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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Modeling mesoscale fission gas behavior in UO2 by directly coupling the phase field method to spatially resolved cluster dynamics

Abstract Fission gas release within uranium dioxide nuclear fuel occurs as gas atoms diffuse through grains and arrive at grain boundary (GB) bubbles; these GB bubbles grow and interconnect with grain edge bubbles; and grain edge tunnels grow and connect to free surfaces. In this study, a hybrid multi-scale/multi-physics simulation approach is presented to investigate these mechanisms of fission gas release at the mesoscale. In this approach, fission gas production, diffusion, clustering to form intragranular bubbles, and re-solution within grains are included using spatially resolved cluster dynamics in the Xolotl code. GB migration and intergranular bubble growth and coalescence are included using the phase field method in the MARMOT code. This hybrid model couples Xolotl to MARMOT using the MultiApp and Transfer systems in the MOOSE framework, with Xolotl passing the arrival rate of gas atoms at GBs and intergranular bubble surfaces to MARMOT and MARMOT passing evolved GBs and bubble surface positions to Xolotl. The coupled approach performs well on the two-dimensional simulations performed in this work, producing similar results to the standard phase field model when Xolotl does not include fission gas clustering or re-solution. The hybrid model performs well computationally, with a negligible cost of coupling Xolotl and MARMOT and good parallel scalability. The hybrid model predicts that intragranular fission gas clustering and bubble formation results in up to 70% of the fission gas being trapped within grains, causing the increase in the intergranular bubble fraction to slow by a factor of six. Re-solution has a small impact on the fission gas behavior at 1800 K but it has a much larger impact at 1000 K, resulting in a twenty-times increase in the concentration of single gas atoms within grains. Due to the low diffusion rate, this increase in mobile gas atoms only results in a small acceleration in the growth of the intergranular bubble fraction. Finally, the hybrid model accounts for migrating GBs sweeping up gas atoms. This results in faster intergranular bubble growth with smaller initial grain sizes, since the additional GB migration results in more immobile gas clusters reaching GBs.

Kim, Dong-Uk↗
DOE OSTI2023

Black and gray box learning of amplitude equations: Application to phase field systems

Here, we present a data-driven approach to learning surrogate models for amplitude equations and illustrate its application to interfacial dynamics of phase field systems. In particular, we demonstrate learning effective partial differential equations describing the evolution of phase field interfaces from full phase field data. We illustrate this on a model phase field system, where analytical approximate equations for the dynamics of the phase field interface (a higher-order eikonal equation and its approximation, the Kardar-Parisi-Zhang equation) are known. For this system, we discuss data-driven approaches for the identification of equations that accurately describe the front interface dynamics. When the analytical approximate models mentioned above become inaccurate, as we move beyond the region of validity of the underlying assumptions, the data-driven equations outperform them. In these regimes, going beyond black box identification, we explore different approaches to learning data-driven corrections to the analytically approximate models, leading to effective gray box partial differential equations.

42 ENGINEERING↗
DOE OSTI2021

Non-intrusive data-driven model reduction for differential–algebraic equations derived from lifting transformations

In this paper we present a non-intrusive data-driven approach for model reduction of nonlinear systems. The approach considers the particular case of nonlinear partial differential equations (PDEs) that form systems of partial differential–algebraic equations (PDAEs) when lifted to polynomial form. Such systems arise, for example, when the governing equations include Arrhenius reaction terms (e.g., in reacting flow models) and thermodynamic terms (e.g., the Helmholtz free energy terms in a phase-field solidification model). Using the known structured form of the lifted algebraic equations, the approach computes the reduced operators for the algebraic equations explicitly, using straightforward linear algebra operations on the basis matrices. The reduced operators for the differential equations are inferred from lifted snapshot data using operator inference, which solves a linear least squares regression problem. The approach is illustrated for the nonlinear model of solidification of a pure material. The lifting transformations reformulate the solidification PDEs as a system of PDAEs that have cubic structure. The operators of the lifted system for this solidification example have affine dependence on key process parameters, permitting us to learn a parametric reduced model with operator inference. Numerical experiments show the effectiveness of the resulting reduced models in capturing key aspects of the solidification dynamics.

