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NREL, Hewlett Packard Enterprise, Daimler, and Power Innovations are demonstrating a proof-of-concept system that integrates a hydrogen-powered fuel cell with IT racks to provide renewable power to a data center.
Characterization data and comparisons of these data are presented for three individual lots of a research test fuel designated as an Experimental Referee Broadened Specification (ERBS) aviation turbine fuel. This research fuel, which is a blend of kerosene and hydrotreated catalytic gas oil, is a representation of a kerojet fuel with broadened properties. To lower the hydrogen content of the ERBS fuel, a blending stock, composed of xylene bottoms and hydrotreated catalytic gas oil, was developed and employed to produce two different ERBS fuel blends. The ERBS fuel blends and the blending stock were also characterized and the results for the blends are compared to those of the original ERBS fuel. The characterization results indicate that with the exception of the freezing point for ERBS lot 2, which was slightly high, the three lots, produced over a 2 year period, met all general fuel requirements. However, although the properties of the fuels were found to be fairly consistent, there were differences in composition. Similarly, all major requirements for the ERBS fuel blends were met or closely approached, and the properties of the blended fuels were found to generally reflect those expected for the proportions of ERBS fuel and blending stock used in their production.
This report serves as an introduction, tutorial, and benchmark specification for out-of-pile tests on metallic fuel. It introduces a new user to the EBR-II legacy fuel performance test program and the fast reactor fuel performance databases built to preserve the records. It then details the information stored in each database and how to find it. A benchmark specification is included for a small set of out-of-pile tests on U-10Zr fuel to function as a tutorial demonstrating how the legacy fuel performance data sets stored in the FIPD and OPTD databases can be used together to benchmark fuel performance models for steady-state and transient performance.
In this study, the elastic properties, structural parameters, sound velocity, and Debye temperature of γ–(U,Zr) were computed using ab initio molecular dynamics (AIMD) at temperatures between 1000 K and 1400 K and for Zr content between 0 at. % and 100 at. %. UZr is used as a metallic fuel for Sodium Fast Reactors (SFRs). The study of the mechanical and thermal behavior of these alloys leads to a better data-informed fuel design. The bulk modulus, shear modulus, Young's modulus, and Poisson's ratio were calculated from the elastic constants and their dependence on Zr content and temperature was investigated, comparing the results with previous computational work and the available experimental data in the literature. Interfacial properties between UZr (up to 32 at. % which typically exists in nuclear fuel) and liquid sodium are also of interest due to the presence of a sodium bond between the fuel and the cladding in metallic nuclear fuel. The interfacial energy between γ–(U,Zr) and liquid sodium, the surface tension of liquid sodium, and the work of adhesion were computed at different temperatures and Zr concentrations. It was demonstrated that γ–(U,Zr) is completely wetted by liquid sodium at all the investigated temperatures and Zr concentrations. Finally, this work provides the basis for the determination of interfacial resistances in SFRs and their implementation into heat transfer fuel performance simulations, which will be the subject of future work.
Fuel is a major cost expense for air carriers. A typical airline spends 10% of its operating budget on the purchase of jet fuel, which even exceeds its expenditures on aircraft acquisitions. Thus, it is imperative that fuel consumption be managed as wisely as possible. The implementation of Flight Operations Quality Assurance (FOQA) programs at airlines may be able to assist in this management effort. The purpose of the study is to examine the literature regarding fuel consumption by air carriers, the literature related to air carrier fuel conservation efforts, and the literature related to the appropriate statistical methodologies to analyze the FOQA-derived data.
