On the stability and accuracy of partially and fully implicit schemes for phase field modeling
Not Available
Xu, Jinchao↗
SEARCH · Engineering Papers
Results for “phase field models”
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Pressure and fluid-driven fracture propagation in porous media using an adaptive finite element phase field model
This work presents phase field fracture modeling in heterogeneous porous media. We develop robust and efficient numerical algorithms for pressure-driven and fluid-driven settings in which the focus relies on mesh adaptivity in order to save computational cost for large-scale 3D applications. In the fluid-driven framework, we solve for three unknowns pressure, displacements and phase field that are treated with a fixed-stress iteration in which the pressure and the displacement–phase-field system are decoupled. The latter subsystem is solved with a combined Newton approach employing a primal–dual active set method in order to account for crack irreversibility. Numerical examples for pressurized fractures and fluid filled fracture propagation in heterogeneous porous media demonstrate our developments. Finally in particular, mesh refinement allows us to perform systematic studies with respect to the spatial discretization parameter.
42 ENGINEERING↗
Phase-field modeling of the beta to omega phase transformation in Zr–Nb alloys
A three-dimensional elastoplastic phase-field model is developed, using the Finite Element Method (FEM), for modeling the athermal beta to omega phase transformation in Zr–Nb alloys by including plastic deformation and strain hardening of the material. The microstructure evolution during athermal transformation as well as under different stress states, e.g. uni-axial tensile and compressive, bi-axial tensile and compressive, shear and tri-axial loadings, is studied. The effects of plasticity, stress states and the stress loading direction on the microstructure evolution as well as on the mechanical properties are studied. The input data corresponding to a Zr – 8 at.% Nb alloy are acquired from experimental studies as well as by using the CALPHAD method. Our simulations show that the four different omega variants grow as ellipsoidal shaped particles. Our results show that due to stress relaxation, the athermal phase transformation occurs slightly more readily in the presence of plasticity compared to that in its absence. The evolution of omega phase is different under different stress states, which leads to the differences in the mechanical properties of the material. The variant selection mechanism, i.e. formation of different variants under different stress loading directions, is also nicely captured by our model.
36 MATERIALS SCIENCE↗
Phase-field Modeling of Displacive Phase Transformations in Elastically Anisotropic and Inhomogeneous Polycrystals
Abstract not provided
36 MATERIALS SCIENCE↗
Grand-potential-based phase-field model for multiple phases, grains, and chemical components
Not Available
Aagesen, Larry K.↗
Phase-Field Modeling of Two Phase Fluid Filled Fractures in a Poroelastic Medium
Not provided.
Mathematics↗
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. Finally, this strategy can be used to parameterize phase-field models of fission gas bubble growth.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
MARMOT Phase-Field Model for the U-Si System
A phase-field model for the U-Si system has been implemented in MARMOT. The free energies for the phases relevant to accident-tolerant fuel applications (U 3 Si 2 , USi, U 3 Si, and liquid) were implemented as free energy materials within MARMOT. A new three-phase phase-field model based on the concepts of the Kim-Kim-Suzuki two-phase model was developed and implemented in the MOOSE phase-field module. Key features of this model are that two-phase interfaces are stable with respect to formation of the third phase, and that arbitrary phase free energies can be used. The model was validated using a simplified three-phase system and the U-Si system. In the U-Si system, the model correctly reproduced three-phase coexistence in a U 3 Si 2 -liquid-USi system at the eutectic temperature, solidification of a three-phase mixture below the eutectic temperature, and complete melting of a three-phase mixture above the eutectic temperature.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
A quantitative phase-field model for crevice corrosion
A quantitative phase-field model is developed for the investigation of crevice corrosion of iron in salt water. Six types of ionic species and some associated chemical reactions have been considered. In addition to the transient distributions of ion concentrations and electric potential in the electrolyte, some physical and chemical properties related to corrosion, such as overpotential, pH value and corrosion rate, under different metal potentials are studied. Benchmarking of the phase-field model against a sharp interface model is conducted. The corrosion rates predicted by the models are in the same order of magnitudes with experimental results.
