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  5. Dynamic Behavior of Origami Structures: Computational and Experimental Study

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Preprint
en
2024

Dynamic Behavior of Origami Structures: Computational and Experimental Study

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0 Files

en
2024
DOI: 10.48550/arxiv.2408.01889arxiv.org/abs/2408.01889

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Satish Nagarajaiah
Satish Nagarajaiah

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Sudheendra Herkal
Satish Nagarajaiah
Glaucio Paulino

Abstract

Origami structures have been receiving a lot of attention from engineering and scientific researchers owing to their unique properties such as deployability, multi-stability, negative stiffness, etc. However, dynamic properties of origami structures have not been explored much due to a lack of validated analytical dynamic modeling approaches. Given the range of interesting properties and applications of origami structures, it is important to study the dynamic behavior of origami structures. In this study, a dynamic modeling approach for origami structures is presented considering distributed mass modeling, which has the potential to be a generalizable approach. In the proposed approach, stiffness is modeled using the bar and hinge modeling approach while the mass is modeled using the mass distribution approach. Various candidate mass distribution approaches were investigated by comparing their responses to the finite element method responses for various geometric conditions, loading and boundary conditions, and deformation modes. It was observed that a dynamic modeling approach with triangle circumcenter mass distribution was able to capture most of the dynamics satisfactorily consistently. Subsequently, a Miura-ori specimen was manufactured and its free vibration response was determined experimentally and then compared to the prediction of the analytical model. The comparison demonstrated that the analytical model was able to capture most of the dynamics in the longitudinal direction.

How to cite this publication

Sudheendra Herkal, Satish Nagarajaiah, Glaucio Paulino (2024). Dynamic Behavior of Origami Structures: Computational and Experimental Study. , DOI: https://doi.org/10.48550/arxiv.2408.01889.

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Publication Details

Type

Preprint

Year

2024

Authors

3

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.48550/arxiv.2408.01889

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