42 ENGINEERING↗
DOE OSTI2019

Radiation-induced Swelling and Microcracking in SiC Cladding for LWRs

After the Fukushima accident in 2011, SiC has attracted considerable attention as a potential material for fuel cladding that can offer increased accident tolerance in light water reactors (LWRs) compared to zircaloy cladding. Existing measurements on nuclear grade SiC/SiC composites made of high-purity, stoichiometric SiC have already shown very good radiation tolerance in various harsh environments. However, there are a number of challenges that need to be addressed before SiC composites can be successfully used in LWR technologies. One of them is irradiation-induced swelling, which can lead to development of significant stresses in the matrix, followed by microcracking and subsequent release of fission products. Models of irradiation-induced swelling had been largely empirical and therefore they cannot be used to predict the microstructural dependence of microcracking. Existing models are also limited by the lack of understanding of defects that form due to irradiation in the range of temperatures relevant to fuel cladding in LWRs (<1000°C). Many of the defects in this regime of temperature are too small to be detected with traditional transmission electron microscopy (TEM) techniques. In fact the defects observed in traditional TEM account only for 10-45% of the swelling measured in irradiated SiC. Here, we developed a multi-scale simulation methodology combined with state of the art experimental imaging techniques based on aberration corrected scanning transmission electron microscopy (STEM) to provide a scientific basis for predictive models of radiation swelling and microcracking in SiC. Specifically, we have developed a phase field fracture model that predicts microcrack initiation and growth in polycrystalline SiC. The model accounts for single crystal anisotropy, including anisotropy in the elastic constants and anisotropic surface energy. It also considers the reduced fracture strength of grain boundaries. The model was implemented using the MARMOT mesoscale nuclear materials tool. We have parameterized the model for SiC using molecular dynamics simulation results. The model has been verified using single crystal, bicrystal, and polycrystal examples. Swelling model has also been developed based on cluster dynamics simulations of defect evolution in SiC. We found that point defects and small defect clusters have a significant contribution to swelling. This finding is important because point defects and very small defect clusters are not visible in experiments (e.g., in TEM). Combined modeling and experiments can bring critical insights into how swelling depends on irradiation conditions and the microstructure. In situ nanoindentation tests and ex situ nanoindentation tests were performed on assynthesized 3C-SiC and 3C-SiC irradiated under different radiation conditions. Without radiation, bend contour movement and residual bend contours after indentation demonstrate some room temperature plastic flow and residual plastic deformation. Plastic deformation recovery is also observed on unirradiated samples during unloading. Radiation-induced embrittlement is demonstrated by differences in crack geometry in in situ tests, and is consistent with ex situ test results. Failure by single, straight, cracks indicates decreased fracture toughness due to irradiation in 3C-SiC. Future work includes coupling of the swelling and fracture models and development of a master equation that can be used in the BISON code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model

Laser powder bed fusion (LPBF) Ti-6Al-4V is widely studied for use in structural applications in aerospace and medical industries, but mechanical anisotropy and microstructural inhomogeneity prohibits its wider adoption. Although successful microstructure prediction models have been developed, a remaining challenge is their limited integration across length/time scales and validation by experimental studies. Here, this work proposes a physics-augmented machine learning surrogate model to unite predictions of LPBF temperature, β phase morphology and texture, and α/α’ formation into a single framework that is calibrated and validated with experiments. First, a phase field (PF) model of the martensitic β→α’ transformation is developed and calibrated using data from in-situ synchrotron cyclic heating/cooling studies quantifying the variation of α phase fraction with time. In parallel, an established finite difference-Monte Carlo (FDMC) model predicts the part-scale temperature profile and β grain formation during solidification. A dataset is developed using LPBF cyclic temperature descriptors from the FDMC model as inputs and corresponding α/α’ phase fraction and width from the PF model as outputs. Five machine learning (ML) regression models are tested and optimized, having mean absolute error in testing ≤ 4 %, and the k-nearest neighbors (KNN) model is selected as the best performing. The KNN model is called at the nodal level during post-processing of the FDMC model to replace and downscale the response of the PF model. The combined agility and accuracy of the hybrid FDMC-ML model enables part-scale microstructure predictions that can be further used for property predictions to accelerate AM process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microstructure modeling of nuclear structural materials: Recent progress and future directions

Modeling and simulation of microstructures are essential to understand the complex responses and behaviors of nuclear materials in extreme environments. The needs to assess the extended life operation as well as the growing interest in accelerating nuclear materials development and qualification have stimulated the use of high-fidelity multiscale models aided by empirical and ab initio data. This paper reviews the role of various models across different length and time scales in investigating irradiation effects on microstructure evolution and degradation, in particular the embrittlement caused by radiation induced or enhanced formation of nanoscale chemical heterogeneities. The strength and limitations of these models, including classical rate theories, cluster dynamics, phase-field methods, and atomistic models informed by ab initio energies, are discussed with seminal examples. Challenges regarding the lack of thermo-kinetic data and theoretical treatments considering chemical complexities and magnetic excitations, as well as the stabilizing effect by excess point defects in nuclear structural materials are presented, along with potential solutions based on ab initio informed surrogate energy models and statistical sampling by Monte Carlo simulations. Further, the review then highlights the opportunities to leverage the advantages of different methods by establishing hybrid models by shared variables or coupled codes and applications. Finally, the review concludes with forward-looking remarks on how the use of physics-based models can aid the improvement of machine-learning models of property degradation and vice versa.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Modeling Graphite Behavior (Fracture and Oxidation)

Purpose of this work Oxidation modeling Oxidation of full-scale components Local properties in oxidized components Graphite fracture strength Phase field fracture modeling Microstructurally informed fracture model IG-110 tensile results and comparison to experiment Work in progress and future work

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Investigation of wing crack formation with a combined phase-field and experimental approach

Fractures that propagate off of weak slip planes are known as wing cracks and often play important roles in both tectonic deformation and fluid flow across reservoir seals. Previous numerical models have produced the basic kinematics of wing crack openings but generally have not been able to capture fracture geometries seen in nature. Here we present both a phase-field modeling approach and a physical experiment using gelatin for a wing crack formation. By treating the fracture surfaces as diffusive zones instead of as discontinuities, the phase-field model does not require consideration of unpredictable rock properties or stress inhomogeneities around crack tips. It is shown by benchmarking the models with physical experiments that the numerical assumptions in the phase-field approach do not affect the final model predictions of wing crack nucleation and growth. With this study, we demonstrate that it is feasible to implement the formation of wing cracks in large scale phase-field reservoir models.

