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Data Science in Engineering, Volume 9 pp 65–69Cite as

Estimation of Elastic Band Gaps Using Data-Driven Modeling

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Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

The paper discusses estimation of elastic band gaps of a one-dimensional periodic structure using the frequency response functions (FRFs) of a unit cell. A unit cell considered in this paper consists of two masses with a spring between them. Such unit cells are connected with a coupled spring to design a periodic lattice structure. The paper establishes the FRF for a different number of unit cells using FRF-based sub-structuring (FBS). The wave-equation method is then used to estimate the dispersion curves and eventually band gaps. The paper follows a data-driven modeling approach to develop state-space models for estimating dispersion curves from FRFs. The vector fitting algorithm creates a data-driven model of the unit cell from noisy FRFs. A multi-unit cell lattice is simulated from data-driven models using FBS. Additionally, the paper investigates tuning of elastic band gaps by changing the mass and the stiffness of the unit cells.

Keywords

  • Band gaps
  • Vector fitting
  • Sub-structuring
  • Wave propagation
  • Periodic structure

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References

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Author information

Authors and Affiliations

  1. Vibrations, Intelligent Testing & Active Learning of Structures (VITALS), Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, Houghton, MI, USA

    H. Gosavi & Vijaya V. N. Sriram Malladi

Authors
  1. H. Gosavi
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  2. Vijaya V. N. Sriram Malladi
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Corresponding author

Correspondence to H. Gosavi .

Editor information

Editors and Affiliations

  1. University of California, San Diego, San Diego, CA, USA

    Ramin Madarshahian

  2. Department of Energy-Defense Programs, Lawrence Livermore National Laboratory, Livermore, CA, USA

    Dr. Francois Hemez

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Gosavi, H., Sriram Malladi, V.V.N. (2022). Estimation of Elastic Band Gaps Using Data-Driven Modeling. In: Madarshahian, R., Hemez, F. (eds) Data Science in Engineering, Volume 9. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-030-76004-5_8

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  • DOI: https://doi.org/10.1007/978-3-030-76004-5_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-76003-8

  • Online ISBN: 978-3-030-76004-5

  • eBook Packages: EngineeringEngineering (R0)

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