Xiao, Z. H.↗
A phase-field model for non-isothermal phase transformation and plasticity in polycrystalline yttria-stabilized tetragonal zirconia
Here we propose an elastoplastic phase-field (PF) model to investigate the mechanics of tetragonal-to-monoclinic phase transformation (TMPT) and elastoplastic deformation of polycrystalline yttria-stabilized tetragonal zirconia (YSTZ). A Landau polynomial with non-vanishing chemical energy at the equilibrium temperature is introduced to account for the actual formation energies of the phases. The effects of different grain orientations, latent heat, and temperature on TMPT and deformation mechanisms are considered. The suppressive transformation effects of the grain boundaries (GBs) is modeled using an inhomogeneous kinetic coefficient in the bulk and GBs. The simulation results for single crystals demonstrate the capability of the model to reproduce the orientation-dependent compressive deformation of YSTZ similar to atomistic simulations and micropillar experiments. The single crystal with [100] crystallographic orientation along the loading direction (SC[100]) displays both TMPT and plasticity, SC[101] experiences only phase transformation, while SC[001] undergoes only plastic yielding. The TMPT induced by compressive loading exhibits shape memory effect (SME) below the equilibrium transformation temperature and pseudoelasticity above it, while the critical transformation stress increases with increasing loading temperature. The irrecoverable plastic strain is found to trap a part of the monoclinic phase, which prevents a complete reverse transformation. The polycrystalline cases also display SME and PE at low and high temperatures, respectively. Due to the orientation differences between grains and the stress concentrations at geometric nonlinearities, plastic deformation occurs in polycrystalline YSTZ for an applied load less than the yield stress. The results suggest a possible limitation of plasticity and an improvement of the shape recovery of YSTZ if one can control the orientation of the grains and/or increase the density of stacking faults at the GBs during material processing.
36 MATERIALS SCIENCE↗
Benchmark problems for numerical implementations of phase field models
Here, we present the first set of benchmark problems for phase field models that are being developed by the Center for Hierarchical Materials Design (CHiMaD) and the National Institute of Standards and Technology (NIST). While many scientific research areas use a limited set of well-established software, the growing phase field community continues to develop a wide variety of codes and lacks benchmark problems to consistently evaluate the numerical performance of new implementations. Phase field modeling has become significantly more popular as computational power has increased and is now becoming mainstream, driving the need for benchmark problems to validate and verify new implementations. We follow the example set by the micromagnetics community to develop an evolving set of benchmark problems that test the usability, computational resources, numerical capabilities and physical scope of phase field simulation codes. In this paper, we propose two benchmark problems that cover the physics of solute diffusion and growth and coarsening of a second phase via a simple spinodal decomposition model and a more complex Ostwald ripening model. We demonstrate the utility of benchmark problems by comparing the results of simulations performed with two different adaptive time stepping techniques, and we discuss the needs of future benchmark problems. The development of benchmark problems will enable the results of quantitative phase field models to be confidently incorporated into integrated computational materials science and engineering (ICME), an important goal of the Materials Genome Initiative.
