Computationally Efficient Phase-field Simulation Studies Using RVE Sampling and Statistical Analysis

Schwarze, Christian; Kamachali, Reza Darvishi; Kühbach, Markus; Mießen, Christian; Tegeler, Marvin; Barrales‐Mora, Luis A.; Steinbach, Ingo; Gottstein, Günter

doi:10.1016/j.commatsci.2018.02.005

Cited by 16 publications

(8 citation statements)

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“…Advanced full-field simulation techniques and mean-field modelling are especially beneficial in material design to establish processing ↔ microstructure ↔ properties relations. In this regard, the current trend of developing multi-physics simulation tools, enhanced by cluster supercomputing and statistical analysis, enables the investigation of coupled thermo-chemomechanical phenomena [1,2,3]. Quantitative modelling and simulation of various coupled physical phenomena can be regarded as the next challenge in this context.…”

Section: Introductionmentioning

confidence: 99%

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Numerical Benchmark of Phase-Field Simulations with Elastic Strains: Precipitation in the Presence of Chemo-Mechanical Coupling

Kamachali

Schwarze

Lin

et al. 2018

Computational Materials Science

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Phase-field studies of solid-state precipitation under strong chemo-mechanical coupling are performed and benchmarked against the existing analytical solutions. The open source software packages OpenPhase and DAMASK are used for the numerical studies. Solutions for chemical diffusion and static mechanical equilibrium are investigated individually followed by a chemo-mechanical coupling effect arising due to composition dependence of the elastic constants. The accuracy of the numerical solutions versus the analytical solutions is quantitatively discussed. For the chemical diffusion benchmark, an excellent match, with a deviation < 0.1%, was obtained. For the static mechanical equilibrium benchmark Eshelby problem was considered. In this case, a deviation of 5% was observed in the normal component of the stress, while the results from the diffuse interface (OpenPhase) and sharp interface (DAMASK) models were slightly different. In the presence of the chemomechanical coupling, the concentration field around a static precipitate was benchmarked for different coupling coefficients that represent the strength of composition dependency of elastic constants. It is found that the deviation increases proportional to the coupling coefficient. Finally, the interface kinetics in the presence of the considered chemo-mechanical coupling were studied using OpenPhase and a hybrid OpenPhase-DAMASK implementation, replacing the mechanical solver of OpenPhase with DAMASK's. The observed deviations in the benchmark studies are discussed to provide guidance for the use of these results in studying further phase transformation models and implementations involving diffusion, elasticity and chemo-mechanical coupling effect.

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Section: Introductionmentioning

confidence: 99%

“…The simulations are performed at 473 K where Li in Al has a diffusion coefficient of D = 1.2 × 10 −18 m 2 s −1 and an atomic mobility of M = D/RT = 3.4 × 10 −22 mol m 2 J −1 s −1[76]. The simulation box sizes are chosen as (64 dx)3 . All simulations are conducted using periodic boundary conditions.…”

mentioning

confidence: 99%

Numerical Benchmark of Phase-Field Simulations with Elastic Strains: Precipitation in the Presence of Chemo-Mechanical Coupling

Kamachali

Schwarze

Lin

et al. 2018

Computational Materials Science

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“…applied to columnar growth competition with a cellular automaton [379]); and RVE sampling and statistical analysis (e.g. applied to the precipitation and growth of δ precipitates in Al-Li alloys [380]).…”

Section: Multiscale Strategies and "Upscaling"mentioning

confidence: 99%

Phase-field modeling of microstructure evolution: Recent applications, perspectives and challenges

Tourret¹,

Liu²,

Llorca³

2021

Preprint

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We briefly review the state-of-the-art in phase-field modeling of microstructure evolution. The focus is placed on recent applications of phase-field simulations of solid-state microstructure evolution and solidification that have been compared and/or validated with experiments. They show the potential of phase-field modeling to make quantitative predictions of the link between processing and microstructure. Finally, some current challenges in extending the application of phase-field models within the context of integrated computational materials engineering are mentioned.

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“…In this case, the microstructure morphology and grain interactions are considered explicitly (Figure 3), allowing to evaluate the influence of local microscale heterogeneities on global material behavior. Commonly used approaches to simulate the evolution of such microstructures are the phase-field (PF) [27][28][29] and the level-set (LS) methods, [30][31][32] based on computational continuum mechanics principles.…”

Section: Introductionmentioning

confidence: 99%

Computationally Efficient Cellular Automata‐Based Full‐Field Models of Static Recrystallization: A Perspective Review

Madej

Sitko

2022

steel research int.

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The evolution of metallic material microstructures under thermomechanical treatment is a critical element that influences the in‐use properties of the final product. In the last four decades, many researchers around the world focused on reliable numerical recreation of phenomena that occur during metal processing. Particular attention is put on the phase transformation, texture evolution, and recrystallization predictions to simulate material behavior at the subsequent processing stages. In recent years, approaches taking into account explicit representation of morphological elements during simulation are becoming more frequently used even at the industrial scale. One of those methods addressed within the current article in application to static recrystallization (SRX) modeling is the cellular automata (CA) approach. A review of the CA‐based full‐field SRX models highlights the progress in capabilities of developed approaches in consideration of various mechanisms controlling microstructure evolution like heterogeneous energy distribution, probabilistic or physics‐based nucleation, grain growth driven by different driving forces, or grain boundary migration is presented within the article. The issue of the computational time associated with 3D CA modeling is particularly addressed. Possible approaches to reducing simulation times based on efficient use of modern computer architecture are finally evaluated as guidelines for further model development.

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Computationally Efficient Phase-field Simulation Studies Using RVE Sampling and Statistical Analysis

Cited by 16 publications

References 50 publications

Numerical Benchmark of Phase-Field Simulations with Elastic Strains: Precipitation in the Presence of Chemo-Mechanical Coupling

Numerical Benchmark of Phase-Field Simulations with Elastic Strains: Precipitation in the Presence of Chemo-Mechanical Coupling

Phase-field modeling of microstructure evolution: Recent applications, perspectives and challenges

Computationally Efficient Cellular Automata‐Based Full‐Field Models of Static Recrystallization: A Perspective Review

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