Dataset: PRISMS-Plasticity: An open-source crystal plasticity finite element software




Files
41
31.21 MB
Samples
30
in dataset
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0
documented
Views
1,806
total views
Downloads
512
total downloads
Published
Apr 19, 2021
5 years ago

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41 files across 1 type(s)

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Published
Published: 5 years ago
Views
1,806
Downloads
512
Total Size
31.21 MB
Description

BCC_Polycrystal_Tension: In this example, the capability of the PRISMS-Plasticity CPFE software to model BCC polycrystalline samples is demonstrated. To do so, the response of a polycrystalline sample of the β titanium alloy Timetal 21S during uniaxial tension is modeled. The results can be compared to simulation results reported by Qidwai et al. (2009).

FCC_Polycrystal_NeperConformingMesh: Here, the PRISMS-Plasticity CPFE software is used to replicate the results presented by Anand and Kothari (1996) for polycrystalline OFHC copper with initial random orientations during a compression experiment. A polycrystalline copper sample consists of 200 randomly oriented grains is generated using Neper. The conforming FE discretization was generated from the Neper output by converting the tetrahedral elements to hexahedral elements using the tet-to-hex converter which is included as a utility software within the PRISMS-Plasticity CPFE code.

FCC_Polycrystal_NeperNonConformingMesh: Here, the PRISMS-Plasticity CPFE software is used to replicate the results presented by Anand and Kothari (1996) for polycrystalline OFHC copper with initial random orientations during a compression experiment. A polycrystalline copper sample consists of 200 randomly oriented grains is generated using Neper. The nonconforming FE discretization with a regular 32×32×32 mesh was generated to model the polycrystalline sample.

FCC_Polycrystal_RandomOrientationBlock: Here, the PRISMS-Plasticity CPFE software is used to replicate the results presented by Anand and Kothari (1996) for polycrystalline OFHC copper with initial random orientations during a compression experiment. the isotropic polycrystalline sample is modeled as an aggregate of 400 single crystals with random orientations, with each grain being modeled by a single eight-node linear hexahedral element. Accordingly, a 5×8×10 FE cubic mesh is generated in the x, y, and z directions, which each element represents a single grain.

HCP_Polycrystal_Compression: The twinning model used in PRISMS-Plasticity CPFE, simulation results are compared against the experimental results during uniaxial compression test reported by Wu (2009) for extruded Mg alloy ZK60A sample at room temperature. The polycrystalline sample is modeled as an aggregate of 1080 single crystals, which reproduces the extruded sample, each grain being modeled by a single eight-node linear hexahedral element. Accordingly, an 8×9×15 FE cubic mesh is generated in the x, y, and z directions, in which each element represents a single grain.

HCP_Polycrystal_Tension: The twinning model used in PRISMS-Plasticity CPFE, simulation results are compared against the experimental results during uniaxial tension test reported by Wu (2009) for extruded Mg alloy ZK60A sample at room temperature. The polycrystalline sample is modeled as an aggregate of 1080 single crystals, which reproduces the extruded sample, each grain being modeled by a single eight-node linear hexahedral element. Accordingly, an 8×9×15 FE cubic mesh is generated in the x, y, and z directions, in which each element represents a single grain. This work was supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering under Award#DE-SC0008637 as part of the Center for Predictive Integrated Structural Materials Science (PRISMS Center) at University of Michigan. We also acknowledge the financial cost-share support of University of Michigan College of Engineering and Office of the Vice President for Research. PRISMS Center, Materials Science and Engineering, University of Michigan, , Ann Arbor, MI 48109, USA.

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BoundaryConditions Input 6 GrainId Input 6 Orientations Input 6 Run Simulation 30 Set Numerical Parameters 6
Citations
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Paper
Mohammadreza Yaghoobi, Sriram Ganesan, Srihari Sundar, Aaditya Lakshmanan, Shiva Rudraraju, John E. Allison, Veera Sundararaghavan
Computational Materials Science 2019 10.1016/j.commatsci.2019.109078
146
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