Fission Gas Release Research Articles

Incorporation of uranium nitride fuel capability into the ENIGMA fuel performance code: Model development and validation

Uranium dioxide (UO2) is the primary fuel form for nuclear reactors but its moderate uranium density and low thermal conductivity have prompted the exploration of alternative materials. Uranium nitride (UN) has emerged as a promising candidate for a variety of reactors, offering higher uranium density and thermal conductivity. This paper details the development and implementation of UN fuel capabilities within the ENIGMA fuel performance code for Light Water Reactor (LWR) applications. The new UN capability in ENIGMA includes correlations for theoretical density at room temperature, thermal conductivity, specific heat capacity, enthalpy, thermal expansion strain, Young’s modulus, Poisson’s ratio, thermal creep strain rate, irradiation creep strain rate and emissivity. Additionally, it incorporates models for densification, solid fission product swelling, fission gas bubble swelling, and fission gas release, along with a modified RADAR model for determining the pellet radial power profile and helium generation. Validation of the UN model was conducted using data from the L414 pin irradiation in the JOYO fast reactor in Japan. Further validation efforts are planned using datasets from JOYO and the Siloé thermal reactor in France. The paper also outlines areas of future work to address experimental data gaps and enhance model accuracy to cover a broader range of cladding materials, manufacturing parameters (including porosity volume fraction) and operating conditions (including fuel temperatures and burnups). Although focused on LWR applications, the work outlined supports the use of UN fuel across various reactor systems.

Modeling and design of a separate effects irradiation test targeting fission gas release from Cr-doped UO2

Fission gas release (FGR) from nuclear fuel during operation can diminish heat transfer properties across the pellet-cladding gap and increase the fuel rod internal pressure, thereby posing a concern to fuel reliability and safety during an accident. Enlarging the fuel grain size, which has been shown to improve fission gas retention, can be achieved by doping the fuel feedstock prior to sintering. In this work, the BISON fuel performance code was used to predict FGR from undoped and chromia-doped UO2 (referred to as Cr-doped UO2) fuel specimens with different grain sizes and across various temperatures. The BISON models identified the irradiation conditions for which FGR is most significant, and a separate effects irradiation experiment in the High Flux Isotope Reactor (HFIR) was then developed targeting those conditions. The experiment leveraged the MiniFuel irradiation capability at Oak Ridge National Laboratory and consisted of 12 fuel specimens of varying grain size and Cr content. A coupling scheme between BISON FGR results and the ANSYS finite element thermal model used for experiment design was formulated to predict cumulative FGR from each fuel specimen based on expected irradiation temperature histories. The fuel samples were fabricated and characterized as a part of this work, and the fuel compositions modeled in BISON were representative of the specimens used in the experiment. This combined modeling and experimental effort aims to study the effect of fuel grain size and Cr content on FGR and to provide simulated BISON FGR results that can be used for future model validation activities.

Preliminary Verification of Key Physical Models in Oxide Fuel Performance Analysis Code FEATURE Based on COMSOL

Nuclear performance analysis is of great significance for fuel design optimization and the safe operation of a reactor. Considering the design requirements of the China initiative Accelerator Driven System (CiADS) project, it is necessary to develop relevant fuel performance analysis tools to predict fuel performance evolution during service. The Fuel Element Analysis Tool for Undercritical Reactor Engineering (FEATURE) code, based on the advanced multiphysics coupling platform COMSOL software, is under development in this context, with a current focus on oxide fuel for fast reactors. This paper mainly conducts preliminary comparative verification of the oxygen redistribution, plutonium redistribution, and fission gas release modules of the FEATURE code through model examples, code-to-code evaluation, and experimental data. The results indicate that the FEATURE code exhibits good accuracy and reliability in simulating oxygen and plutonium redistribution, as well as fission gas release, making it suitable for integrated coupling calculations.

Modeling fission gas release at the mesoscale using multiscale DenseNet regression with attention mechanism and inception blocks

Mesoscale simulations of fission gas release (FGR) in nuclear fuel provide a powerful tool for understanding how microstructure evolution impacts FGR, but they are computationally intensive. In this study, we present an alternate, data-driven approach, using deep learning to predict instantaneous FGR flux from 2D simulated nuclear fuel microstructure images. Four convolutional neural network (CNN) architectures with multiscale regression are trained and evaluated on simulated FGR data generated using a hybrid phase field/cluster dynamics model. All four networks show high predictive power, with R2 values above 98%. The best performing network combines a Convolutional Block Attention Module (CBAM) and InceptionNet mechanisms to provide superior accuracy (mean absolute percentage error of 4.4%), training stability, and robustness on very low instantaneous FGR flux values. The trained model demonstrates the utility of deep learning in this context, but has no validity outside the 2D model and conditions used to generate the training data.

