Quantitative 3D phase field modelling of solidification using next-generation adaptive mesh refinement

Greenwood, Michael; Shampur, Kaushik; Ofori-Opoku, Nana; Pinomaa, Tatu; Wang, Lei; Gurevich, S. M.; Provatas, Nikolas

doi:10.1016/j.commatsci.2017.09.029

Cited by 60 publications

(34 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Often, only the numerical methods that are easiest to implement are used, namely finite difference methods and Fourier-spectral methods 1,3 . While substantial efforts over the past twenty years have been focused on techniques that greatly improve performance such as adaptive mesh refinement and multilevel parallelism [11][12][13][14][15][16][17] , these techniques are often neglected in userdeveloped single-purpose codes, as they are time-consuming to implement.…”

Section: Introductionmentioning

confidence: 99%

PRISMS-PF: A general framework for phase-field modeling with a matrix-free finite element method

et al. 2020

View full text Add to dashboard Cite

A new phase-field modeling framework with an emphasis on performance, flexibility, and ease of use is presented. Foremost among the strategies employed to fulfill these objectives are the use of a matrix-free finite element method and a modular, application-centric code structure. This approach is implemented in the new open-source PRISMS-PF framework. Its performance is enabled by the combination of a matrix-free variant of the finite element method with adaptive mesh refinement, explicit time integration, and multilevel parallelism. Benchmark testing with a particle growth problem shows PRISMS-PF with adaptive mesh refinement and higher-order elements to be up to 12 times faster than a finite difference code employing a second-order-accurate spatial discretization and first-order-accurate explicit time integration. Furthermore, for a two-dimensional solidification benchmark problem, the performance of PRISMS-PF meets or exceeds that of phase-field frameworks that focus on implicit/semi-implicit time stepping, even though the benchmark problem's small computational size reduces the scalability advantage of explicit timeintegration schemes. PRISMS-PF supports an arbitrary number of coupled governing equations. The code structure simplifies the modification of these governing equations by separating their definition from the implementation of the numerical methods used to solve them. As part of its modular design, the framework includes functionality for nucleation and polycrystalline systems available in any application to further broaden the phenomena that can be used to study. The versatility of this approach is demonstrated with examples from several common types of phase-field simulations, including coarsening subsequent to spinodal decomposition, solidification, precipitation, grain growth, and corrosion.

show abstract

Section: Introductionmentioning

confidence: 99%

PRISMS-PF: A general framework for phase-field modeling with a matrix-free finite element method

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The phase field simulations use the finite difference method with explicit Euler forward time stepping, with the time step size set to 0.8 of the linear stability limit for the heat diffusion equation. The mesh was adaptively refined to capture gradients of order parameter and dimensionless temperature with the software platform introduced in [37]. The used phase field model parameters are shown in Table 2.…”

Section: Rapid Solidification Microstructuresmentioning

confidence: 99%

Process-Structure-Properties-Performance Modeling for Selective Laser Melting

Pinomaa

Yashchuk

Lindroos

et al. 2019

Metals

Self Cite

View full text Add to dashboard Cite

Selective laser melting (SLM) is a promising manufacturing technique where the part design, from performance and properties process control and alloying, can be accelerated with integrated computational materials engineering (ICME). This paper demonstrates a process-structure-properties-performance modeling framework for SLM. For powder-bed scale melt pool modeling, we present a diffuse-interface multiphase computational fluid dynamics model which couples Navier-Stokes, Cahn-Hilliard, and heat-transfer equations. A computationally efficient large-scale heat-transfer model is used to describe the temperature evolution in larger volumes. Phase field modeling is used to demonstrate how epitaxial growth of Ti-6-4 can be interrupted with inoculants to obtain an equiaxed polycrystalline structure. These structures are enriched with a synthetic lath martensite substructure, and their micromechanical response are investigated with a crystal plasticity model. The fatigue performance of these structures are analyzed, with spherical porelike defects and high-aspect-ratio cracklike defects incorporated, and a cycle-amplitude fatigue graph is produced to quantify the fatigue behavior of the structures. The simulated fatigue life presents trends consistent with the literature in terms of high cycle and low cycle fatigue, and the role of defects in dominating the respective performance of the produced SLM structures. The proposed ICME workflow emphasizes the possibilities arising from the vast design space exploitable with respect to manufacturing systems, powders, respective alloy chemistries, and microstructures. By digitalizing the whole workflow and enabling a thorough and detailed virtual evaluation of the causal relationships, the promise of product-targeted materials and solutions for metal additive manufacturing becomes closer to practical engineering application.to be modified to maintain similar properties achieved with traditional techniques such as casting or wrought parts. Compared to traditional manufacturing methods, due to local powder fusion in rapid solidification conditions and solid-state heat cycling, the resulting microstructures present unique features such as metastable phases, smaller scale microsegregation, formation of unexpected secondary phases, spherical or high-aspect-ratio pores, oxide inclusions, microstructural residual stresses, melt pool boundaries, grains with complex morphology, and strong texture.The aforementioned structural features in SLM lead to properties and performance that are unexpected [1]. For example, stainless steel 316L can achieve remarkably high hardness and ductility [2] but has a susceptibility to pitting corrosion [3]; Inconel 625 nickel superalloy contains detrimental intermetallic and carbide phases [4]; Ti-6-4 tends to produce hard needlelike α martensite which leads to high hardness but low ductility; tool steels are extremely difficult to fabricate with SLM due to their complex metallurgy, including a tendency to form brittle martensite and high volume fractions of ...

show abstract

“…This is due to its fundamental origins, connections with non-equilibrium thermodynamics, and numerical efficiency compared to interface tracking approaches. Phase field modeling has been used in the study of solidification in a range of materials, from ideal dilute binary alloys [2,3] to more complex binary alloys [4] and multi-component or multi-phase al-Preprint submitted to Elsevier September 17, 2018 arXiv:1809.05148v1 [cond-mat.mtrl-sci] 13 Sep 2018 loys [5,6,7].…”

Section: Introductionmentioning

confidence: 99%

“…In the limit of low undercooling (or low supersaturation), a robust set of results derived from a matched asymptotic boundary layer analysis of this model, for an ideal binary alloy [3], can be used to map the model's behaviour quantitatively onto the classical sharp interface model; these results can also be essentially used to recover the classical sharp interface limit of most of the above-cited phase field models [4,6,7].…”

Section: Introductionmentioning

confidence: 99%

“…with finite volume method. The mesh was adaptively refined to capture gradients in phase field and concentration fields appropriately with the software platform introduced in[7], with the smallest allowed grid spacing set to 60% of the interface width, dx = 0.6W 0 . The 1D runs assumed a constant dimensionlessundercooling ∆ = (T l − T )( (1 − k e )|me l |c o l ).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Quantitative phase field modeling of solute trapping and continuous growth kinetics in quasi-rapid solidification

Pinomaa

Provatas

2019

Acta Materialia

Self Cite

View full text Add to dashboard Cite

Quantitative 3D phase field modelling of solidification using next-generation adaptive mesh refinement

Cited by 60 publications

References 39 publications

PRISMS-PF: A general framework for phase-field modeling with a matrix-free finite element method

PRISMS-PF: A general framework for phase-field modeling with a matrix-free finite element method

Process-Structure-Properties-Performance Modeling for Selective Laser Melting

Quantitative phase field modeling of solute trapping and continuous growth kinetics in quasi-rapid solidification

Contact Info

Product

Resources

About