36 MATERIALS SCIENCE↗
Elucidating the complex interplay between thermodynamics, kinetics, and electrochemistry in battery electrodes through phase-field modeling
Abstract This article highlights applications of phase-field modeling to electrochemical systems, with a focus on battery electrodes. We first provide an overview on the physical processes involved in electrochemical systems and applications of the phase-field approach to understand the thermodynamic and kinetic mechanisms underlying these processes. We employ two examples to highlight how realistic thermodynamics and kinetics can naturally be incorporated into phase-field modeling of electrochemical processes. One is a composite battery cathode with an intercalation compound (Li x FePO 4 ) as the electrochemically active material, and the other is a displacement reaction compound (Li–Cu–TiS 2 ). With the input parameters mostly from atomistic calculations and experimental measurements, phase-field simulations allowed us to untangle the interactions among transport, reaction, electricity, chemistry, and thermodynamics that lead to highly complex evolution of the materials within battery electrodes. The implications of these observations for battery performance and degradation are discussed. Graphical abstract
Andrews, W. Beck (ORCID:0000000287824621)↗
An end-to-end deep learning method for solving nonlocal Allen–Cahn and Cahn–Hilliard phase-field models
Here, we propose an efficient end-to-end deep learning method for solving nonlocal Allen–Cahn (AC) and Cahn–Hilliard (CH) phase-field models. One motivation for this effort emanates from the fact that discretized partial differential equation-based AC or CH phase-field models result in diffuse interfaces between phases, with the only recourse for remediation is to severely refine the spatial grids in the vicinity of the true moving sharp interface whose width is determined by a grid-independent parameter that is substantially larger than the local grid size. In this work, we introduce non-mass conserving nonlocal AC or CH phase-field models with regular, logarithmic, or obstacle double-well potentials. Because of non-locality, some of these models feature totally sharp interfaces separating phases. The discretization of such models can lead to a transition between phases whose width is only a single grid cell wide. Another motivation is to use deep learning approaches to ameliorate the otherwise high cost of solving discretized nonlocal phase-field models. To this end, loss functions of the customized neural networks are defined using the residual of the fully discrete approximations of the AC or CH models, which results from applying a Fourier collocation method and a temporal semi-implicit approximation. To address the long-range interactions in the models, we tailor the architecture of the neural network by incorporating a nonlocal kernel as an input channel to the neural network model. We then provide the results of extensive computational experiments to illustrate the accuracy, predictive capabilities, and cost reductions of the proposed method.
42 ENGINEERING↗
Order parameter re-mapping algorithm for 3D phase field model of grain growth using FEM
Phase field modeling (PFM) is a well-known technique for simulating microstructural evolution. To model grain growth using PFM, typically each grain is assigned a unique non-conserved order parameter and each order parameter field is evolved in time. Traditional approaches using a one-to-one mapping of grains to order parameters present a challenge when modeling large numbers of grains due to the computational expense of using many order parameters. This problem is exacerbated when using an implicit finite element method (FEM), as the global matrix size is proportional to the number of order parameters. While previous work has developed methods to reduce the number of required variables and thus computational complexity and run time, none of the existing approaches can be applied for an implicit FEM implementation of PFM. Here, we present a modular, dynamic, scalable reassignment algorithm suitable for use in such a system. Polycrystal modeling with grain growth and stress require careful tracking of each grain’s position and orientation which is lost when using a reduced order parameter set. In conclusion, the method presented in this paper maintains a unique ID for each grain even after reassignment, to allow the PFM to be tightly coupled to calculations of the stress throughout the polycrystal. Implementation details and comparative results of our approach are presented.
36 MATERIALS SCIENCE↗
Adaptive-Grid Methods for Phase Field Models of Microstructure Development
In this work the authors show how the phase field model can be solved in a computationally efficient manner that opens a new large-scale simulational window on solidification physics. Our method uses a finite element, adaptive-grid formulation, and exploits the fact that the phase and temperature fields vary significantly only near the interface. We illustrate how our method allows efficient simulation of phase-field models in very large systems, and verify the predictions of solvability theory at intermediate undercooling. We then present new results at low undercoolings that suggest that solvability theory may not give the correct tip speed in that regime. We model solidification using the phase-field model used by Karma and Rappel.
Provatas, Nikolas↗
Deep operator network surrogate for phase-field modeling of metal grain growth during solidification
A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the the Allen-Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400° C at an average rate of 30° C / min , traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal's microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. Further, these relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.
36 MATERIALS SCIENCE↗