Radiation induced athermal diffusivity in uranium mononitride

Uranium mononitride (UN) is one of the ceramic nuclear fuel alternatives to oxide fuel considered for light water reactors and advanced reactor designs. Properties like self- and fission gas diffusivity need to be better understood, given that they influence key fuel performance phenomena such as fission gas swelling and release. In particular, the radiation induced athermal (D3) diffusivity remains challenging to accurately predict and has only been sparsely characterized in UN, despite its importance as it likely governs diffusion at the low temperatures this high-thermal-conductivity fuel form may operate. Molecular Dynamics simulations are used to estimate the mean square displacement induced by a primary knock-on atom (PKA) with a given kinetic energy. These results are combined with the PKA energy distributions obtained from binary collision approximation calculations to obtain the displacement due to a particular fission fragment. Finally, this is combined with experimental fission fragment yields to determine the displacement due to an average fission event and, thus, express the athermal diffusivity as a function of the fission rate density. These results are in excellent agreement with available experimental data. A particular importance is given to the understanding and the quantification of the variability of these results.

Parameterization of vacancy production rate in phase-field models of fission gas bubble evolution in nuclear fuel

Phase-field modeling has increasingly been used to study microstructural evolution in fission gas bubbles in nuclear fuel to improve understanding of fission gas release. To improve computational efficiency, often only vacancies and gas atoms are included as defect species. In this case, the net effects of vacancy and interstitial production, recombination, and biased sink absorption are included as a net vacancy source, or net vacancy source combined with an effective sink. However, there has been a lack of clarity on what parameter values should be used for these approaches to best match the more complete physical picture that includes interstitials and vacancies. Here, we compare a phase-field model of void growth to analytical models for the source-only and source plus sink approach to gain insight into how the phase-field models can be parameterized effectively. The source-only approach provides greater flexibility to match growth rates determined from the full vacancy-interstitial picture. A strategy was developed for determining the value of the net vacancy source term by comparing to an analytical model that includes vacancy and interstitial production, recombination, and biased sink absorption. This strategy can be used to parameterize phase-field models of fission gas bubble growth.

Integral-scale validation of the SCIANTIX code for Light Water Reactor fuel rods

Mechanistic multi-scale modelling holds the potential to inform fuel performance codes by incorporating high-fidelity models, algorithms, parameters, and material properties. In this context, meso-scale codes emerge as valuable tools for developing detailed models and performing separate verification and validation steps. This work focuses on SCIANTIX, an open-source 0D meso-scale code designed to describe the behaviour of gaseous and volatile fission products in nuclear oxide fuel. The code predominantly employs engineering physics-based behavioural models featuring computational times that align with typical fuel performance code requirements. Given the numerical foundation of the code, it is applicable to both stationary and transient conditions. Following a recent work outlining the standalone SCIANTIX (version 2.0) performance and its separate-effect validation database, we present its performance when coupled with fuel performance codes to simulate light water reactor fuel rods. The experiments selected for the comparative analysis constitute an initial integral validation database. The comparison focuses on conventional engineering quantities of interest, such as integral fission gas release, demonstrating the satisfactory performance of the code. Additionally, it highlights the potential advantages of multi-scale modelling over conventional semi-empirical approaches.

Investigation of the fission gas release and grain boundary percolation in oxide fuels: A COMSOL multiphysics-based study

In this study, we explore fission gas release (FGR) behaviour in oxide fuels using the COMSOL Multiphysics framework. Building upon the conventional Booth model, our model incorporates the influence of grain boundary network percolation on FGR. The microstructure of the fuel pellet is represented by a 2D axi-symmetric geometry. We investigate various factors affecting FGR, mainly includes bubble contact angle, resolution rate, radial position. This study aims to capture the physical phenomena observed in FGR experiments and assess the sensitivity of FGR to these parameters at both microscopic and macroscopic scales. Moreover, our model accounts for long-range percolation on the networked grain boundaries, a factor overlooked by the Booth model. The gas resolution of grain boundaries is also considered. Finally, we compare and analyze the results obtained from our model with those from the Booth model and other relevant models, aiming at enhancing our understanding of FGR behavior in oxide fuels.

