Estimating experimental dispersion curves from steady-state frequency response measurements

Malladi, V. V. N. Sriram; Albakri, Mohammad I.; Krishnan, Manu; Gugercin, Serkan; Tarazaga, Pablo A.

doi:10.1016/j.ymssp.2021.108218

Cited by 9 publications

(2 citation statements)

References 28 publications

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“…Some of the projection-based techniques mentioned above (and employed in this paper) have been extended to the structured models we consider here; see, e.g., Ref. [14][15][16][17][18][19] and the references therein for some of the data-driven approaches to structured dynamics. However, this is not our focus in this paper and these considerations are left to future work.…”

Section: Introductionmentioning

confidence: 99%

Application of Interpolatory Methods of Model Reduction to an Elevated Railway Pier

Bertero¹,

Gugercin²,

Sarlo³

2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

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Be it due to time constraints or insufficient processing power -or a combination of both -the use of models with large numbers of degrees of freedom (DoF) may be unsuitable to provide a client with results in a timely manner. The use of physics-based reduced models -or proxy structures -are popular among practitioners to solve this issue, as they keep intact all the underlying properties of the second order problems at a fraction of the cost. In this paper, interpolatory methods of model reduction are explored as an alternative, and applied to a 3D Space Frame. The methods chosen allow for structure-preserving reduced models and differ mainly on the selection of interpolation points. A comparison between the response of these reduced models and a proxy structure against two different types of inputs show that interpolatory methods are a viable, more flexible option when it comes to reducing the internal DoF's of a structural model, though engineering judgement helps to ensure it adequately captures the most relevant aspects of the response for the specific application.

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Section: Introductionmentioning

confidence: 99%

Application of Interpolatory Methods of Model Reduction to an Elevated Railway Pier

Bertero¹,

Gugercin²,

Sarlo³

2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

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“…Structural dynamics is another example. Even when a mathematical model of a highly complex physical structure is not available, the structural (displacement and velocity) time and frequency domain responses can be measured accurately at specific locations on the structures, thanks to the advances in testing capabilities and the near ubiquitous deployment of high bandwidth sensing [ 30 ]. We refer the reader to [ 3 , 5 , 8 , 12 , 16 , 26 , 36 ] and the references therein for more details on data-driven modeling.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Modeling of Linear Dynamical Systems with Quadratic Output in the AAA Framework

Gosea

Gugercin

2022

J Sci Comput

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We extend the Adaptive Antoulas-Anderson () algorithm to develop a data-driven modeling framework for linear systems with quadratic output (). Such systems are characterized by two transfer functions: one corresponding to the linear part of the output and another one to the quadratic part. We first establish the joint barycentric representations and the interpolation theory for the two transfer functions of systems. This analysis leads to the proposed algorithm. We show that by interpolating the transfer function values on a subset of samples together with imposing a least-squares minimization on the rest, we construct reliable data-driven models. Two numerical test cases illustrate the efficiency of the proposed method.

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Circuits and Algorithms for Physical Modeling

Uncini

2022

Springer Topics in Signal Processing

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Estimating experimental dispersion curves from steady-state frequency response measurements

Cited by 9 publications

References 28 publications

Application of Interpolatory Methods of Model Reduction to an Elevated Railway Pier

Application of Interpolatory Methods of Model Reduction to an Elevated Railway Pier

Data-Driven Modeling of Linear Dynamical Systems with Quadratic Output in the AAA Framework

Circuits and Algorithms for Physical Modeling

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