Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Applying Uncertainty Quantification to Structural Systems: Parameter Reduction for Evaluating Model Complexity
:
Locke, Robert; Kupis, Shyla; Gehb, Christopher M.; Platz, Roland; Atamturktur, Sez  Society for Experimental Mechanics: Model Validation and Uncertainty Quantification, Vol.3 : Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019, Orlando, Florida, January 2831, 2019 Cham: Springer International Publishing, 2020 (Conference proceedings of the Society for Experimental Mechanics series) ISBN: 9783030120740 (Print) ISBN: 9783030120757 (Online) S.241256 
 International Modal Analysis Conference (IMAC) <37, 2019, Orlando/Fla.> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer LBF () 
 sensitivity analysis; Analysis of Variation; uncertainty quantification; Bayesian inference; MCMC 
Abstract
Different mathematical models can be developed to represent the dynamic behavior of structural systems and assess properties, such as risk of failure and reliability. Selecting an adequate model requires choosing a model of sufficient complexity to accurately capture the output responses under various operational conditions. However, as model complexity increases, the functional relationship between input parameters varies and the number of parameters required to represent the physical system increases, reducing computational efficiency and increasing modeling difficulty. The process of model selection is further exacerbated by uncertainty introduced from input parameters, noise in experimental measurements, numerical solutions, and model form. The purpose of this research is to evaluate the acceptable level of uncertainty that can be present within numerical models, while reliably capturing the fundamental physics of a subject system. However, before uncertainty quantification can be performed, a sensitivity analysis study is required to prevent numerical illconditioning from parameters that contribute insignificant variability to the output response features of interest. The main focus of this paper, therefore, is to employ sensitivity analysis tools on models to remove low sensitivity parameters from the calibration space. The subject system in this study is a modular springdamper system integrated into a space truss structure. Six different cases of increasing complexity are derived from a mathematical model designed from a twodegree of freedom (2DOF)mass springdamper that neglects single truss properties, such as geometry and truss member material properties. Model sensitivity analysis is performed using the Analysis of Variation (ANOVA) and the Coefficient of Determination R2. The global sensitivity results for the parameters in each 2DOF case are determined from the R2 calculation and compared in performance to evaluate levels of parameter contribution. Parameters with a weighted R2 value less than .02 account for less than 2% of the variation in the output responses and are removed from the calibration space. This paper concludes with an outlook on implementing Bayesian inference methodologies, delayedacceptance singlecomponent adaptive Metropolis(DASCAM) algorithm and Gaussian Process Models for Simulation Analysis (GPM/SA), to select the most representative mathematical model and set of input parameters that best characterize the system’s dynamic behavior.