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DOE OSTI2011

Deep Bed Iodine Sorbent Testing FY 2011 Report

Nuclear fission results in the production of fission products (FPs) and activation products that increasingly interfere with the fission process as their concentrations increase. Some of these fission and activation products tend to evolve in gaseous species during used nuclear fuel reprocessing. Analyses have shown that I129, due to its radioactivity, high potential mobility in the environment, and high longevity (half life of 15.7 million years), can require control efficiencies of up to 1,000x or higher to meet regulatory emission limits. Deep-bed iodine sorption testing has been done to evaluate the performance of solid sorbents for capturing iodine in off-gas streams from nuclear fuel reprocessing plants. The objectives of the FY 2011 deep bed iodine sorbent testing are: (1) Evaluate sorbents for iodine capture under various conditions of gas compositions and operating temperature (determine sorption efficiencies, capacities, and mass transfer zone depths); and (2) Generate data for dynamic iodine sorption modeling. Three tests performed this fiscal year on silver zeolite light phase (AgZ-LP) sorbent are reported here. Additional tests are still in progress and can be reported in a revision of this report or a future report. Testing was somewhat delayed and limited this year due to initial activities to address some questions of prior testing, and due to a period of maintenance for the on-line GC. Each test consisted of (a) flowing a synthetic blend of gases designed to be similar to an aqueous dissolver off-gas stream over the sorbent contained in three separate bed segments in series, (b) measuring each bed inlet and outlet gas concentrations of iodine and methyl iodide (the two surrogates of iodine gas species considered most representative of iodine species expected in dissolver off-gas), (c) operating for a long enough time to achieve breakthrough of the iodine species from at least one (preferably the first two) bed segments, and (d) post-test purging with pure N 2 to drive loosely or physisorbed iodine species off of the sorbent. Post-test calculations determine the control efficiencies for each bed, iodine loadings on the sorbent, and mass transfer zone depths. Portions of the iodine-laden sorbent from the first bed of two of the tests have been shipped to SNL for waste form studies. Over the past three years, we have explored a full range of inlet iodine and methyl iodide concentrations ranging from ~100 ppb to ~100 ppm levels, and shown adequate control efficiencies within a bed depth as shallow as 2 inches for lower concentrations and 4 inches for higher concentrations, for the AgZ-type sorbents. We are now performing a limited number of tests in the NC-77 sorbent from SNL. Then we plan to continue to (a) fill in data gaps needed for isotherms and dynamic sorbent modeling, and (b) test the performance of additional sorbents under development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2017

UFD Proof-of-Concept Demonstration

The BISON Spent Nuclear Fuel modeling capability has been demonstrated through simulations of the complete nuclear fuel lifecycle including in-reactor irradiation, spent fuel pool cooling, vacuum drying and finally dry cask storage. The importance of hydrogen in the cladding and the formation of hydrides at various stages of the fuel lifecycle have been a focus of the initial development work. In addition, the integration in BISON of a hydride damage model has improved the prediction of the cladding mechanical behavior. The work described in this report was performed under funding from the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. This report is issued in satisfaction of the level 2 milestone M2MS-17IN0201012 on UFD Proof-of-Concept Demonstration for the INL Engineering-Scale Fuel Performance (BISON) Workpackage.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1995

Direct adaptive performance optimization of subsonic transports: A periodic perturbation technique

Aircraft performance can be optimized at the flight condition by using available redundancy among actuators. Effective use of this potential allows improved performance beyond limits imposed by design compromises. Optimization based on nominal models does not result in the best performance of the actual aircraft at the actual flight condition. An adaptive algorithm for optimizing performance parameters, such as speed or fuel flow, in flight based exclusively on flight data is proposed. The algorithm is inherently insensitive to model inaccuracies and measurement noise and biases and can optimize several decision variables at the same time. An adaptive constraint controller integrated into the algorithm regulates the optimization constraints, such as altitude or speed, without requiring and prior knowledge of the autopilot design. The algorithm has a modular structure which allows easy incorporation (or removal) of optimization constraints or decision variables to the optimization problem. An important part of the contribution is the development of analytical tools enabling convergence analysis of the algorithm and the establishment of simple design rules. The fuel-flow minimization and velocity maximization modes of the algorithm are demonstrated on the NASA Dryden B-720 nonlinear flight simulator for the single- and multi-effector optimization cases.

