Abstract

Joints are difficult to model because of various small-scale and nonlinear features, such as local geometry, preload, friction and contact. These features cause a local change in stiffness and energy dissipation which result in changes in the effective natural frequency and damping of the whole structure. Past studies have found good success using nonlinear Iwan elements as whole joint models for bolted joints in a few different structures. In Chapter 3, a similar approach is proposed for modeling riveted joints, which seeks to capture the vibrational response of a test structure including changes in the effective modal frequency and damping with vibration amplitude. A four parameter, nonlinear Iwan element is used to model each rivet joint in the test structure. The rest of the structure is modeled using a linear finite element model (FEM), which is reduced using interface spiders and the Hurty/Craig-Bampton method to speed up simulations. The Quasi-Static Modal Analysis (QSMA) approach is used to find the effective mode frequencies, shapes, and damping as a function of vibration amplitude. These results are then compared to experimental measurements of the impact excited response on a test structure to validate the model accuracy. The focus of this research was that included in Chapter 3. Chapters 2 and 4 include other work that contributed to the understanding of the broader field of joint nonlinearity and so they are included as well. In Chapter 2, modal hammer impact testing was performed on the Round Robin Frame and Wing Structure. Various amplitude impacts were used to excite any nonlinearities in the structure. Experimental modal analysis was performed to separate the modal responses of the system. Mode shapes and frequencies are examined to understand any nonlinearities in the system. In Chapter 4, new methods are investigated for the estimation of nonlinear characteristics when large amounts of data are available. The methods include direct nonlinear optimization-based identification techniques like the more commonly used sparse identification package, SINDy and a more customizable sequential learning algorithm. Besides, parameter initialization (such that any optimized models are able to avoid bad local minima) is studied to accomplish a successful identification. Multiple data sets from an experimental setup of nonlinear structures with the expected source of nonlinearity, i.e., the bolted joints are used in this study to evaluate the performance of the identification methods. For assessing the quality of the resulting models, the responses from simulations are compared to the measured responses of the structures such as amplitude-dependent frequencies and damping ratios.

Degree

College and Department

Ira A. Fulton College of Engineering; Mechanical Engineering

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