## 2 Method  
To effectively model reconfigurable dynamic systems, a structured, step-by-step approach is essential. Firstly, appropriate measurement points and DoFs are selected, with emphasis on both interface and interior points of the substructures to capture necessary data. The system is then partitioned into manageable substructures based on available subsystem data and design requirements, facilitating a more feasible analysis of the system's dynamic behavior. Boundary conditions are established at the interfaces between subsystems, ensuring compatibility and equilibrium while considering factors like single or multiple connection points and interface flexibility. Next, each disassembled subsystem undergoes modal analysis to determine its FRF matrix at the free-free condition, which can be derived through finite element analysis (FEA), analytical methods, or experimental techniques. After obtaining individual FRFs, a suitable coupling approach is selected to integrate these subsystem models into a system model; options include impedance coupling, receptance coupling, [PERSON]'s approach, and LM-FBS coupling, depending on system complexity and required accuracy. Finally, the receptance matrices of individual substructures are synthesized using the chosen coupling method to create a cohesive model representing the dynamic behavior of the entire system. This structured approach, summarized in Figure 1, provides a roadmap for modeling reconfigurable dynamic systems using a generalized coupling method with Frequency-Based Substructuring (FBS).  
The proposed methodology offers several key advantages, particularly for reconfigurable dynamic systems with modular-based configurations. It simplifies the analysis of complex systems through reduced-order models by combining receptance points that represent measurement and excitation points, such as those in suspension systems, without requiring geometrical complexity. This improves accessibility while maintaining accuracy. By integrating experimental and analytical data, it enhances prediction precision and reduces errors, particularly at subsystem interfaces. The method also facilitates early-stage NVH modeling, delivering critical insights--especially in vehicle electrification, where tire-road noise has become a primary source of cabin noise. Additionally, it provides explicit tools for cost-effective subsystem coupling and decomposition, enabling accurate noise prediction and system performance optimization.  
The methodology is highly adaptable, supporting straightforward updates to individual components without requiring a complete system redesign, making it ideal for iterative design processes. Its focus on modular-based configurations allows for the efficient analysis and refinement of individual subsystems, making it particularly well-suited for reconfigurable systems. It also has potential to simplify the analysis of systems with local non-linear components by treating non-linear behavior within subsystems while maintaining linear assumptions at connection points through compatibility and equilibrium. Non-linear dynamics can be incorporated as additional dynamic stiffness or experimental receptance measurements modeled as black-box subsystems. This modular approach enables the targeted refinement of non-linear substructures without necessitating the reanalysis of the entire system, offering significant computational efficiency and flexibility in design and analysis.