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This is the data from the paper "Systematic and Predictive Trends to Chromium Poisoning in Solid Oxide Fuel Cell Cathodes" by Hokon Kim et al., appearing in the Journal of Power Sources. The files here are the microstructures, mesh files, and outputs from the ERMINE finite element model of chromium poisoning in SOFC cathodes. Further description can be found in Readme.rtf and in the paper..
The US Department of Energy Spent Fuel and Waste Science and Technology (SFWST) program is planning to conduct a series of full-scale shake table tests to simulate hypothetical earthquake conditions and record the response of surrogate spent nuclear fuel (SNF) assemblies in a canister dry storage system mockup. The shake table motions will represent a range of hypothetical earthquake conditions at hypothetical locations in the continental US to generally define the range of mechanical loads that SNF can be expected to experience during extended dry storage periods. The earthquake conditions will represent seismic hazards in the 2,000-to-20,000-year return period range. The test will use instrumented pressurized water reactor fuel assemblies with surrogate mass inside the fuel rods instead of radioactive fuel pellets. Dummy assemblies with similar mass and dimensions to actual fuel assemblies will occupy the rest of the fuel assembly locations in a SNF canister that holds 32 fuel assemblies. The canister will be located inside a mockup vertical concrete cask. Instrumentation will record the motion of the major components of the complex dynamic system, and strain gauges will be used to record the cladding strain at select locations. Preparations for the test require modeling predictions to identify the range of response of the system and to help select specific earthquake cases to be simulated on the shake table from a large set of potential cases. This paper describes the pretest nonlinear finite element modeling efforts that have been completed to date, including cask system level modeling and fuel assembly modeling in LS-DYNA. The cask system level models are critical for anticipating sliding or tipping of an unanchored cask during the test. The fuel assembly model is needed to estimate the range of cladding strain response and fuel assembly structural response to be expected during the test and cladding strain measurements will be key metrics for model validation and the development of modeling best practices after the test is concluded.
This report describes a physics-based model for creep and thermal aging in Alloy 709. Alloy 709 is an advanced austenitic alloy, targeted for use in future Sodium Fast Reactors (SFRs) and other advanced reactors. The material has superior high temperature properties compared to currently qualified 316 and 304 stainless steels. However, the available creep and thermal aging test database for Alloy 709 is significantly more limited compared to the historical materials. The physics-based model developed here is one way to accelerate the qualification of the material by providing more accurate long-term predictions for creep properties and thermal aging, compared to current empirical time-extrapolate techniques. The crystal plasticity finite element model is used to predict the deformation and failure of alloy 709. The same setup for the CPFE model is used in both the baseline model calibration process and the simulation campaigns for parameter inference. Specific constitutive choices are made for Alloy 709 to capture the primary deformation mechanisms. The dislocation creep formulation developed by Hu and Cocks is extended to account for coupled precipitation formation and the grain boundary cavitation model developed by Sham, Needleman, et al. is used to model grain boundary cavitation-induced failure. A novel update algorithm is proposed to render the semi-discrete constitutive update for the Sham-Needleman model unconditionally stable. A progressive calibration approach is adopted based on the observations that several types of material responses can be effectively decoupled. A surrogate model is trained based on full-fledged CPFE simulations to accelerate the forward model evaluations, and stochastic variational inference (SVI) is used to calibrate the unknown microstructural model parameters. The calibrated mechanistic model is used to predict the long-term creep life of Alloy 709, and the predictions are compared against classical empirical approaches.
All-solid-state Li-metal batteries (ASLMBs) represent a significant breakthrough in the quest to overcome limitations associated with traditional Li-ion batteries, particularly in energy density and safety aspects. However, widespread implementation is stymied due to a lack of profound understanding of the complex mechano-electro-chemical behavior of Li metal in the ASLMBs. Herein, operando neutron imaging and X-ray computed tomography (XCT) are leveraged to nondestructively visualize Li behaviors within ASLMBs. This approach offers real-time observations of Li evolutions, both pre- and post- occurrence of a “soft short”. The coordination of 2D neutron radiography and 3D neutron tomography enables charting of the terrain of Li metal deformation operando. Concurrently, XCT offers a 3D insight into the internal structure of the battery following a “soft short”. Despite the manifestation of a “soft short”, the persistence of Faradaic processes is observed. To study the elusive “soft short”, phase field modeling is coupled with electrochemistry and solid mechanics theory. The research unravels how external pressure curbs dendrite growth, potentially leading to dendrite fractures and thus uncovering the origins of both “soft” and “hard” shorts in ASLMBs. Furthermore, by harnessing finite element modeling, it dive deeper into the mechanical deformation and the fluidity of Li metal.
