This study investigates supervised machine learning techniques for identifying ship hydrodynamic coefficients from CFD-generated data from free-running simulations. Specifically, ordinary least squares and regularized regression methods are applied to Abkowitz-type manoeuvring models. Training and validation datasets are derived from URANS simulations of zig-zag and turning circle manoeuvres, which are validated against experimental benchmark data. The analysis evaluates the effects of coefficient set size, minimum training length required for predictive model training, and manoeuvre combinations on model performance. Results demonstrate the suitability of large-angle zig-zag manoeuvres for hydrodynamic system identification, provided that multicollinearity is addressed through appropriate coefficient selection, regression models, or input data variability. Larger coefficient sets offer greater model flexibility for variable conditions but are more prone to multicollinearity. Regularized regression techniques effectively mitigate multicollinearity and notably enhance prediction accuracy, as does incorporating more diverse manoeuvring data. Among tested models, Ridge regression provided the best compromise between computational efficiency and prediction accuracy.

翻译：本研究探究了基于CFD生成数据的自由航行模拟中，用于辨识船舶水动力系数的监督机器学习技术。具体而言，将普通最小二乘法和正则化回归方法应用于Abkowitz型操纵运动模型。训练与验证数据集来源于URANS模拟的Z形操纵和回转运动模拟，并与实验基准数据进行对比验证。分析评估了系数集大小、预测模型训练所需的最小训练长度以及机动组合对模型性能的影响。结果表明，大角度Z形操纵适用于水动力系统辨识，前提是通过适当的系数选择、回归模型或输入数据变异性来解决多重共线性问题。较大的系数集在可变条件下提供更强的模型灵活性，但更易出现多重共线性。正则化回归技术能有效缓解多重共线性并显著提升预测精度，引入更多样化的机动数据同样如此。在所测试的模型中，岭回归在计算效率与预测精度之间实现了最佳平衡。