58 GEOSCIENCES↗
DOE OSTI2012

Mesoscale Benchmark Demonstration, Problem 1: Mesoscale Simulations of Intra-granular Fission Gas Bubbles in UO 2 under Post-irradiation Thermal Annealing

A study was conducted to evaluate the capabilities of different numerical methods used to represent microstructure behavior at the mesoscale for irradiated material using an idealized benchmark problem. The purpose of the mesoscale benchmark problem was to provide a common basis to assess several mesoscale methods with the objective of identifying the strengths and areas of improvement in the predictive modeling of microstructure evolution. In this work, mesoscale models (phase-field, Potts, and kinetic Monte Carlo) developed by PNNL, INL, SNL, and ORNL were used to calculate the evolution kinetics of intra-granular fission gas bubbles in UO 2 fuel under post-irradiation thermal annealing conditions. The benchmark problem was constructed to include important microstructural evolution mechanisms on the kinetics of intra-granular fission gas bubble behavior such as the atomic diffusion of Xe atoms, U vacancies, and O vacancies, the effect of vacancy capture and emission from defects, and the elastic interaction of non-equilibrium gas bubbles. An idealized set of assumptions was imposed on the benchmark problem to simplify the mechanisms considered. The capability and numerical efficiency of different models are compared against selected experimental and simulation results. These comparisons find that the phase-field methods, by the nature of the free energy formulation, are able to represent a larger subset of the mechanisms influencing the intra-granular bubble growth and coarsening mechanisms in the idealized benchmark problem as compared to the Potts and kinetic Monte Carlo methods. It is recognized that the mesoscale benchmark problem as formulated does not specifically highlight the strengths of the discrete particle modeling used in the Potts and kinetic Monte Carlo methods. Future efforts are recommended to construct increasingly more complex mesoscale benchmark problems to further verify and validate the predictive capabilities of the mesoscale modeling methods used in this study.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Vulnerable window of yield strength for swelling-driven fracture of phase-transforming battery materials

Abstract Despite numerous experimental and theoretical investigations of the mechanical behavior of high-capacity Si and Ge Li-ion battery anodes, our basic understanding of swelling-driven fracture in these materials remains limited. Existing theoretical studies have provided insights into elasto-plastic deformations caused by large volume change phase transformations, but have not modeled fracture explicitly beyond Griffith’s criterion. Here, we use a multi-physics phase-field approach to model self-consistently anisotropic phase transformation, elasto-plastic deformation, and crack initiation and propagation during lithiation of Si nanopillars. Our computational results reveal that fracture occurs within a “vulnerable window” inside the two-dimensional parameter space of yield strength and fracture energy and highlight the importance of taking into account the surface localization of plastic deformation to accurately predict the magnitude of tensile stresses at the onset of fracture. They further demonstrate how the increased robustness of hollow nanopillars can be understood as a direct effect of anode geometry on the size of this vulnerable window. Those insights provide an improved theoretical basis for designing next-generation mechanically stable phase-transforming battery materials undergoing large volume changes.

25 ENERGY STORAGE↗
DOE OSTI2018

An optimization‐based phase‐field method for continuous‐discontinuous crack propagation

Summary A new continuous‐discontinuous strategy for the computational modeling of crack propagation within the context of phase‐field models of fracture is presented. The method is designed to introduce and update a sharp crack surface within an evolving damage band and to enhance the kinematics of the finite element approximation accordingly. The proposed approach relies on three key elements. First, we propose the use of a crack length functional to provide a trigger for initiating a continuous to discontinuous transition. Next, the crack path identification is addressed by introducing the concept of an auxiliary damage field that varies with an extension of the sharp crack surface. The sharp crack surface is extended through an optimization algorithm, in which the difference between the auxiliary field and the actual damage field stemming from the phase‐field framework is minimized. Finally, a strong discontinuity is inserted in the wake of the diffuse crack tip with the extended finite element method, completing the continuous to discontinuous transition. Several benchmark problems in two‐dimensional quasi‐static fracture mechanics are presented to demonstrate the accuracy and robustness of the method.

Geelen, Rudy J. M.↗
NASA NTRS1976

Summary of nozzle-exhaust plume flowfield analyses performed in support of space shuttle applications

Results are summarized of various analyses of the exhaust plumes of the space shuttle. Specific topics discussed included: the development of the two-phase plume flow field model, including finite rate chemistry and free molecular effects; analyses of exhaust plume simulation studies; verification of the analytical two-phase plume flow field model; and complete documentation of the two-phase plume computer code.

Smith, S. D.↗