Parameter estimation in modelling frequency response of coupled systems using a stepwise approach. (1st July 2019) - Göransson, P.; Cuenca, J.; Lähivaara, T.
Highlights: Multiple resonances induce local minima in inverse estimation for dynamic problems. A simplified coupled fluid-solid problem exhibiting additive noise is studied. A gradient-based optimiser and a Bayesian inversion framework are employed. An incremental-frequency stepwise approach finds the global solution of the problem. The low frequency asymptotic behaviour of the cost function governs the convergence. Abstract: This paper studies the problem of parameter estimation in resonant, acoustic fluid-structure interaction problems over a wide frequency range. Problems with multiple resonances are known to be subjected to local minima, which represents a major challenge in the field of parameter identification. We propose a stepwise approach consisting in subdividing the frequency spectrum such that the solution to a low-frequency subproblem serves as the starting point for the immediately higher frequency range. In the current work, two different inversion frameworks are used. The first approach is a gradient-based deterministic procedure that seeks the model parameters by minimising a cost function in the least squares sense and the second approach is a Bayesian inversion framework. The latter provides a potential way to assess the validity of the least squares estimate. In addition, it presents several advantages by providing invaluable information on the uncertainty and correlation between the estimated parameters. The methodology is illustrated on synthetic measurements with known design variables and controlled noise levels. The model problem is deliberately kept simple to allow for extensive numerical experiments to be conducted in order to investigate the nature of the local minima in full spectrum analyses and to assess the potential of the proposed method to overcome these. Numerical experiments suggest that the proposed methods may present an efficient approach to find material parameters and their uncertainty estimates with acceptable accuracy.