Modeling efforts were undertaken in fiscal year (FY) 2023 to evaluate the feasibility and capability for sensing of liquid water inside canister-based dry cask storage systems (DCSSs). The focus was on the development of full-scale finite element models (FEMs) of ultrasound propagation through DCSS canister components. For these initial investigations, simulation of the baseplate component was targeted, envisioning water collection at the bottom of a vertically oriented canister. The effects of internal canister components (i.e., fuel basket, fuel assemblies) on ultrasound propagation in the baseplate component were evaluated in these efforts. The environment inside a DCSS confinement is intended to be inert and free of water to prevent potential corrosion of used fuel cladding or other internal hardware. Spent fuel assemblies are dried, after storage in water pools, to make sure water has been removed from assembly cavities. However, there is some uncertainty about the amount of residual water potentially left behind in a DCSS after drying processes, because water can become trapped in cavities or other small crevices in the surfaces formed by the fuel cladding, fuel assemblies, and other internal hardware components. Considering the complex space- and time-dependent temperature profiles in DCSSs, water may be in a liquid or gas phase depending on its location in the cask and how long the cask has been in storage. Evacuating most water and oxidizing agents contained within a canister is recommended by NUREG-1536 (NRC 2010), which covers DCSSs. As summarized by Salazar et al. (2020), existing guidance typically relies on achievable vacuum pumping pressures sustained over a hold period as a signal of dryness and water removal. However, time series data about water removal from full-scale commercial drying procedures are lacking (Hanson and Alsaed 2019). A review of drying specifications from several vendors led to the conclusion that if the specifications are followed correctly, the residual moisture in DCSSs should present an insignificant risk of cladding degradation (Knoll and Gilbert 1987). A more recent analysis concluded that much larger quantities of residual water could remain in DCSSs, but the amount would still not be expected to lead to significant corrosion of fuel cladding or other internal components (Jung et al. 2013). Industry drying procedures are mostly prescriptive in nature, and operational issues arising during the process could result in incomplete drying. A summary of operational issues and potential negative effects of residual water is provided by Salazar et al. (2019). Experimental efforts are ongoing to validate the extent of water removal in a DCSS based on drying procedures used at nuclear power plants through well-designed investigations of drying process efficacy and water retention (Durbin et al. 2021; Pulido et al. 2022a, Pulido et al. 2022b). This has been approached by simulating limited portions of a DCSS internal volume and fuel assemblies. So far, these efforts have focused on the effects of potential water trapping in the dashpot region of control rod guide tubes but are expected to be expanded to include the effects of other internal hardware features and failed fuel rod cladding. A method for detection and measurement of liquid water (Meyer et al. 2022; Meyer et al. 2021), in tandem with the drying information gained through experimental investigations, provides comprehensive bases for understanding the internal conditions of DCSSs and provides technical information to support licensing decisions.
In many countries, permanent repositories for high burnup spent nuclear fuel assemblies have yet to be established. As a result, much of the spent fuel from nuclear reactors is contained within dry storage casks, with many being beyond their designed service life. The assessment of the internal structural integrity of these casks and the fuel assemblies contained therein is of critical importance for both extended storage and transport to permanent repositories. The large size, structural complexity, and inaccessibility of the interior of the casks make this task challenging. To address these difficulties, a 1:6 scaled model based closely on the design of a Transnuclear (TN-32) dry storage cask was fabricated to facilitate controlled studies of these structures in the laboratory. Vibrational spectroscopy was used to evaluate the state of the cargo and internal structures within the cask utilizing only measurements on the outer surface. Using modes identified through Finite Element modeling corresponding to those previously measured on a full-scale TN-32 cask, we report on the development of amplitude- and phase-based metrics that are sensitive to internal conditions in the lab cask. Steel rod bundles and steel shot were used as surrogates for intact and damaged fuel assemblies, and various internal configurations of these materials were investigated. Finally, the metrics were based on acquired spectra involving the (1, 2) global bending mode and the (2, 1) radial-with-shearing mode. The results show that the metrics are sensitive to the condition of a single assembly and have some ability to determine the locations of damaged and empty slots.
