Constructing Accurate Phenomenological Surrogate for Fluid Structure Interaction Models

The accurate prediction of structural instability caused by vortex shedding behind bodies or by nonlinear unsteady aerodynamic is fundamental to avoid the degradation of structural performance or even failure of the system. Numerous approaches can represent analytical models to modeling both the structure and fluid. The CFD (Computational Fluid Dynamics) approaches consists of solving the Navier-Stokes equations directly, mostly limited by heavily computational costs that, many times, are tough to satisfy in the practical engineering. To increase the expectations of solving practical problems, the use of phenomenological surrogate models, are an alternative approach for the underlying physics, where phenomenological equations emulate the fluid dynamic forces acting on the structure, have become an essential tool to simplify the analysis and can be a very useful tool in broad industrial applications. However, constructing accurate surrogate models introduce additional challenges that will be addressed in this work. Most of these models present a series of empirical parameters that need to be calibrated from experimental data. To build an accurate phenomenological model we need putting this parameter variability in the general context of Uncertainty Quantification (UQ). We present a phenomenological model for fluid-structure interaction to be calibrated. In the first stage of this processes, we do global sensitivity analysis for the empirical parameters of the model, where uncertainty source is introduced earlier using the Sparse Grid Stochastic Collocation method. After this, a backward parameter estimation analysis is done using a Bayesian technique to calibrate these empirical parameters, through exploring posterior density functions. Synthetic data were generated as reference simulating experimental data to show the calibration technique used. This kind of analysis can help to understand the effects of varying empirical parameters in the response variables. The influence of these parameters and other coefficients that affect the dynamical response is analyzed and also discussed.