Advanced reactor concepts currently being developed throughout the industry are significantly different from light water reactor (LWR) designs with respect to geometry, materials, and operating conditions, and consequently, with respect to their reactor physics behavior. Given the limited operating experience with non-LWRs, the accurate simulation of reactor physics and the quantification of associated uncertainties are critical for ensuring that advanced reactor concepts operate within the appropriate safety margins. Nuclear data are a major source of input uncertainties in reactor physics analysis. As part of an ongoing project at Oak Ridge National Laboratory, the effects of nuclear data uncertainties on key figures of merit associated with advanced reactor safety are being assessed for selected advanced reactor technologies. Key nuclear data relevant for reactor safety analysis for each selected advanced reactor technology were identified in Phase 1, and their impact on important key figures of merit was assessed in Phase 2. This report describes the outcome of Phase 3. Available benchmarks and fuel irradiation data for use in evaluating the impact of uncertainties and gaps in nuclear data that impact reactivity control for advanced reactor designs through the fuel cycle were identified and assessed. Benchmarks were identified by searching (1) the Organisation for Economic Co-operation and Development (OECD)/Nuclear Energy Agency (NEA) International Criticality Safety Benchmark Evaluation Project (IRPhEP) handbook, (2) the OCED/NEA International Reactor Physics Experiment Evaluation Project (IRPhEP) handbook, (3) ongoing OECD/NEA benchmark activities, and (4) documentation in public literature. Relevant benchmarks were identified by selecting reactors with geometry, materials, and neutron energy spectra similar to those of selected advanced reactor technologies. This assessment identified six benchmarks, of which three are experimental and three are purely computational. One experimental and one computation benchmark contain depleted fuel; all others are limited to fresh fuel. This report provides short descriptions of the selected benchmarks along with the availability of measured data for comparison.
Historical data collection on nuclear fuel cladding materials has focused on generating a statistically significant amount of data to assess the material and its failure behavior. Furthermore, data generated to support material model and failure criteria development were previously posttest evaluations, so a large number of tests was required to gain new understanding. A way to expedite this process is to develop techniques capable of generating large, high-fidelity data sets from a single test with lower uncertainty or quantified uncertainty. One such example of this approach is Oak Ridge National Laboratory’s use of modified burst tests (MBTs) to analyze the mechanical behavior and failure conditions of cladding during a simulated reactivity-initiated accident (RIA). Each test incorporates digital image correlation (DIC) analysis techniques that are used to assess the accumulated strain in situ, as well as eventual cladding failure. This work has been fruitful in defining strain-to-failure conditions for materials like silicon carbide (SiC) fiber–reinforced/SiC matrix composite tubes (SiC/SiC), iron-chromium-aluminum (FeCrAl) alloy tubes, and chromium-coated Zircaloy-4 tubes. However, there are numerous DIC software available, including open-source and proprietary software. The different DIC software use various algorithms to process images and calculate displacement values. Using these different software and algorithms can lead to varying results, and perhaps larger-than-expected uncertainties. In the present study, previously published MBT data encompassing a variety of test conditions were reanalyzed with two different DIC software to assess the variance in the calculated strain results. The data consisted of SiC/SiC, FeCrAl, and chromium-coated Zircaloy-4 tubes. Plots of the calculated strains during the transient revealed good agreement between the two DIC software. The average root-mean-square errors between the two software was 0.20% strain, which is slightly larger than a previously reported error value for these tests. In conclusion, this variance in results is low enough that this analysis method can be used for code validation.
High-Performance Computing (HPC) and Leadership-Class Supercomputing are driving forces behind scientific advancements, enabling researchers to tackle complex challenges in physics, chemistry, biology, and engineering. These systems power vast simulations and data analyses, fueling discoveries in fields ranging from materials science to climate modeling. However, their use often involves processing sensitive data—such as proprietary industry simulations, biomedical records, and national security computations—posing significant privacy concerns. In conclusion, this issue is amplified in collaborative environments like Department of Energy (DOE) user facilities, where HPC resources are shared across institutions to foster innovation.
This paper presents the combustion dynamic data and fuel modulation response of a three-cup Lean Direct Injection combustor developed by Woodward, FST. The test was conducted at the NASA Glenn Research Center CE-5 flame tube test facility. The facility provided inlet air up to 922 K and pressure up to 19.0 bar. At the low-power configuration, the combustion noise was quiet. Large combustion pressure oscillations were observed with the High-power configuration at an off design condition, with low inlet air temperature and pressure conditions and a high equivalence ratio (about T3=600 K, P3 = 800 kPa, and ER =0.46). The noise amplitude was as high as 1.5 psi at around 220 Hz. As inlet air pressure and temperature increased, this combustion instability decreased. Fuel modulated signals were produced with the WASK fuel modulator located in the fuel line upstream of the center cup pilot fuel-air mixer. The amplitudes of the modulated signals detected in the combustor were low. Only less than 0.13% (0.06 psi) of the input energy was detected, and the signal amplitudes decreased as the modulated frequencies increased. Interaction between the modulated signals and the combustion noise varied with operating conditions. At a condition with low combustion noise around 150 hz, modulating a signal at around the same frequency would increase the combustion noise from 0.2 psi to as high as 0.6 psi, whereas at a condition with a high combustion instability around 250 hz, the modulated signal did not seem to have much effect on the combustion noise.