Accurate prediction of ductile failure is critical to Sandia’s NW mission, but the models are computationally heavy. The costs of including high-fidelity physics and mechanics that are germane to the failure mechanisms are often too burdensome for analysts either because of the person-hours it requires to input them or because of the additional computational time, or both. In an effort to deliver analysts a tool for representing these phenomena with minimal impact to their existing workflow, our project sought to develop modern data-driven methods that would add microstructural information to business-as-usual calculations and expedite failure predictions. The goal is a tool that receives as input a structural model with stress and strain fields, as well as a machine-learned model, and output predictions of structural response in time, including failure. As such, our project spent substantial time performing high-fidelity, three-dimensional experiments to elucidate materials mechanisms of void nucleation and evolution. We developed crystal-plasticity finite-element models from the experimental observations to enrich the findings with fields not readily measured. We developed engineering length-scale simulations of replicated test specimens to understand how the engineering fields evolve in the presence of fine-scale defects. Finally, we developed deep learning convolutional neural networks, and graph-based neural networks to encode the findings of the experiments and simulations and make forward predictions in time for structural performance. This project demonstrated the power of data-driven methods for model development, which have the potential to vastly increase both the accuracy and speed of failure predictions. These benefits and the methods necessary to develop them are highlighted in this report. However, many challenges remain to implementing these in real applications, and these are discussed along with potential methods for overcoming them.
The purpose of this work is to fit a previously developed empirical equation for puncture energy to simulation data. The conservative puncture energy equation could be used to expedite the process of performing calculations in the development of safety measures, avoiding the need to create complex finite element models for specific puncture scenarios. A total of 108 simulations are developed by varying coupon thickness, coupon material, probe shape, and probe diameter. The simulations are comprised of a low-velocity probe puncturing the coupons, from which the probe kinetic energy change is calculated. The empirical equation is fit to the dimensions, material properties, and energy results using a non-linear least-squares regression method within Python, which determines the two constant parameters for each fit. More statistically significant fit results are achieved by separating the data by probe shape and coupon material.
Superconducting RF (SRF) cavities usually are made of thin-wall high RRR Niobium that is susceptible to Lorentz Force Detuning (LFD) ? cavity deformation phenomena caused by high magnitude RF fields. This type of deformation can be mitigated by using an additional copper layer deposited on the outer surface of the cavity. In this paper, we present both modeling results and experimental data of high gradient test of an SRF cavity at cryogenic temperatures. It was demonstrated that LFD can be significantly reduced (factor of two) by the copper cold spray reinforcement without sacrificing cavity flexibility for tuning. We also present a finite-element model that allows us to confirm our experimental results and optimize the cavity geometry for LFD reduction with incorporated coupled RF, structural and thermal modules.
