Abstract Real-time prediction of ship motion is essential for decision making in shipborne maritime operations. Differences in ship hulls render different ship motion characteristics, which consequently affects the performance of real-time prediction models. In this study, the ship hull scale effects in real-time motion prediction are investigated using the AR model. The ship datasets are generated by applying the strip theory. These ship motions datasets with various spectral characteristics are used in real-time prediction simulations. This study explores how the spectrum bandwidth, peak frequency, and ship hull scale influence prediction performance, and conclusions are drawn based on numerical simulation results. Prediction accuracy shows a negative relation to spectrum bandwidth and peak frequency. The AR model performance is better for ships with larger principal dimensions where ship hulls are the same. A preliminary empirical formulation for evaluating the maximum predictable time duration is developed based on the above regularities.

中文翻译：

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AR模型实时船舶运动预测中的尺度效应

摘要 船舶运动的实时预测对于船载海上作业的决策至关重要。船体的差异导致船舶运动特性不同，从而影响实时预测模型的性能。在这项研究中，使用 AR 模型研究了实时运动预测中的船体尺度效应。船舶数据集是通过应用条带理论生成的。这些具有各种光谱特征的船舶运动数据集用于实时预测模拟。本研究探讨了频谱带宽、峰值频率和船体尺度如何影响预测性能，并根据数值模拟结果得出结论。预测精度与频谱带宽和峰值频率呈负相关。对于船体相同的主要尺寸较大的船舶，AR 模型的性能更好。基于上述规律，开发了用于评估最大可预测持续时间的初步经验公式。