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Description
Dynamic Substructuring provides a framework for analyzing the dynamics of complex, large-scale systems at the subcomponent level, often integrating model reduction techniques in numerical workflows. For machines and components with geometric and dynamic complexities, incorporating experimental data for component characterization is crucial. A frequency-based approach is commonly adopted for efficiently coupling experimentally tested components. While Substructuring focuses on assembling passive components, Transfer Path Analysis (TPA) addresses the characterization of active components and the propagation of vibrations to the passive system.
The chain of operations for experimental substructuring and TPA in the frequency domain begins with well-designed measurements that generate passive frequency response functions (FRFs) representing subcomponent dynamics and active operational measurements to characterize unknown sources. Interface compatibility is often weakened, reducing the problem to a few generalized degrees of freedom while retaining key dynamic features. The substructuring assembly (e.g. using Lagrange multipliers) and source characterization (e.g. through in-situ blocked forces) both involve solving inverse problems.
While these operations may seem straightforward, several sources of error can undermine the methodology, leading to unreliable predictions. Proper modeling of interfaces and degrees of freedom is crucial, and filtering and regularization techniques can help mitigate vibration noise and distortions. This paper evaluates the effectiveness of filtering (e.g., modal filtering, PRANK) and regularization (e.g., truncated SVD, Tikhonov regularization, added compliance) techniques within the framework of Frequency-Based Substructuring and TPA. The physical consistency, usability, and effectiveness of these methods are discussed and benchmarked using both numerical and experimental examples.
