A data-driven metamodel-based approach for point force localization. (15th May 2022) - Aucejo, M.
Abstract: 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. Highlights: A data-driven metamodel-based strategy is developed for point force localization. The proposed method is applied both numerically and experimentally. Polynomial Chaos and Universal Kriging metamodels are compared. Obtained results point out the robustness of the method measurement noise. Universal Kriging generally leads to more accurate localizations.