ABSTRACT

We present a hybrid linear potential flow - machine learning (LPF-ML) model for simulating weakly nonlinear wave-body interaction problems. In this paper we focus on using hierarchical modelling for generating training data to be used with recurrent neural networks (RNNs) in order to derive nonlinear correction forces. Three different approaches are investigated: (i) a baseline method where data from a Reynolds averaged Navier Stokes (RANS) model is directly linked to data from a LPF model to generate nonlinear corrections; (ii) an approach in which we start from high-fidelity RANS simulations and build the nonlinear corrections by stepping down in the fidelity hierarchy; and (iii) a method starting from low-fidelity, successively moving up the fidelity staircase. The three approaches are evaluated for the simple test case of a heaving sphere. The results show that the baseline model performs best, as expected for this simple test case. Stepping up in the fidelity hierarchy very easily introduce errors that propagate through the hierarchical modelling via the correction forces. The baseline method was found to accurately predict the motion of the heaving sphere. The hierarchical approaches struggled with the task, with the approach that steps down in fidelity performing somewhat better of the two.

INTRODUCTION

Linear hydro-dynamic models remain the tools-of-the trade in marine and ocean engineering despite their well-known assumptions of small amplitude waves and motions. As of now, fully nonlinear simulation tools simply cannot be exclusively used in the design loop due to the computational speed required to evaluate numerous irregular sea states. In order to extend the capabilities of the linear tools, nonlinearities are often included as approximated corrections. The two main corrections are standard Morison type drag (quadratic in relative velocity) and nonlinear Froude-Krylov forces (varying with instantaneous body position in the undisturbed wave). In this paper we exchange these classical approximations with estimates based on machine learning (ML) algorithms.

This content is only available via PDF.
You can access this article if you purchase or spend a download.