The digital twin concept proves to be highly advantageous for smart ships as it allows the digital model to adapt in real time with changes in the physical ship. The mean value engine model (MVEM) is investigated with three different compressor mass flow rate models: a 2D interpolation model (2D), an experience-based M-JENSEN (MJ) model, and a physics-based Kang Song (KS) model. Each model is studied on a MAN 7S80ME engine with ABB A270 turbochargers. The root-mean-square errors (RMSE) for the test data are approximately 2.5 \({\mathrm{m}}^{3}/\mathrm{s}\) for the 2D model, 2.8 \({\mathrm{m}}^{3}/\mathrm{s}\) for the KS model, and 0.56 \({\mathrm{m}}^{3}/\mathrm{s}\) for the MJ model. For the training data, the RMSEs are around 0.0, 3.0, and 0.69 \({\mathrm{m}}^{3}/\mathrm{s}\), respectively. When coupling each model to the MVEM, it is observed that the calculated values differ only in the turbocharger speed. The RMREs under steady-state processes are 0.54% for MVEM with the 2D model, 1.63% for MVEM with the KS model, and 0.48% for MVEM with the MJ model. Under dynamic processes where the engine load is changed from 100% to 80%, the RMREs are 1.59%, 2.40%, and 1.58%, respectively. Among the studied models, the 2D model exhibits instability, the MJ model shows the highest accuracy, while the KS model performs the worst. However, all models demonstrate comparable results when used in the MVEM. Moreover, the physics-based KS model is a favorable choice for digital twin applications due to its excellent extrapolation ability and low dependence on measured data.

事实证明，数字孪生概念对于智能船舶非常有利，因为它允许数字模型实时适应实体船舶的变化。使用三种不同的压缩机质量流量模型研究平均值发动机模型 (MVEM)：二维插值模型 (2D)、基于经验的 M-JENSEN (MJ) 模型和基于物理的康松 (KS) 模型。每个模型均在配备 ABB A270 涡轮增压器的 MAN 7S80ME 发动机上进行研​​究。测试数据的均方根误差 (RMSE) 约为 2.5 \({\mathrm{m}}^{3}/\mathrm{s}\)对于 2D 模型，2.8 \({\mathrm{ KS 模型为m}}^{3}/\mathrm{s}\) ， MJ 模型为0.56 \({\mathrm{m}}^{3}/\mathrm{s}\) 。对于训练数据，RMSE 分别约为 0.0、3.0 和 0.69 \({\mathrm{m}}^{3}/\mathrm{s}\)。将每个模型与 MVEM 耦合时，可以观察到计算值仅在涡轮增压器速度方面有所不同。稳态过程下，2D 模型的 MVEM 的 RMRE 为 0.54%，KS 模型的 MVEM 的 RMRE 为 1.63%，MJ 模型的 MVEM 的 RMRE 为 0.48%。在发动机负荷从100%变化到80%的动态过程下，RMRE分别为1.59%、2.40%和1.58%。在所研究的模型中，2D模型表现出不稳定性，MJ模型显示出最高的精度，而KS模型表现最差。然而，所有模型在 MVEM 中使用时都显示出类似的结果。此外，基于物理的KS模型由于其出色的外推能力和对测量数据的低依赖性，是数字孪生应用的有利选择。