Espana, Martin D.↗

NASA NTRS1978

Solid Polymer Electrolyte (SPE) fuel cell technology program

Many previously demonstrated improved fuel cell features were consolidated to (1) obtain a better understanding of the observed characteristics of the operating laboratory-sized cells; (2) evaluate appropriate improved fuel cell features in 0.7 sq ft cell hardware; and (3) study the resultant fuel cell capability and determine its impact on various potential fuel cell space missions. The observed performance characteristics of the fuel cell at high temperatures and high current densities were matched with a theoretical model based on the change in Gibbs free energy voltage with respect to temperature and internal resistance change with current density. Excellent agreement between the observed and model performance was obtained. The observed performance decay with operational time on cells with very low noble metal loadings (0.05 mg/sq cm) were shown to be related to loss in surface area. Cells with the baseline amount of noble catalyst electrode loading demonstrated over 40,000 hours of stable performance.

Source record↗

DOE OSTI2020

Sensitivity Analysis and Effective Parametrization of PEM Fuel Cell Models

The cost of proton-exchange-membrane fuel cells (PEMFCs) remains a major hurdle in large-scale commercialization of this technology. To improve their performance and reduce cost, novel materials and electrode designs are continuously envisioned, e.g., non-PGM catalyst layers, ultra-thin Pt/Pt-Ni based catalyst layers, structured ionomer arrays or NSTF catalyst layers.1 Understanding the impact of these improvement strategies can be extremely time and cost intensive due to complex physical phenomena and large design space. We have previously developed a PEMFC modeling framework2 which has been a time and cost effective tool for understanding and optimizing the complex multi-physics phenomena within PEMFCs; however, several of the cell parameters used in the modeling have large spread in measured data.3 Furthermore, several transport parameters such as water adsorption kinetics have not been accurately measured and the approximations are spread over several orders of magnitude. These uncertainties cause problems in ascertaining accuracy of the modeling approach and they reduce the predictive power of the numerical models. The aim of this work is to identify the sensitivity of PEFC numerical model outputs to various input parameters. The previously in-house developed MEA modeling framework2 is used for PEMFC modeling. The sensitivity of the model outputs with respect to inputs parameters is obtained by analyzing the condition numbers for different output-input pairs at varying operating conditions. An example of the sensitivity analysis is shown in Figure 1. The condition numbers are obtained for the entire possible range of input parameters at varying operating conditions to identify the most crucial parameters of the PEMFC model. Based on our preliminary analysis, parameters related to kinetics (exchange current density and ECSA) and heat/water management in electrodes and ionomer (ionomer fraction, thermal conductivity) are most crucial. One of the major goals of this work is to identify the most crucial set of parameters towards which the model shows maximum sensitivity. This will guide future experimentalists to measure these properties with higher accuracy. Furthermore, the sensitivity analysis will also enable us to optimize the PEMFC performance by selectively targeting the most sensitive parameters and thereby making the largest impact. Acknowledgements The work is funded under the Fuel Cell Performance and Durability Consortium (FC-PAD), by the Fuel Cell Technologies Office (FCTO), Office of Energy Efficiency and Renewable Energy (EERE), of the U.S. Department of Energy under contract number DE-AC02-05CH11231. The authors would like to thank Nathan Craig at Robert Bosch LLC for his valuable input in designing the sensitivity analysis. The authors would also like to thank Giovanna Bucci and Matthias Hanauer at Robert Bosch for their valuable inputs and discussion. References P. K. Sinha, W. Gu, A. Kongkanand and E. Thompson, J. Electrochem. Soc., 158, B831 (2011). L. M. Pant, M. R. Gerhardt, N. Macauley, R. Mukundan, R. L. Borup and A. Z. Weber, Electrochim. Acta, 326, 134963 (2019). R. Vetter and J. O. Schumacher, ArXiv181110091 Phys. (2018). Figure 1

