用物理约束的神经网络，更准预测船舶轴功率以省油减排。
Physics-guided Neural Network-based Shaft Power Prediction for Vessels
- 将经验公式嵌入神经网络，融合物理规律与数据学习
- 在四艘货轮上测试，误差均低于传统方法和纯神经网络
- 适合关注航运节能、智能船舶动力优化的研究者
优化海上运输，尤其是降低船舶燃油消耗，对全球贸易至关重要。由于燃油消耗与船舶轴功率密切相关，准确预测轴功率成为降低成本和减排的关键。传统方法依赖经验公式，难以应对海况变化或船体污损等动态条件。本文提出一种混合式物理引导神经网络方法，将经验公式融入网络结构中，结合神经网络与传统方法的优势。基于四艘同尺寸货轮的数据进行评估，结果表明，该方法在所有测试船舶上的平均绝对误差、均方根误差和平均绝对百分比误差均低于基于经验公式的传统方法和基准神经网络。
原文摘要 · Abstract (English)
Optimizing maritime operations, particularly fuel consumption for vessels, is crucial, considering its significant share in global trade. As fuel consumption is closely related to the shaft power of a vessel, predicting shaft power accurately is a crucial problem that requires careful consideration to minimize costs and emissions. Traditional approaches, which incorporate empirical formulas, often struggle to model dynamic conditions, such as sea conditions or fouling on vessels. In this paper, we present a hybrid, physics-guided neural network-based approach that utilizes empirical formulas within the network to combine the advantages of both neural networks and traditional techniques. We evaluate the presented method using data obtained from four similar-sized cargo vessels and compare the results with those of a baseline neural network and a traditional approach that employs empirical formulas. The experimental results demonstrate that the physics-guided neural network approach achieves lower mean absolute error, root mean square error, and mean absolute percentage error for all tested vessels compared to both the empirical formula-based method and the base neural network.
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