Data-driven modeling of low-frequency hydrodynamic loads

Abstract This paper presents a data-driven approach for estimating the linear and quadratic transfer functions relating incoming waves to hydrodynamic loads on floaters. The procedure relies on constructing a nonlinear auto-regressive (NARX) surrogate model for forecasting the hydrodynamic loads and a harmonic probing algorithm for extracting the transfer functions of the system. The implemented harmonic probing method is of numerical nature and avoids the use of computationally expensive symbolic coding tools. The method was verified against synthetic data generated from a potential-flow-based numerical model of the INO WINDMOOR 12 MW floater. The main advantage of NARX models is the fexibility of their structure, meaning that they do not presuppose that the nonlinearity of the hydrodynamic load is quadratic, as it is commonly done.