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Prediction of ice resistance for ice-going ships in level ice using artificial neural network technique

机译：使用人工神经网络技术预测冰冰船上冰船舶的抗性

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摘要

Ice resistance is affected by many parameters, i.e., ship geometries, ice properties and interaction properties. Over the years, various methods ranging from empirical, semi-empirical, numerical methods and various of their combinations were developed to calculate the ice resistance with certain degree of success. In this study, from a brand-new perspective, a data-driven approach for estimating ice resistance based on the artificial neural network was proposed. The artificial neural network (ANN) is one of the main tools for machine learning. It can accurately and efficiently correlate directly the inputs and outputs of complex and nonlinear systems, such as the ice resistance calculation system. This physical system involves several interaction phases and at least three groups of input parameters (i.e., ship, ice and interaction characteristics). Following the basic idea of ANN, we trained and built six ANN models based on datasets that were collected over past decades involving both model tests and full-scale measurements. The six different ANN models differ in the amount of input parameters. Based on comparative studies, among all the input parameters (i.e., 7 variables in total: ship length, ship breadth, ship draft, stem angle, ship speed, ice flexural strength and ice thickness), we found that the ship breadth, ice thickness and ship speed have the largest influence on the calculation of ice resistance. Afterwards, the 7-variable ANN model's prediction was compared with existing semi-empirical methods and measurements; and favorable agreement was achieved with fairly simple matrix form formulas (i.e., Eq. (19)). The formula offers another simple, yet reliable approach to calculate ice resistance. However, since the method is data driven, high quality data are always needed in improving the predicting capability of the relevant ANN model following the methodology outlined in this paper.
机译：抗性受许多参数，即船舶几何，冰属性和交互性能影响。多年来，开发了各种方法，从经验，半实证，数值和各种组合中断，以计算具有一定程度的成功抗性。在本研究中，从一个全新的视角来，提出了一种用于基于人工神经网络估计抗抗蚀性的数据驱动方法。人工神经网络（ANN）是机器学习的主要工具之一。它可以准确和有效地将复合物和非线性系统的输入和输出直接相关，例如耐用抗冰电阻计算系统。该物理系统涉及多个交互阶段和至少三组输入参数（即，船舶，冰和交互特征）。在ANN的基本思想之后，我们根据在过去几十年中收集的数据集培训并建立了六个ANN模型，涉及模型测试和全面测量。六种不同的ANN模型的输入参数的数量不同。基于比较研究，在所有输入参数中（即总共7个变量：船舶长度，船舶宽，船舶牵伸，茎角，船舶速度，冰弯曲强度和冰厚），我们发现船舶宽度，冰厚度而船舶速度对抗抗蚀性的计算具有最大的影响。之后，将7变量的ANN模型的预测与现有的半经验方法和测量进行了比较;通过相当简单的矩阵形式公式（即，方程式）实现了有利的协议。该公式提供另一种简单但可靠的方法来计算抗性。然而，由于该方法是数据驱动，因此在提高本文概述的方法后，始终需要高质量数据来提高相关ANN模型的预测能力。

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