Pant, Lalit↗

DOE OSTI2017

The Accuracy and Correction of Fuel Consumption from Controller Area Network Broadcast

Fuel consumption (FC) has always been an important factor in vehicle cost. With the advent of electronically controlled engines, the controller area network (CAN) broadcasts information about engine and vehicle performance, including fuel use. However, the accuracy of the FC estimates is uncertain. In this study, the researchers first compared CAN-broadcasted FC against physically measured fuel use for three different types of trucks, which revealed the inaccuracies of CAN-broadcast fueling estimates. To match precise gravimetric fuel-scale measurements, polynomial models were developed to correct the CAN-broadcasted FC. Lastly, the robustness testing of the correction models was performed. The training cycles in this section included a variety of drive characteristics, such as high speed, acceleration, idling, and deceleration. The mean relative differences were reduced noticeably.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2017

Heat deposition analysis for the High Flux Isotope Reactor’s HEU and LEU core models

The High Flux Isotope Reactor at Oak Ridge National Laboratory is an 85 MW th pressurized light-water-cooled and -moderated flux-trap type research reactor. The reactor is used to conduct numerous experiments, advancing various scientific and engineering disciplines. As part of an ongoing program sponsored by the US Department of Energy National Nuclear Security Administration Office of Material Management and Minimization, studies are being performed to assess the feasibility of converting the reactor’s highly enriched uranium fuel to low-enriched uranium fuel. To support this conversion project, reference models with representative experiment target loading and explicit fuel plate representation were developed and benchmarked for both fuels to (1) allow for consistent comparison between designs for both fuel types and (2) assess the potential impact of low-enriched uranium conversion. These high-fidelity models were used to conduct heat deposition analyses at the beginning and end of the reactor cycle and are presented herein. This article (1) discusses the High Flux Isotope Reactor models developed to facilitate detailed heat deposition analyses of the reactor’s highly enriched and low-enriched uranium cores, (2) examines the computational approach for performing heat deposition analysis, which includes a discussion on the methodology for calculating the amount of energy released per fission, heating rates, power and volumetric heating rates, and (3) provides results calculated throughout various regions of the highly enriched and low-enriched uranium core at the beginning and end of the reactor cycle. These are the first detailed high-fidelity heat deposition analyses for the High Flux Isotope Reactor’s highly enriched and low-enriched core models with explicit fuel plate representation. Lastly, these analyses are used to compare heat distributions obtained for both fuel designs at the beginning and end of the reactor cycle, and they are essential for enabling comprehensive thermal hydraulics and safety analyses that require detailed estimates of the heat source within all of the reactor’s fuel element regions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2015

On-board diesel autothermal reforming for PEM fuel cells: Simulation and optimization

Alternative power sources are nowadays the only option to provide a quick response to the current regulations on automotive pollutant emissions. Hydrogen fuel cell is one promising solution, but the nature of the gas is such that the in-vehicle conversion of other fuels into hydrogen is necessary. In this paper, autothermal reforming, for Diesel on-board conversion into a hydrogen-rich gas suitable for PEM fuel cells, has investigated using the simulation tool Aspen Plus. A steady-state model has been developed to analyze the fuel processor and the overall system performance. The components of the fuel processor are: the fuel reforming reactor, two water gas shift reactors, a preferential oxidation reactor and H{sub 2} separation unit. The influence of various operating parameters such as oxygen to carbon ratio, steam to carbon ratio, and temperature on the process components has been analyzed in-depth and results are presented.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2012

Reply To "Comments on "Why Hasn’t Earth Warmed as Much as Expected?""

