Journal: Theoretical and Applied Mechanics Letters

Abbreviation

Publisher

Elsevier

Journal Volumes

ISSN

2589-0336
2095-0349

Description

Filters

2023 - 20242
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Publications 1 - 2 of 2
  • Spatially-graded 3D-printed viscoelastic truss metamaterials for impact trajectory control and energy absorption
    Item type: Journal Article
    Radi, Kaoutar; Glaesener, Raphaël N.; Kumar, Siddhant; et al. (2024)
    Theoretical and Applied Mechanics Letters
    This study demonstrates that two- and three-dimensional spatially graded, truss-based polymeric-material metamaterials can be designed for beneficial impact mitigation and energy absorption capabilities. Through a combination of numerical and experimental techniques, we highlight the broad property space of periodic viscoelastic trusses, realized using 3D printing via selective laser sintering. Extending beyond periodic designs, we investigate the impact response of spatially variant viscoelastic lattices in both two and three dimensions. Our result reveal that introducing spatial variations in lattice topology allows for redirecting of the impact trajectory, opening new opportunities for engineering and tailoring lightweight materials with target impact functionality. This is achieved through the combined selection of base material and metamaterial design.
  • Feature identification in complex fluid flows by convolutional neural networks
    Item type: Journal Article
    Wen, Shizheng; Lee, Michael W.; Kruger Bastos, Kai M.; et al. (2023)
    Theoretical and Applied Mechanics Letters
    Recent advancements have established machine learning's utility in predicting nonlinear fluid dynamics, with predictive accuracy being a central motivation for employing neural networks. However, the pattern recognition central to the networks function is equally valuable for enhancing our dynamical insight into the complex fluid dynamics. In this paper, a single-layer convolutional neural network (CNN) was trained to recognize three qualitatively different subsonic buffet flows (periodic, quasi-periodic and chaotic) over a high-incidence airfoil, and a near-perfect accuracy was obtained with only a small training dataset. The convolutional kernels and corresponding feature maps, developed by the model with no temporal information provided, identified large-scale coherent structures in agreement with those known to be associated with buffet flows. Sensitivity to hyperparameters including network architecture and convolutional kernel size was also explored. The coherent structures identified by these models enhance our dynamical understanding of subsonic buffet over high-incidence airfoils over a wide range of Reynolds numbers.
Publications 1 - 2 of 2