Multifidelity Recursive Cokriging for Dynamic Systems' Response Modification

In order to perform the accurate tuning of a machine and improve its performance to the requested tasks, the knowledge of the reciprocal influence among the system's parameters is of paramount importance to achieve the sought result with minimum effort and time. Numerical simulations are an invaluable tool to carry out the system optimization, but modeling limitations restrict the capabilities of this approach. On the other side, real tests and measurements are lengthy, expensive, and not always feasible. This is the reason why a mixed approach is presented in this work. The combination, through recursive cokriging, of low-fidelity, yet extensive, numerical model results, together with a limited number of highly accurate experimental measurements, allows to understand the dynamics of the machine in an extended and accurate way. The results of a controllable experiment are presented and the advantages and drawbacks of the proposed approach are also discussed.