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Bayesian calibration is widely used for inverse analysis and uncertaintyanalysis for complex systems in the presence of both computer models andobservation data. In the present work, we focus on large-scale fluid-structureinteraction systems characterized by large structural deformations. Numericalmethods to solve these problems, including embedded/immersed boundary methods,are typically not differentiable and lack smoothness. We propose a frameworkthat is built on unscented Kalman filter/inversion to efficiently calibrate andprovide uncertainty estimations of such complicated models with noisyobservation data. The approach is derivative-free and non-intrusive, and is ofparticular value for the forward model that is computationally expensive andprovided as a black box which is impractical to differentiate. The framework isdemonstrated and validated by successfully calibrating the model parameters ofa piston problem and identifying the damage field of an airfoil under transonicbuffeting.

Bayesian Calibration for Large-Scale Fluid Structure Interaction Problems Under Embedded/Immersed Boundary Framework