NRC’s methodology to estimate fuel dispersal during a large break loss of coolant accident

Recent experimental findings indicate that under certain transient conditions, high-burnup fuel operating at sufficiently high power can fragment and escape from burst fuel cladding, as discussed in the Nuclear Regulatory Commission’s Research Information Letter (RIL) 2021–13. The escaped fuel fragments could subsequently be distributed throughout a reactor coolant system (RCS), potentially challenging core coolability during a loss of coolant accident (LOCA). Consequently, the NRC has developed a methodology to estimate the mass of fuel that could be dispersed during a large-break LOCA. The methodology involves several NRC-sponsored computer codes, including SCALE/Polaris for lattice physics, PARCS/PATHS for full-core neutronics, TRACE for core and RCS thermal hydraulics, and FAST for steady-state and transient fuel performance calculations. Output from FAST is combined with the models in RIL 2021–13 to estimate fuel dispersal to the coolant.NRC staff have exercised this methodology using a prospective Westinghouse four loop core design with high burnup and extended enrichment that was obtained from the Department of Energy’s NEAMS program. Results demonstrate that the NRC methodology can be exercised to provide fuel dispersal estimates that can subsequently be used to study the potential consequences of FFRD. However, this exercise has also identified several areas for improvements to both the codes and to the modeling approaches used here, including the pin power reconstruction methods in PARCS, the modeling approach used to connect the Cartesian and cylindrical vessel components in TRACE, transient fission gas release and fuel rod upper plenum models in FAST, and tighter coupling between FAST and TRACE.

Interpretation of the online measurement data for selected tests of the RISOE-III project

Two tests, AN3 and AN4 from the RISOE-III Fission Gas Release (FGR) project are analysed using the updated version of the FALCON MOD01 code coupled with the GRSW-A model. Based on comparison of the stand-alone code calculation with the measurement for centerline fuel temperature, the previous calibration and verification of the thermal analysis of the code is confirmed. Additional analysis with the FALCON-to-FRELAX (F2F) coupled code system is carried out and presented for simulation of the effects of delayed axial redistribution of the released fission gases in the rodlets, as an attempt to interpret the data on significant jumps in measured upper-plenum pressure at a power dip. The other feature of the data, addressed by the additional sensitivity study with F2F, is peaking that can be seen in the measured fuel temperature after each power rise. A conclusion can be proposed that the peculiarities in the online-measurement could, probably, result from the features of fabrication of the fuel segments for pre-irradiation in the NPP, and instrumentation of the rodlets to be tested in the research reactor. Therefore, these features can have only limited effect on evaluation of reliability and safety of the standard fuel during normal operation in power reactors.

Fission accelerated steady-state post irradiation examinations - Part II

The Advanced Fuels Campaign's Fission Accelerated Steady State Testing (FAST) approach at Idaho National Laboratory creates a benchmark for evaluating accelerated irradiation via control rodlets and advanced metal fuel alloys for sodium-cooled fast reactors (SFRs). FAST experiments have been developed to generate prototypic temperature conditions during steady state irradiations of scaled geometric fuel pins. This approach helps to attain higher burn ups at a much faster rate than previous irradiation tests. For this study, the results from profilometry, fission gas release, and metallography of a FAST experiment are presented. Profilometry determined 0 % effective strain in the rodlets. The fission gas release fraction was measured from puncture/collection analysis. Constituent redistribution was observed in two specimens despite the peak fuel temperatures being below the normal ranges in which redistribution is expected. Metallography of the two higher temperature specimens showed typical swelling with the solid pin closing the fuel-cladding gap and the annular specimen having a fully closed annulus. Additionally, metallography indicated no swelling, no redistribution, and a homogenous microstructure for specimens with lower irradiation temperature. Post irradiation examination of FAST rodlets generally showed the expected representative behavior of metallic fuels within SFRs.

Multi-physical effects induced by spatiotemporal corrosion of T91 cladding in oxygen-controlled LBE environment

T91, a pivotal candidate material for cladding in lead-based fast reactors (LFRs), demonstrates considerable potential for corrosion resistance in liquid lead-bismuth eutectic (LBE) corrosion experiments. However, the multi-physics characteristics of T91 cladding corrosion in its operational environment remain unclear, posing an obstacle to its practical application. This study focuses on the spatiotemporal corrosion of T91 cladding in multi-physics coupling scenarios by means of COMMA (Cladding/Oxidation corrosion Multi-physics Modeling and Analysis code), in which, modules of cladding oxidation, cladding oxide removal, heat transfer, mechanical deformation, and fission gas release are integrated and collaboratively solved. The corrosion patterns of T91 cladding with different oxygen concentrations and the underlying multi-physics behavior of the corrosion processes under operational conditions simulating are summarized. For the lower oxygen concentration of 1.8e-9 wt% in LFRs, the results indicate a 0.2 m axial zone with base material exposed to LBE reveals the presence of the oxidation protection failure. An exacerbation of cladding heat transfer degradation is observed after oxygen concentration reaching the oxygen-dominant region, due to the enhanced temperature positive feedback of oxidation corrosion. For the high oxygen concentration of 1.4e-6 wt%, an axial offset of 0.24 m for the location of the minimum gap size is observed. This study identifies a trend of the reduction thickness of cladding that first decreasing and then increasing with an elevated oxygen concentration, attributed to a transition in corrosion patterns. The temperature and mechanical analyses indicate that the optimal oxygen concentration for corrosion inhibition and fuel safety is located around the critical oxygen-dominant concentration.

