Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

High fidelity modeling of complex systems can require large finite element models to capture the physics of interest. Typically these high-order models take an excessively long time to run. For important studies such as model validation and uncertainty quantification, where probabilistic measures of the response are required, a large number of simulations of the high fidelity model with different parameters are necessary. In addition, some environments, such as an extensive random vibration excitation, require a long simulation time to capture the entire event. A process that produces a highly efficient model from the original high order model is necessary to enable these analyses. These highly efficient models are referred to as surrogate models, for their purpose is to represent the main physics that is of importance, but decrease the computational burden. A critical aspect of any surrogate model is how faithfully the efficient model represents the original high-order model. This paper describes the process for verifying a surrogate model using response quantities of interest and quantifying the introduced uncertainties in the use of the surrogate model. A sequel paper to be submitted continues this work by validating the surrogate model and quantifying margins of uncertainty. © 2012 by the American Institute of Aeronautics and Astronautics, Inc.

Abstract not provided.

Abstract not provided.

When estimating parameters for a material model from experimental data collected during a separate effects physics experiment, the quality of fit is only a part of the required data. Also necessary is the uncertainty in the estimated parameters so that uncertainty quantification and model validation can be performed at the full system level. The uncertainty and quality of fit of the data are many times not available and should be considered when fitting the data to a specified model. There are many techniques available to fit data to a material model and a few of them are presented in this work using a simple acoustical emission dataset. The estimated parameters and the affiliated uncertainty will be estimated using a variety of techniques and compared.

When estimating parameters for a material model from experimental data collected during a separate effects physics experiment, the quality of fit is only a part of the required data. Also necessary is the uncertainty in the estimated parameters so that uncertainty quantification and model validatino can be performed at the full system level. The uncertainty and quality of fit of the data are many times not available and should be considered when fitting the data to a specified model. There are many techniques available to fit data to a material model and a few of them are presented in this work using a simple acoustical emission dataset. The estimated parameters and the affiliated uncertainty will be estimated using a variety of techniques and compared.

Abstract not provided.

Estimation of the uncertainty in experimental modal parameters is valuable when validation of a finite element model is performed based on modal frequencies and shapes. The uncertainty in the data is needed to establish distribution functions for the comparison to computational models. A part that is typically neglected with the experimental data is to quantify the uncertainty in the estimated parameters established from the data. This uncertainty is an important piece of the puzzle in a validation exercise. If the uncertainty in the fit is not accounted for, the uncertainty estimate of the experimental data is incomplete. This paper will explore the uncertainty of the modal parameter estimates measured from the Synthesize Modes and Correlate (SMAC) algorithm using a Monte Carlo technique. The uncertainty in the parameter fit will be determined for both analytically and experimentally determined frequency response functions. It is found that the largest uncertainty in the SMAC algorithm is within the optimization step of the fitting process. © 2009 Society for Experimental Mechanics Inc.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

This test report covers the SNL modal test results for two nominally identical TX-100 wind turbine blades. The TX-100 blade design is unique in that it features a passive braking, force-shedding mechanism where bending and torsion are coupled to produce desirable aerodynamic characteristics. A specific aim of this test is to characterize the coupling between bending and torsional dynamics. The results of the modal tests and the subsequent analysis characterize the natural frequencies, damping, and mode shapes of the individual blades. The results of this report are expected to be used for model validation--the frequencies and mode shapes from the experimental analysis can be compared with those of a finite-element analysis. Damping values are included in the results of these tests to potentially improve the fidelity of numerical simulations, although numerical finite element models typically have no means of predicting structural damping characteristics. Thereafter, an additional objective of the test is achieved in evaluating the test to test and unit variation in the modal parameters of the two blades.

Abstract not provided.

Abstract not provided.

Abstract not provided.

The focus of this work will be to simulate a harsh, blast environment on a space structure. Data from a reverse Hopkinson bar (RHB) test is used to generate the response to a symmetric, distributed load. The RHB generates a high-amplitude, high-frequency content, concentrated pulse that excites components at near-blast levels. The transfer functions generated at discrete points, with the RHB, are used to generate an experimental model of the structure, which is then used in conjunction with the known pressure distribution, to estimate the component response to a blast. The shock spectrum of the predicted response and the actual response compared well in two of the three cases presented.

In this paper, a support and preload system is presented in which the frequencies and damping of the test article are affected by the stiffness and damping of the supporting structure. A dynamic model is derived for the support system that includes the damping as well as the mass and stiffness of the supports. The frequencies, damping, and mode shapes are compared with the experimentally determined parameters. It is shown that for a seemingly simple support system, deriving a predictive model is not a trivial task.

The predictive modeling of vibration of many structural systems is crippled by an inability to predictively model the mechanics of joints. The lack of understanding of joint dynamics is evidenced by the substantial uncertainty of joint compliances in the numerical models and by the complete inability to predict joint damping. The lore is that at low amplitudes, joint mechanics are associated with Coulomb friction and stick-slip phenomena and that at high amplitudes, impact processes result in dissipation as well as shift of energy to other frequencies. Inadequate understanding of the physics precludes reliable predictions. In this introductory work, joint compliance is studied in both a numerical and experimental setting. A simple bolted interface is used as the test article and compliance is measured for the joint in both compression and in tension. This simple interface is shown to exhibit a strong non-linearity near the transition from compression to tension (or vice-versa). Modeling issues pertaining to numerically solving for the compliance are addressed. It is shown that the model predicts the experimental strains and compliance fairly well. It will be seen that the joint behavior is a mechanical analogy to a diode. In compression, the joint is very stiff, acting almost as a rigid link, while in tension the joint is soft, acting as a soft spring. Although there have been many other studies performed on bolted joints, the variety of joint geometries has demonstrated large variations in behavior. This study is an attempt to quantify the behavior of typical joints found in today`s weapon systems.

To properly determine what is needed in a structural health monitoring system, actual operational structures need to be studied. We have found that to effectively monitor the structural condition of an operational structure four areas must be addressed: determination of damage-sensitive parameters, test planning, information condensation, and damage identification techniques. In this work, each of the four areas has been exercised on an operational structure. The structures studied were all be wind turbines of various designs. The experiments are described and lessons learned will be presented. The results of these studies include a broadening of experience in the problems of monitoring actual structures as well as developing a process for implementing such monitoring systems.

Variation of model size as determined by grid density is studied for both model refinement and damage detection. In model refinement 3 it is found that a large model with a fine grid is preferable in order to achieve a reasonable correlation between the experimental response and the finite element model. A smaller model falls victim to the inaccuracies of the finite element method. As the grid become increasing finer, the FE method approaches an accurate representation. In damage detection the FE method is only a starting point. The model is refined with a matrix method which doesn`t retain the FE approximation, therefore a smaller model that captures most of the dynamics of the structure can be used and is preferable.