System equivalent model mixing: A modal domain formulation. (1st September 2022)

• Record Type: Journal Article
• Authors: Pogačar, Miha; Ocepek, Domen; Trainotti, Francesco; Čepon, Gregor; Boltežar, Miha
• Is Part Of: Mechanical systems and signal processing. Volume 177(2022)
• Publication Date: 2022-09-01
• Subjects: SEMM -- SEREP -- Model expansion -- Hybrid modeling -- Dynamic substructuring; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.109239
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Hybrid modeling is an increasingly common procedure for predicting the structural dynamics of complex products. System equivalent model mixing (SEMM), a dynamic substructuring-based method, is a recent addition to the field. Its implementation within frequency-based substructuring has great potential. Therefore, it is reasonable to explore the options for implementing an equivalent substructuring framework in any of the other substructuring domains. The objective of this paper is to present M-SEMM, system equivalent model mixing in the modal domain. A theoretical derivation reveals that under certain constrained conditions, the proposed methodology is equivalent to the system equivalent reduction expansion process (SEREP), a well-established reduction/expansion technique. When considering the expansion of spatially sparse models with a high modal density, M-SEMM represents a novel expansion method, which can be seen as a potentially useful extension to SEREP. The proposed implementation offers certain advantages, the most notable being a superior ability to disregard spurious modes in the hybrid model. A study of the proposed methodology on a numerical and an experimental case demonstrates its applicability and provides a comparison with SEREP and the original implementation of SEMM in the frequency domain. Graphical abstract: Highlights: System equivalent model mixing in the modal domain (M-SEMM) is introduced. Under certain constraint conditions, M-SEMM is analytically equivalent to SEREP. For spatially sparse models, M-SEMM is a novel expansion method in the modal domain. The advantage of modal SEMM formulation is the ability to filter out spurious peaks.