Abstract Photovoltaic (PV) modules are subjected to environmental stressors (UV exposure, temperature, and humidity) that cause degradation within the encapsulant and its interfaces with adjacent glass and cell substrates. To save experimental time and to enable long‐term assessment with intensive degradation only taking place after many years, the development of predictive models is indispensable. Previous works have modeled the delamination of the ethylene vinyl acetate (EVA) encapsulant/glass and encapsulant/cell interfaces under field aging conditions with fundamental photochemical degradation reactions that lead to molecular scission and loss of interfacial adhesion, characterized by the fracture resistance, G c . However, these models were fundamentally limited in that the following aspects were not incorporated: (i) molecular crosslinking in the field, (ii) synergistic autocatalytic interactions of degradation mechanisms, (iii) connection between degraded encapsulant structure and its mechanical properties, and (iv) rigorous treatment of the plasticity contribution to G c with finite element models. Here, we present a time‐dependent multiscale model that addresses these limitations and is applicable to a wide range of encapsulants and interfaces. For the reference EVA encapsulant and its interfaces with the glass and cell, the presented model predicts an initial rise in G c in the first 3 years of field aging from crosslinking, then a subsequent sharp decline from degradation mechanisms. We used nanoindentation to measure the changes in EVA mechanical properties over exposure time to tune the model parameters. The model predictions of G c and mechanical properties match with experimental data and show an improvement compared to previous models. The model can even predict switches in failure interfaces, such as the observed EVA/cell to EVA/glass transition. We also conducted a sensitivity analysis study by varying the degradation and crosslinking kinetic parameters to demonstrate their effects on G c . Model extensions to polyolefin elastomer‐ and silicone‐encapsulants and their interfaces are also demonstrated.
(U) Single Feature Testbeds to Reduce Uncertainty in Simulations and Tests [3] provided an overview for the need of experiments, with accompanying models and simulations, specifically designed to reduce uncertainties and build confidence in complex testing and analysis applications. The Contact Thermal Conductance (CTC) testbed is a joint effort between W-13 (formerly E-13) and MST-8. The objective of this study was to design a testbed, with accompanying finite element models, to quantify, reduce, and propagate measurement uncertainties in contact thermal conductance measurements between two mating components in WR-like thermal environments. The knowledge gained through this simplified experiment would educate more complex assessments and interaction conditions with WR-like materials and geometries.
Abstract Lithium anodes show great promise in commercial applications, but are hindered by lithium plating and dendrite growth which cause safety concerns during long‐term cell operation. Stack pressure is experimentally observed to improve cell lifetime; however, the relationship between stress and lithium deposition has remained difficult to elucidate. In this work, a transient, 3D, finite‐element model of the evolution of a lithium anode due to stripping and plating is developed. The evolution of a microscale protrusion on the anode surface is tracked over one charge‐discharge cycle with respect to stack pressure and lithium yield strength. Lithium plastic deformation, nonconformal anode‐separator contact, and separator porosity effects are accounted for. Over the course of several hours of stripping/plating, the anode surface evolves to a similar morphology under pressure regardless of the initial conditions due to lithium plastic deformation and hardening. The rate of this evolution highly depends on the applied pressure and assumed lithium yield strength.
An integrated experimental and computational approach was developed to determine the plastic strain at fracture and to investigate the fracture energy of as-received and hydrided unirradiated Zircaloy-4 nuclear cladding tubes in the hoop direction at room temperature from ring compression testing (RCT). This work builds on previous methods in the literature that were developed to obtain the mechanical properties of nuclear cladding before fracture (Young’s modulus, yield stress, and strain-hardening parameters) from experimental RCT results and extends the characterization of this material class to include material failure properties. Furthermore, a ductile damage approach was developed and implemented in a validated finite element model to predict material failure, evaluate fracture energy, and quantify the plastic strain at fracture initiation. The hydrogen content of the Zircaloy-4 specimens was varied up to 610 wppm when the failure became brittle. As expected, increasing hydrogen content caused embrittlement of the Zircaloy-4 samples tested and the plastic strain at fracture and fracture energy to decrease.
