Data-driven surrogate modeling of aerodynamic forces on the superstructure of container vessels

Abstract

The operation of fluid engineering systems is usually governed by a wide range of different parameters. Investigations of the entire parameter spectrum using classical, first-principle based CFD methods are costly with regards to CPU and wall-clock time. Therefore, a near real-time assessment of complex flows using CFD to support the operation is deemed unfeasible. The paper is concerned with methods for data-based surrogate models to predict the forces exerted by the aerodynamic pressure field on the superstructure of a full-scale container ship for different container loading conditions and wind directions. The strategy aims to assist a fuel-efficient operation and is based on a two-step approach. During an initial step, a reduced representation of pressure fields obtained from 3D Navier–Stokes simulations is compiled. To this extent, a classical proper orthogonal decomposition is compared with convolutional neural network autoencoders. A subsequent parameterization employs a feedforward neural network to link the reduced model with the operational parameters, i.e. the angle of attack and container loading condition, and to enable a rapid on board assessment. Both methods provide a similar agreement for the pressure fields, as well as the resulting forces, with the CNN-based surrogate model being significantly more compact.