No abstract prepared.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Modeling Activities Related to Waste Form Degradation: Progress Report

This report represents the milestone deliverable M4SF-22SN010309092 “Modeling Activities Related to Waste Form Degradation: Progress Report” that describes the progress of R&D activities of ongoing modeling investigations specifically on nuclear waste glass degradation, Density Functional Theory (DFT) studies on clarkeite structure and stability, and electrochemical model development of spent nuclear fuel (SNF). These activities are part of the Waste Form Testing, Modeling, and Performance work package at Sandia National Laboratories (SNL). This work package is part of the “Inventory and Waste Form Characteristics and Performance” control account that includes various experimental and modeling activities on nuclear waste degradation conducted at Oak Ridge National Laboratory (ORNL), SNL, Argonne National Laboratory (ANL), and Pacific Northwest National Laboratory (PNNL).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2015

VERA 3.3 Release Notes

The Virtual Environment for Reactor Applications components included in this distribution include selected computational tools and supporting infrastructure that solve neutronics, thermal-hydraulics, fuel performance, and coupled neutronics-thermal hydraulics-fuel performance problems. The infrastructure components provide a simplified common user input capability and provide for the physics integration with data transfer and coupled-physics iterative solution algorithms. Neutronics analysis can be performed for 2D lattices, 2D core and 3D core problems for pressurized water reactor geometries that can be used to calculate criticality and fission rate distributions by pin for input fuel compositions. Two neutronics capabilities are provided in this distribution including MPACT (from the University of Michigan) that supports 2D lattices and Insilico (from Oak Ridge National Laboratory) that supports 2D lattices, 2D core planes, and 3D core geometries. Both codes include integrated cross section capabilities that provide problem-specific cross sections for the problems defined. A thermal-hydraulics capability is provided with CTF (an updated version of the COBRA-TF code developed by the Pennsylvania State University) that allows thermal-hydraulics analyses for single and multiple assemblies using the simplified VERA common input. This distribution also includes an initial coupled neutronics/thermal-hydraulics capability to allow calculations of single assemblies using Insilico or MPACT coupled with CTF. The VERA fuel rod performance subcomponent BISON-CASL calculates, on a 3D basis, fuel rod temperature, fuel rod internal pressure, free gas volume, clad integrity and fuel rod waterside diameter. These capabilities allow simulation of power cycling, fuel conditioning and deconditioning, high burnup performance, power uprate scoping studies, and accident performance. Specifically targeted at VERA’s core simulator functionality (VERA-CS), the existing VERA subchannel-neutronics capability based on CTF and Insilico has been extended to use the axisymmetric 2D R-Z fuel rod modeling functionality of BISON-CASL to produce a three-way coupled neutronics, thermal-hydraulics and fuel performance capability known as Tiamat. This distribution is intended to be used for testing and evaluation of the released components of VERA. Testing within CASL has focused specifically on Westinghouse four-loop reactor geometries and conditions with example problems included in the distribution.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

Fuel Cycle Performance of Fast Spectrum Molten Salt Reactor Designs

In the search for new ways to generate low-carbon, reliable base-load power, a resurgence of interest in advanced nuclear energy technologies, including Molten Salt Reactors (MSRs), has produced multiple new conceptual MSRs including fast neutron spectrum designs. The fuel cycle performance of four historical fast MSR designs is analyzed using a recently developed SCALE/TRITON 6.2.4 Alpha with a continuous online reprocessing functionality. The fast spectrum and continuous feed and removal of material enable these concepts to have remarkable fuel cycle metrics: (1) resource utilization is approximately 18 times better than for a typical low-enriched thermal spectrum once-through fuel cycle (i.e., from approximately 180 t/GWe-year to 1 t/GWe-year); (2) fast MSRs generate approximately 25 times less nuclear waste than the current once-through fuel cycle. These metrics are consistent with the Evaluation and Screening Study, which produced a technology-agnostic quantification of the characteristic performance of alternate fuel cycles. Additionally, full-core and unit cell transport models were created and compared to verify the viability of using simplified unit cell geometries for long-term depletion simulation. The unit cell approximation provided a speedup of 20 times relative to the full-core simulation, with depleted mass relative error for major isotopes of less than 2%. Additional fast MSRs design and analysis challenges associated with different fuel cycles and the use of MSR technology are addressed and discussed.