Assessing the PLEIADES/GERMINAL V3 fuel performance code against transmutation experiments in sodium fast reactors

We present the results of a first assessment of the multiphysics PLEIADES/GERMINAL V3 fuel performance code against integral irradiation experiments of americium-bearing MOX fuel pins irradiated in sodium fast reactors. The pins considered in this work cover a range of americium contents from 2 to 5 weight %, being representative for the homogeneous transmutation path. The code predictions are compared to several post-irradiation examination results on integral (e.g., fission gas release) and local quantities (e.g., actinides redistribution in the fuel pellet), and demonstrate a satisfactory agreement with experimental data. The deviations between calculated and experimental quantities shed some light on modeling developments needed to better calculate the behavior of americium-bearing mixed-oxides fuel pins under irradiation.

Transient analysis of temperature field and fission gas about the arc plate fuel element

Obtaining the temperature field and fission gas release of fuel elements are crucial to the study on fuel behaviors and ensure the safety of elements. In order to enhance fuel efficiency, providing a design idea to make the fast reactor with high flux and multi-function, we have designed a core of the lead-based fast reactor with arc plate fuel elements. Based on above, a transient performance analysis program of arc plate fuel elements (TPAPAFE) was developed in this paper, for analyzing the thermal and fission gas module of arc plate fuel element by calculating transient variation in the maximum pellet temperature, internal and external coolant outlet temperature, and fission gas release share. The modular verification was done by comparing the results of TPAPAFE with others. Besides, the result of the arc plate fuel analyzed by TPAPAFE shows that the code is feasible and rational, and the single arc plate is so thin that coolant outlet temperatures on both sides of the plate are almost the same. Meanwhile, the error in temperature data between TPAPAFE and the steady-state program is only 5%. Furthermore, fission gas release fraction increased linearly with pellet temperature and bubble growth.

Impact of grain boundary and surface diffusion on predicted fission gas bubble behavior and release in UO2 fuel

In this work, we quantify the impact of grain boundary (GB) and surface diffusion on fission gas bubble evolution and fission gas release in UO2 nuclear fuel using simulations with a hybrid phase field/cluster dynamics model. We begin with a comprehensive literature review of uranium vacancy and xenon atom diffusivity in UO2 through the bulk, along GBs, and along surfaces. In our model we represent fast GB and surface diffusion using a heterogeneous diffusivity that is a function of the order parameters that represent bubbles and grains. We find that the GB diffusivity directly impacts the rate of gas release via GB transport, and that the GB diffusivity is likely below 104 times the lower value from Olander and van Uffelen (2001). We also find that the surface diffusivity impacts bubble coalescence and mobility, and that the bubble surface diffusivity is likely below 10−4 times the value from Zhou and Olander (1984).

Review of fission gas release in liquid metal reactor fuel cladding failure accident

The release of gas fission products from liquid metal fast reactors in the event of cladding failure accidents is a significant focus during the development and improved design of such reactors. Researchers worldwide have devoted considerable attention to this topic. But studies aiming at liquid metal reactors have not yet reached the same level as studies of fission gas release in water-cooled reactors. To address this gap, this paper reviews the research progress of related fission gas release from the aspects of mechanism model, experimental research, numerical simulation, and measurement. Considering the potential essential similarity of the release process of fission gas in water-cooled reactors and liquid metal cooled reactors, the researches on water-cooled reactors are also included. This review helps to understand the behavior and characteristics of fission gas release in liquid metal cooled reactors and provides some guidance for subsequent numerical simulation and experimental research on fission gas release based on liquid metal cooled reactors.

