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Metal alloys frequently contain distributions of second-phase particles that deleteriously affect the material behavior by acting as sites for void nucleation. These distributions are often extremely complex and processing can induce high levels of anisotropy. The particle length-scale precludes high-fidelity microstructure modeling in macroscale simulations, so computational homogenization methods are often employed. These, however, involve simplifying assumptions to make the problem tractable and many rely on periodic microstructures. Here we propose a methodology to bridge the gap between realistic microstructures composed of anisotropic, spatially varying second-phase void morphologies and idealized periodic microstructures with roughly equivalent mechanical responses. We create a high-throughput, parametric study to investigate 96 unique bridging methods. We apply our proposed solution to a rolled AZ31B magnesium alloy, for which we have a rich dataset of microstructure morphology and mechanical behavior. Our methodology converts aµ-CT scan of the realistic microstructure to idealized periodic unit cell microstructures that are specific to the loading orientation. We recreate the unit cells for each parameter set in a commercial finite element software, subject them to macroscopic uniaxial loading conditions, and compare our results to the datasets for the various loading orientations. We find that certain combinations of our parameters capture the overall stress–strain response, including anisotropy effects, with some degree of success. The effect of different parameter options are explored in detail and we find that excluding certain particle populations from the analysis can give improved results.
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Multiscale Modeling of Composite Materials under Volumetric and Interfacial Damage: Achieving Adaptive Model Order Reduction
In this manuscript, we present a multiscale Adaptive Reduced-Order Modeling (AROM) framework to efficiently simulate the response of heterogeneous composite microstructures under interfacial and volumetric damage. This framework builds on the eigendeformation-based reduced-order homogenization model (EHM), which is based on the transformation field analysis (TFA) and operates in the context of computational homogenization with a focus on model order reduction of the microscale problem. EHM pre-computes certain microstructure information by solving a series of linear elastic problems defined over the fully resolved microstructure (i.e., concentration tensors, interaction tensors) and approximates the microscale problem using a much smaller basis spanned over subdomains (also called parts) of the microstructure. Using this reduced basis, and prescribed spatial variation of inelastic response fields over the parts, the microscale problem leads to a set of algebraic equations with part-wise responses as unknowns, instead of node-wise displacements as in finite element analysis. The volumetric and interfacial influence functions are calculated by using the Interface enriched Generalized Finite Element Method (IGFEM) to compute the coefficient tensors, in which the finite element discretization does not need to conform to the material interfaces. AROM takes advantage of pre-computed coefficient tensors associated with the finest ROM and efficiently computes the coefficient tensors of a series of gradually coarsening ROMs. During the multiscale analysis stage, the simulation starts with a coarse ROM which can capture the initial elastic response well. As the loading continues and response in certain parts of the microstructure starts to localize, the analysis adaptively switches to the next level of refined ROM to better capture those local responses. The performance of AROM is evaluated by comparing the results with regular EHM (no adaptive refinement) and IGFEM under different loading conditions and failure modes for various 2D and 3D microstructures. The proposed AROM provides an efficient way to model history-dependent nonlinear responses for composite materials under localized interface failure and phase damage.
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- Award ID(s):
- 2114822
- PAR ID:
- 10396899
- Date Published:
- Journal Name:
- AIAA SCITECH 2023 Forum
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.2514/6.2023-0138