Rykhlevskii, Andrei↗

DOE OSTI2015

Classical Molecular Dynamics Simulation of Nuclear Fuel

Molecular dynamics simulation is well suited to study primary damage production by irradiation, defect interactions with fission gas atoms, gas bubble nucleation, grain boundary effects on defect and gas bubble evolution in nuclear fuel, and the resulting changes in thermo-mechanical properties. In these simulations, the forces on the ions are dictated by interaction potentials generated by fitting properties of interest to experimental data. The results obtained from the present generation of potentials are qualitatively similar, but quantitatively different. There is a need to refine existing potentials to provide a better representation of the performance of polycrystalline fuel under a variety of operating conditions, and to develop models that are equipped to handle deviations from stoichiometry. In addition to providing insights into fundamental mechanisms governing the behaviour of nuclear fuel, MD simulations can also provide parameters that can be used as inputs for mesoscale models.

Devanathan, Ram↗

NASA NTRSDate not supplied

Modeling Validation and Its Design Relevance for NTP Fuel Elements

The development of Nuclear Thermal Propulsion engines depends heavily on modeling and simulation of a number of disciplines—neutronic / fluid / thermal / structural are only a start. Further, rockets involve extreme conditions, particularly high material temperatures in the nuclear fuel. This paper considers validation of the underling physical models and codes and how their accuracy influences several design challenges. Several issues are identified, where models, material data, could enable design solutions. In particular, thermal and CTE mismatch stresses are a concern, and stress predictions would benefit from improved mechanical property data at high temperatures. Nuclear inter-element heating causes propellant flow maldistribution and performance risks, but a simple model provides insights. High temperature chemical diffusion should be modeled to understand fuel element mass loss.

Nuclear Propulsion↗

NASA NTRSDate not supplied

Modeling Validation and Its Design Relevance for NTP Fuel Elements

Nuclear Propulsion↗

DOE OSTI2021

Modeling Activities Related to Waste Form Degradation: Progress Report

This report represents the milestone deliverable M4SF-21SN010309021 “Modeling Activities Related to Waste Form Degradation: Progress Report” that describes the progress of R&D activities of ongoing modeling investigations specifically on nuclear waste glass degradation, Density Functional Theory (DFT) studies on clarkeite structure and stability, and electrochemical modeling of spent nuclear fuel (SNF). These activities are part of the newly-created Waste form Testing, Modeling, and Performance work package at Sandia National Laboratories (SNL). This work package is part of the “Inventory and Waste Form Characteristics and Performance” control account that includes various experimental and modeling activities on nuclear waste degradation conducted at Oak Ridge National Laboratory (ORNL), SNL, Argonne National Laboratory (ANL), and Pacific Northwest National Laboratory (PNNL).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Modeling Metallic Fuel using Peridynamics

Based on available modeling and simulation capabilities of peridynamics module in MOOSE framework for oxide fuel, the overall goal of this project is to further develop the peridynamics capabilities for modeling metallic fuel. It includes two major tasks: 1) develop validated scheme to handle the shape tensor singularity due to insufficient active neighbors of a material particle in the peridynamic correspondence model for fracture problems, and 2) develop failure modeling scheme including failure criterion for metallic fuels. Before the peridynamics can be applied to model metallic fuel, the formulation instability of the peridynamic correspondence model should be addressed. The PI first worked on developing new stabilization method to improve the performance of the peridynamic correspondence model and reduce the possibility of getting a singular shape tensor while applying the model for fracture problems. The new stabilization scheme uses bond-associated weight function rather than bond-associated horizon. Compared to bondassociated horizon stabilized method, this new stabilization scheme has better performance with improved prediction accuracy and reduced free surface effect. Using this newly developed stabilization, materials models from BISON can be directly used in peridynamics for metallic fuels, such as fission rate and burnup dependent creep and swell models. Publication of this work is under preparation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