniques were used to investigate a proposed method for fast neutron irradiations in an existing thermal-spectrum reactor, the Advanced Test Reactor (ATR). This method builds upon pre-existing ideas, where fast flux is increased by surrounding the specimens with fissionable “booster fuel” but diverges from historical approaches by using an already developed fuel element design used in the Belgian Reactor 2 (BR2) as the booster fuel while leveraging modern 3-D modeling and simulation techniques. Design evaluations and neutronics simulations were performed to evaluate the performance of a BR2 fuel element irradiated in an ATR flux trap with test pins in the central channel of the BR2 fuel element. These efforts have yielded promising results. Adding a BR2 fuel element in the northeast (NE) flux trap of ATR was predicted to result in a 150% increase to the incident fast neutron flux with a fast (>0.1 MeV) to thermal (<0.625 eV) neutron flux ratio ranging from approximately 50 to 150, dependent on the material used for thermal neutron filtering and volume of moderator within the central channel of the BR2 element. The predicted annual fast neutron fluence (>0.1 MeV) ranges from 7.9 × 1021 to 9.1 × 1021 n/cm2. Given the relatively large fast to thermal neutron flux ratio, the calculated radial power profiles within 4.3 mm outer diameter U10Zr fueled specimens irradiated within the BR2 booster fuel element are adequate representations of those within fast neutron reactors. The predicted radial power profiles are not flat, but they are more prototypic than those seen in advanced fuels tests which began in ATR in 2003. Another distinct advantage of this experiment design is that full-scale test pins can be irradiated to augment the ongoing series of reduced scale advanced fuels tests. The proposed experiment design irradiated within a BR2 fuel element in a flux trap of ATR offers an improved alternative to the current testing of advanced reactor fuels in ATR. Selected results from this design evaluation are presented.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