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.

流体工程系统的运行通常受各种不同参数的控制。就 CPU 和挂钟时间而言，使用基于第一原理的经典 CFD 方法研究整个参数谱的成本很高。因此，使用 CFD 对复杂流进行近乎实时的评估来支持操作被认为是不可行的。本文关注基于数据的替代模型的方法，用于预测不同集装箱装载条件和风向下，气动压力场对全尺寸集装箱船的上层结构施加的力。该策略旨在帮助实现节油运营，并基于两步法。在初始步骤中，编译从 3D Navier-Stokes 模拟获得的压力场的简化表示。到这个程度，将经典的适当正交分解与卷积神经网络自动编码器进行比较。随后的参数化采用前馈神经网络将简化模型与操作参数（即攻角和集装箱装载条件）联系起来，并实现快速的船上评估。两种方法都为压力场以及合力提供了类似的一致性，基于 CNN 的代理模型更加紧凑。