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Modeling Bioinspired Fish Scale Designs via a Geometric and Numerical Approach.
Chen, Ailin; Thind, Komal; Demir, Kahraman G; Gu, Grace X.
Afiliação
  • Chen A; Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA.
  • Thind K; Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA.
  • Demir KG; Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA.
  • Gu GX; Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA.
Materials (Basel) ; 14(18)2021 Sep 17.
Article em En | MEDLINE | ID: mdl-34576605
ABSTRACT
Fish scales serve as a natural dermal armor with remarkable flexibility and puncture resistance. Through studying fish scales, researchers can replicate these properties and tune them by adjusting their design parameters to create biomimetic scales. Overlapping scales, as seen in elasmoid scales, can lead to complex interactions between each scale. These interactions are able to maintain the stiffness of the fish's structure with improved flexibility. Hence, it is important to understand these interactions in order to design biomimetic fish scales. Modeling the flexibility of fish scales, when subject to shear loading across a substrate, requires accounting for nonlinear relations. Current studies focus on characterizing these kinematic linear and nonlinear regions but fall short in modeling the kinematic phase shift. Here, we propose an approach that will predict when the linear-to-nonlinear transition will occur, allowing for more control of the overall behavior of the fish scale structure. Using a geometric analysis of the interacting scales, we can model the flexibility at the transition point where the scales start to engage in a nonlinear manner. The validity of these geometric predictions is investigated through finite element analysis. This investigation will allow for efficient optimization of scale-like designs and can be applied to various applications.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article