Phase-field simulation on fission gas release behavior of large grain UO<sub>2</sub> fuel

In order to predict the release behavior of fission gas in large grain UO<sub>2</sub> fuel and provide support for the development of accident tolerant fuel, a phase-field model is used to simulate the release behavior of fission gas in the microstructure of UO<sub>2</sub> polycrystalline in this work. This model adopts a set of coupled Cahn-Hilliard equations and Allen-Cahn equations, using conserved field variables to represent the distribution of fission gas and vacancies, and distinguishing bubble phase from matrix phase by using order parameters. This model focuses on investigating the effects of different grain sizes, temperature conditions, and diffusion coefficients on the release behavior of fission gas, demonstrating the nucleation, growth, and fusion behavior of bubbles. Simulation results are obtained for fuel porosity, bubble coverage on grain boundaries, and average bubble radius at a certain degree of burnup. The results show that temperature and diffusion coefficient have a significant influence on porosity and bubble coverage on grain boundaries. When the diffusion coefficient is high, grain size also has a significant influence on fission gas release behavior. And when the diffusion coefficient is low, the influence of grain size is not significant. In addition, the distribution of fission gas bubbles under high burnup obtained through this model is also in good agreement with experimental result. The model can predict the behavior of fission gas release in large grain UO<sub>2</sub> fuel.

Building a DFT+U machine learning interatomic potential for uranium dioxide

Despite uranium dioxide (UO2) being a widely used nuclear fuel, fuel performance models rely extensively on empirical correlations of material behavior, leveraging the historical operating experience of UO2. Mechanistic models that consider an atomistic understanding of the processes governing fuel performance (such as fission gas release and creep) will enable a better description of fuel behavior under non-prototypical conditions such as in new reactor concepts or for modified UO2 fuel compositions. To this end, molecular dynamics simulation is a powerful tool for rapidly predicting physical properties of proposed fuel candidates. However, the reliability of these simulations depends largely on the accuracy of the atomic forces. Traditionally, these forces are computed using either a classical force field (FF) or density functional theory (DFT). While DFT is relatively accurate, the computational cost is burdensome, especially for f-electron elements, such as actinides. By contrast, classical FFs are computationally efficient but are less accurate. For these reasons, we report a new accurate machine learning interatomic potential (MLIP) for UO2 that provides high-fidelity reproduction of DFT forces at a similar low cost to classical FFs. We employ an active learning approach that autonomously augments the DFT training data set to iteratively refine the MLIP. To further improve the quality of our predictions, we utilize transfer learning to retrain our MLIP to higher-accuracy DFT+U data. We validate our MLIPs by comparing predicted physical properties (e.g., thermal expansion and elastic properties) with those from existing classical FFs and DFT/DFT+U calculations, as well as with experimental data when available.

An integrated statistical-thermodynamic model for fission gas release and swelling in nuclear fuels

We propose a new model for burst fission gas release induced by microcracking in ceramic nuclear fuels such as uranium dioxide. The model stipulates that the densities of defects in the fuel material, such as microcracks and fission gas bubbles on grain boundaries, evolve in accordance with the second law of thermodynamics. Central to the model is the notion of an effective temperature, conjugate to the configurational entropy of the fuel material, and directly linked to the burnup. The model predicts that microcracking, driven by the internal stress state of the fuel material, reduces the bubble storage capacity of grain boundaries, and accounts for burst fission gas release during rapid temperature transients that simulate power transients, reactor startup, and loss-of-coolant accident conditions.

An efficient instance segmentation approach for studying fission gas bubbles in irradiated metallic nuclear fuel

Gaseous fission products from nuclear fission reactions tend to form fission gas bubbles of various shapes and sizes inside nuclear fuel. The behavior of fission gas bubbles dictates nuclear fuel performances, such as fission gas release, grain growth, swelling, and fuel cladding mechanical interaction. Although mechanical understanding of the overall evolution behavior of fission gas bubbles is well known, lacking the quantitative data and high-level correlation between burnup/temperature and microstructure evolution blocks the development of predictive models and reduces the possibility of accelerating the qualification for new fuel forms. Historical characterization of fission gas bubbles in irradiated nuclear fuel relied on a simple threshold method working on low-resolution optical microscopy images. Advanced characterization of fission gas bubbles using scanning electron microscopic images reveals unprecedented details and extensive morphological data, which strains the effectiveness of conventional methods. This paper proposes a hybrid framework, based on digital image processing and deep learning models, to efficiently detect and classify fission gas bubbles from scanning electron microscopic images. The developed bubble annotation tool used a multitask deep learning network that integrates U-Net and ResNet to accomplish instance-level bubble segmentation. With limited annotated data, the model achieves a recall ratio of more than 90%, a leap forward compared to the threshold method. The model has the capability to identify fission gas bubbles with and without lanthanides to better understand the movement of lanthanide fission products and fuel cladding chemical interaction. Lastly, the deep learning model is versatile and applicable to the micro-structure segmentation of similar materials.