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Non-colloidal suspensions undergoing dipolar interactions in an electric field have been extensively studied and are also known as smart materials as they share similarities with electrorheological (ER) fluids. Although the macroscopic responses are well-documented, the multiscale nature of such suspensions is still lacking. In this study, a large-scale Stokesian dynamics simulation is used to investigate the structural formation of such suspensions in an electric field up to highly concentrated regimes across different length scales: from particle-level (microscale) to particle cluster-level (mesoscale) and stress response-level (macroscale). It is observed that at a volume fraction of ϕ ≈ 30%, the steady-state structures are the most isotropic at the microscale, but at the macroscale, their normal stress fields are the most anisotropic. Interestingly, these structures are also the most heterogeneous at both the microscale and mesoscale. Furthermore, the effects of confinement on the multiscale responses are explored, revealing that there could be a strong link between the mesoscale and macroscale. This multiscale nature can offer the potential for precisely controlling or designing ER fluids in practical applications.
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Multiscale Modeling of Metal-Ceramic Spatially Tailored Materials via Gaussian Process Regression and Peridynamics
In this paper, a micro-to-macro multiscale approach with peridynamics is proposed to study metal-ceramic composites. Since the volume fraction varies in the spatial domain, these composites are called spatially tailored materials (STMs). Microstructure uncertainties, including porosity, are considered at the microscale when conducting peridynamic modeling and simulation. The collected dataset is used to train probabilistic machine learning models via Gaussian process regression, which can stochastically predict material properties. The machine learning models play a role in passing the information from the microscale to the macroscale. Then, at the macroscale, peridynamics is employed to study the mechanics of STM structures with various volume fraction distributions.
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- Award ID(s):
- 2104383
- PAR ID:
- 10425235
- Date Published:
- Journal Name:
- International Journal of Computational Methods
- Volume:
- 19
- Issue:
- 10
- ISSN:
- 0219-8762
- 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
- Journal Article:
- https://doi.org/10.1142/S0219876222500256