When the Halden Boiling Water Reactor closed down in 2018, a need to restore the capability for in-reactor power ramp testing arose. Such testing is valuable for studying pellet-clad interaction phenomena in nuclear fuels. The data from these studies is of great interest to a number of research programs, including the accident-tolerant fuel (ATF) program at Idaho National Laboratory (INL). In 2022, Woolstenhulme et al. proposed several power ramp testing ideas using facilities at INL, including irradiation in the Transient Reactor Test Facility (better known as TREAT) and the Advanced Test Reactor (ATR) [1]. Worrall et al. [2] and Labossiere-Hickman et al. [3] subsequently performed feasibility studies for the ATR testing options in 2023. This summary further investigates the three-pin trefoil design (Fig. 1) for the proposed ATF-2Ramp Experiment discussed in Labossiere-Hickman et al. [3]. ATF-2Ramp is designed to operate in the center flux trap (CFT) of the ATR during a powered axial locator mechanism (PALM) cycle: a short, variable-powered cycle with an asymmetric power distribution. Previously, it was shown that tailoring the thickness of the hafnium (Hf) neutron shields (“mini-shrouds”) surrounding each pin offered a degree of control sufficient to achieve the programmatic linear heat generation rate (LHGR) targets for ATF-2Ramp during the high-power period of a PALM cycle. New work involves shortening the experiment test train for consistency with the fuel pins in ATF-2D [4] and then shaping the axial power profile of the three test pins.
The CASL reactor simulation package VERA has been adapted to provide high-fidelity simulation capabilities for modeling source range detector response during subcritical reactor configurations. New features include the activation and shuffling of secondary-source assemblies, use of burned fuel neutron emission data from the ORIGEN depletion solver to the MPACT deterministic neutron transport solver, allowance of user-defined sources in MPACT based on material composition, ability to solve the subcritical source-driven system with neutron multiplication using the MPACT diffusion solver, and transfer of the calculated fission source from MPACT to the continuous-energy Monte Carlo solver Shift for final detector response evaluation using the CADIS methodology for variance reduction. These new capabilities were benchmarked against Watts Bar Unit 1 plant operating data for the first few fuel loading steps and were found to demonstrate excellent agreement with the measured data.
Most nuclear fuels irradiations at Oak Ridge National Laboratory (ORNL) over the past decade have been conducted using MiniFuel—a static capsule design employing subscale fuel specimens to collect separate-effects irradiated fuel performance data. Irradiation conditions for MiniFuel experiments are predicted pre-test using reactor physics, and thermal models are verified post-irradiation via SiC dilatometry and various spectrometry methods. Relevant fuel performance parameters are also observed post-irradiation in a hot cell, thereby providing a single data point for each parameter representing the cumulative effects of the irradiation conditions. Substantially more data can be harvested from a single test and within a shorter duration by instrumenting irradiation vehicles and measuring desired quantities in situ. This report presents the design and analysis of the MiniFuel INstrumented Irradiation Test Apparatus for Understanding Radiation Effects (MINITAURE)—an instrumented test rig based on the separate-effects MiniFuel concept that aims to capture fission gas release (FGR) and thermal conductivity degradation of fuel specimens during irradiation in the High Flux Isotope Reactor (HFIR). MINITAURE will be integrated with the Materials Irradiation Facility (MIF) located in the HFIR building outside the reactor containment. The MIF will act as the instrumentation and control center for the experiment, enabling real-time feedback from in situ sensors and control of irradiation temperatures via a gas delivery system. Two unique capsule designs were developed to capture each phenomenon: the thermal conductivity capsule, which uses a thermopile method to estimate fuel specimen thermal conductivity, and the fission gas release capsule, which will have continuous flowing gas communication to high-purity germanium detectors that are housed in the MIF for monitoring FGR. This report details the reactor physics and heat transfer modeling activities that were used to inform the experiment design and predict capsule performance. It also describes out-of-pile activities conducted to stand up this new capability and verify the measurement techniques. Modeling efforts to date have demonstrated the feasibility of the in situ measurement techniques and supported the development of the MINITAURE assembly configuration. Out-of-pile testing of the thermal conductivity measurement shows promise in capturing relative changes in thermal conductivity. However, significant errors exist in the measured absolute value, posing a need for further refinement.
