Some Recent Advances in Model Updating

Abstract

Abstract Model updating has now become a well-known field of application in engineering. The following is restricted to spatially discretized mathematical models. First, the classical sensitivity (including sensitivities of the measuring errors) and the selective sensitivity within updating are mentioned. The latter leads to a computational construction of selective excitation force vectors. Reliable estimates can then be obtained. The solutions of updating methods which result in nonlinear equations with respect to the parameters to be estimated are the global minima of these equations. This problem will be solved by applying interval arithmetic. Additionally, hints are given with regard to an efficient arithmetical procedure by massive parallel computing. Large-sized problems need the reduction of the model orders. Substructuring and incomplete modal transformations are the methods that are well-known and applied. Some necessary and sufficient conditions are discussed in this context. Examples will demonstrate the subjects discussed. The outlook mentions more advanced signal processing and identification methods which take non-stationary processes and non-linearly behaving systems into account.