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Article Dans Une Revue Mechanical Systems and Signal Processing Année : 2022

A data-driven metamodel-based approach for point force localization

Résumé

This paper introduces a novel strategy for point force localization in the frequency domain, based on metamodeling techniques and independent of the excitation level. More precisely, the ability of well-established techniques, such as Polynomial Chaos expansion or Universal Kriging, in providing accurate surrogate models for locating a point force through an optimization procedure is evaluated. The proposed methodology is applied in a purely data-driven context. Obtained results highlight the good performance of the proposed strategy for relatively small data sets, as well as its robustness to noise in both training and deployment phases.
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Dates et versions

hal-03557526 , version 1 (04-02-2022)

Identifiants

Citer

Mathieu Aucejo. A data-driven metamodel-based approach for point force localization. Mechanical Systems and Signal Processing, 2022, 171, pp.108881. ⟨10.1016/j.ymssp.2022.108881⟩. ⟨hal-03557526⟩
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