A Spatio-Temporal Model for Response and Distributed Wave Load Estimation on Offshore Wind Turbines

Sequential Bayesian inference schemes show tremendous potential for online information extraction from sparsely instrumented, uncertain dynamical systems. Within this context, notable paradigms are the tasks of state, input-state, and joint input-state-parameter estimation. A problem that has been scarcely studied in this context is the concurrent estimation of dynamic states and distributed loads on the basis of output-only (response) measurements. Examples of particular practical interest include the estimation of wind pressure on wind turbine blades, high-rise buildings and bridges, as well as wave loading in offshore structures. In such cases, the sensing of distributed inputs is heavily constrained by the instrumentation cost and the oftentimes limited access for sensor deployment. To tackle this challenge, this contribution investigates the fusion of Gaussian process regression (GPR) models with physics-based system representations for the recursive state and distributed wave load estimation on monopile offshore wind turbines. In particular, the distributed excitation is modeled with a GPR, which enables the implementation of a spatio-temporal filtering for the input process, while the system dynamics are represented by a physics-based model, which is in turn tailored to a recursive Bayesian scheme for the solution of the state estimation problem. The proposed approach is assessed in terms of a simulated case study on the finite element model of an offshore wind turbine.