Linear structural dynamic models are often used to support system design and qualification. Overall, linear models provide an efficient means for conducting design studies and augmenting test data by recovering un-instrumented or unmeasurable quantities (e.g. stress). Nevertheless, the use of linear models often adds significant conservatism in design and qualification programs by failing to capture critical mechanisms for energy dissipation. Unfortunately, the use of explicit nonlinear models can require unacceptably large efforts in model development and experimental characterization to account for common nonlinearities such as frictional interfaces, macro-slip, and other complex material behavior. The computational requirements are also greater by orders of magnitude. Conversely, modal models are much more computationally efficient and experimentally have shown the ability to capture typical structural nonlinearity. Thus, this work will seek to use modal nonlinear identification techniques to improve the predictive capability of a finite element structural dynamics model. Part I of this paper discussed experimental aspects of this work. Part II will consider use of nonlinear modal models in finite element modeling. First, the basic theory and numerical implementation is discussed. Next, the linear structural dynamic model of a configuration of interest is presented and model updating procedures are discussed. Finally, verification exercises are presented for a high level excitation using test data and simulated predictions from a structural dynamics model augmented with models obtained in nonlinear identification efforts.
Simulation of the response of a system to an acoustic environment is desirable in the assessment of aerospace structures in flight-like environments. In simulating a laboratory acoustic test a large challenge is modeling the as-tested acoustic field. Acoustic source inversion capabilities in Sandia’s Sierra/SD structural dynamics code have allowed for the determination of an acoustic field based on measured microphone responses—given measured pressures, source inversion optimization algorithms determine the input parameters of a set of acoustic sources defined in an acoustic finite element model. Inherently, the resulting acoustic field is dependent on the target microphone data. If there are insufficient target points, then the as-tested field may not be recreated properly. Here, the question of number of microphones is studied using synthetic data, that is, target data taken from a previous simulation which allows for comparison of the full pressure field—an important benefit not available with test data. By exploring a range of target points distributed throughout the domain, a rate of convergence to the true field can be observed. Results will be compared with the goal of developing guidelines for the number of sensors required to aid in the design of future laboratory acoustic tests to be used for model assessment.
