On the robustness of a model-based inverse force identification applied on a structure submerged in different media

Abstract

This paper addresses an experimental study on the implementation of an Augmented Kalman filter algorithm complemented by displacement dummy measurements, aiming at identifying a stochastic point force. For this purpose, the present experimental approach features a cantilevered structure instrumented with two pairs of accelerometers. A force sensor is used to measure the actual input force for benchmarking. The main objective of this study is to evaluate the ability of the algorithm to predict forces when the system is perturbed by different fluids surrounding the structure. Firstly, the structure is identified via experimental modal analysis, in two conditions, in air and underwater. Predicted and measured forces, for tests in each media, are compared showing good agreement. Additionally, the method is used to estimate forces applied when the structure is submerged in water while using a system model identified in air, in order to assess the algorithm robustness in scenarios that are either difficult or impossible to be tested. Although results accuracy in such cross-identification conditions depend on the closeness between the reference model and the actual boundary condition, in general, the frequency content of the predicted forces match with those predicted in the direct scheme, allowing qualitative data assessment in an otherwise unfeasible scenario.