论文标题
朝着改善船舶绩效的预测：对在职船舶监控数据进行建模的比较分析
Towards Improved Prediction of Ship Performance: A Comparative Analysis on In-service Ship Monitoring Data for Modeling the Speed-Power Relation
论文作者
论文摘要
船舶绩效的准确建模对于运输行业来说至关重要，以优化燃油消耗并随后减少排放。但是，预测现实情况下的速度关系仍然是一个挑战。在这项研究中，我们使用了来自具有不同船体形状的多个容器的服务监测数据，以比较数据驱动的机器学习（ML）算法的准确性与评估船舶性能的传统方法。我们的分析由两个主要部分组成：（1）将海洋试验曲线与适合操作数据的平静曲线进行比较，以及（2）具有基于ML的方法的多个添加波电阻理论的基准。我们的结果表明，一个简单的神经网络的表现超过了建立的半经验公式，按照第一原则。神经网络仅需要操作数据作为输入，而传统方法则需要广泛的船舶细节，而这些船只通常不可用。这些发现表明，数据驱动的算法对于预测实际应用中的船舶绩效可能更有效。
Accurate modeling of ship performance is crucial for the shipping industry to optimize fuel consumption and subsequently reduce emissions. However, predicting the speed-power relation in real-world conditions remains a challenge. In this study, we used in-service monitoring data from multiple vessels with different hull shapes to compare the accuracy of data-driven machine learning (ML) algorithms to traditional methods for assessing ship performance. Our analysis consists of two main parts: (1) a comparison of sea trial curves with calm-water curves fitted on operational data, and (2) a benchmark of multiple added wave resistance theories with an ML-based approach. Our results showed that a simple neural network outperformed established semi-empirical formulas following first principles. The neural network only required operational data as input, while the traditional methods required extensive ship particulars that are often unavailable. These findings suggest that data-driven algorithms may be more effective for predicting ship performance in practical applications.