The rising usage of intermittent energy has garnered the need for large scale energy storage systems. Redox flow batteries (RFB) based energy storage system shows promising potential. Numerical simulations and machine learning approaches have been widely used to study RFB performance. The development of autonomous material discovery framework and digital twin of energy storage system usually needs to query cell performance through fast response models. In this study, two computationally efficient models are introduced: a physics-based analytical flow battery model (EZBattery), and a machine learning operator model (Deep Operator Network, denoted by DeepONet). Both models can provide cell performance near instantly, and prediction accuracy was systematically examined on an application of evaluating the performances of a 780 cm 2 aqueous organic redox flow battery (AORFB), using potential anolyte candidates in dihydroxyphenazine (DHP)-based family of organic materials. A validated computationally expansive 3-dimensional multi-physics finite element model by COMSOL was used as the ground truth and provided the training data set for the DeepONet. 1280 samples were generated with 10 properties to mimic the different possible anolyte candidates, and the cell performances were evaluated under 10 different combined operating conditions. The accuracy comparisons for the two computationally efficient models show that both models can provide comparable accuracy in predicting cell charging/discharging voltage curves. DeepONet can provide slightly higher overall accuracy than EZBattery with faster calculation speed, but highly relies on the training dataset. EZBattery does not need a training dataset and can provide interpretable physics-based explanations of the results, while being more flexible to adjust to adapt any different cell designs, flow battery architectures, and electrolyte materials.
The nonlinear hysteretic behavior of rocks under cyclic loading is a crucial area of study in geomechanics. The macroscopic response of a variety of materials has been found to be contingent upon the behavior of the micro-scale structure. This project aims to develop a functional and maintainable software package for generating a multi-phase numerical mesh and accompanying simulation files for finite element modeling used in computational mechanics solvers. Meshes generated from images often lack key preprocessing that reduces noise and prevents mesh element distortion that can increase computational cost. By incorporating user feedback throughout, grain2mesh ensures a high-fidelity mesh that can be used to model grain-scale interactions such as shearing, crack propagation, and interfacial material contrast. Scientific applications of this software include material fracturing, stress-strain analysis for natural and engineered materials, and nonlinear meso-scale analysis.
The advanced materials team investigated the use of additively manufactured metallic lattice structures for mitigating impact response in a Davis gun earth penetrator impact experiment. High-fidelity finite element models were developed and validated with quasistatic experiments. These models were then used to simulate the response of such lattices when subjected to the acceleration loads expected in the Davis gun experiment. Results reveal how the impact mitigation performance of lattices can change drastically at a certain relative density. Based on these observations, an experiment deck was designed to probe the response of lattices with different relative densities during the Davis gun phase 2 shots. The expected performance of these lattices is predicted before testing based on simulation results. The results of the Davis gun phase 2 shots are expected to provide data which will be used to assess the predictive capability of the finite element simulations in such a complex impact environment.
This paper presents a microstructure-based model for low cycle fatigue (LCF) behavior and life of Nickel-based alloy Hastelloy X manufactured using laser-powder bed fusion (L-PBF) additive manufacturing (AM). AM Hastelloy X, a solution-strengthened alloy, is tested at elevated temperature under fully reversed LCF conditions at different strain levels. A generalized plane strain finite element model is generated from electron backscatter diffraction (EBSD) characterization. The constitutive behavior of the material under fatigue is modeled using crystal plasticity and calibrated with both monotonic tensile and cyclic stress–strain data. The fatigue micro-crack initiation and propagation in the microstructure is modeled using a modified Chaboche fatigue damage model. An embedded boundary condition with a homogenous medium is used to apply the cyclic deformation and prevent numerically introduced over-constraints during fatigue simulation. A ‘cycle-jump’ method is used to accelerate the fatigue simulation and reduce the computational cost. The simulation results are compared to LCF experiments, showing satisfactory matches in cyclic stress behavior and number of cycles to macro-crack initiation for all applied strain ranges. In addition, the model illustrates the potential for quantifying microscale fatigue life impacting factors such as microstructure and surface roughness, which is needed to accurately quantify the reliability of AM components in service.
In this work, a method is developed to calculate the length into a sample to which a Frequency Domain Thermoreflectance (FDTR) measurement is sensitive. Sensing depth and sensing radius are defined as limiting cases for the spherically spreading FDTR measurement. A finite element model for FDTR measurements is developed in COMSOL multiphysics and used to calculate sensing depth and sensing radius for silicon and silicon dioxide samples for a variety of frequencies and laser spot sizes. The model is compared to experimental FDTR measurements. Design recommendations for sample thickness are made for experiments where semi-infinite sample depth is desirable. For measurements using a metal transducer layer, the recommended sample thickness is three thermal penetration depths, as calculated from the lowest measurement frequency.