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.
The Advanced Gas Reactor (AGR) Fuel Development and Qualification Program was established in 2002 to conduct research and development on tri-structural isotropic (TRISO)-coated particle fuel for High Temperature Gas-cooled Reactors (HTGRs). All AGR irradiations have been completed: AGR-1 (Collin 2015), AGR-2 (Collin 2014), AGR-3/4 (Collin 2016) and AGR-5/6/7 (Pham 2021). The objectives of each program are presented in Figure 1.1. The purpose of the AGR-5/6/7 program was to provide a baseline fuel qualification data set to support licensing, deployment, and operation of HTGRs in the United States. To achieve these goals, the program includes fuel fabrication, irradiations of TRISO fuels and high-temperature materials, safety testing and post-irradiation examination (PIE), fuel performance modeling, and fission product (FP) transport (Sharp 2020). The AGR-5/6/7 work continues as a final irradiation program of the Advanced Reactor Technologies (ART) program, that began in February 2018 and ended in July 2020. The AGR-5/6/7 fuel compacts were irradiated for a total of approximately 360.9 effective full power days (EFPDs) (Stempien 2023), resulting in final burn-up values, on a per-compact basis, ranging from 5.66% to 15.26% fissions per initial heavy metal atom (FIMA), and fast fluence values ranging from 1.62×1025 n/m2 to 5.55×1025 n/m2 (E >0.18 MeV) (Stempien 2022)
The results presented represent the first successful attempt at the measurement of the driving capabilities of coaxial gaseous propellant rocket injectors. The required data have been obtained by employing the modified impedance tube technique with compressed air as the oxidizer and acetylene gas as the fuel. The data describe the frequency dependence of the injector admittances, from which the frequency dependence of the injection response factors can be calculated. The measured injector admittances have been compared with the predictions of the Feiler and Heidmann (1967) analytical model assuming different values for the characteristic combustion time. The values of combustion time which result in a best fit between the measured and predicted data are indicated for different equivalence ratios. It is shown that for the coaxial injector system investigated in this study the characteristic combustion times vary between .7 and 1.2 msec for equivalence ratios in the range of .57 to 1.31. The experimental data clearly show that the tested injector system could indeed drive combustion instabilities over a frequency range that is in qualitative agreement with the predictions of the Feiler and Heidmann model.
This paper summarizes the analysis of the Passive Neutron Albedo Reactivity (PNAR) measurements using the ORIGEN data analysis module for 23 boiling water reactor spent fuel assemblies that were performed in Finland under an international collaboration on spent fuel safeguards verification methods. PNAR is part of the proposed integrated nondestructive system to be used for safeguards verifications at the planned Finnish encapsulation facility. Besides measuring passive neutron and gamma emission rates from an assembly like a Fork detector, PNAR also measures the PNAR ratio, which is expected to correlate with the fissile content in the assembly. The emission rates and PNAR ratio can be used to verify the operator declarations and the fissile content of an assembly, respectively. The analysis was performed using the ORIGEN Data Analysis Module, which was originally developed for predicting Fork detector neutron and gamma signals for spent fuel measurements and has been integrated into the Integrated Review and Analysis Package developed by Euratom and the IAEA. The Module includes the ORIGEN burnup analysis code and integrates detector response functions pre-generated using MCNP to predict detector signals in several seconds per assembly. In this study, new response functions specific to PNAR measurements were generated for ORIGEN Module. The study also analyzes impacts of using detailed fuel design and operation information vs. standard safeguards information on results calculated by ORIGEN Module. Using detailed information reduced the standard deviation of relative differences between calculated and measured neutron count rates among the 23 assemblies from ~10% to ~4%. The results obtained using standard safeguards information for these PNAR measurements were similar to those obtained for the Fork detector. A clear trend was found between the calculated net neutron multiplications and the measured PNAR ratios of the 23 assemblies. This paper describes how ORIGEN Module calculates the expected PNAR neutron and gamma signals and PNAR ratio and how they compare with corresponding measured values.
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 (Advanced Exploration Systems (AES) Modular Power Systems) Power Module Demonstration in which the AMPS team used a PEM fuel cell system to power a 478 kilogram 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.
The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.