用流体仿真数据训练机器学习模型，提升船舶操纵系数识别精度。
Regularized Machine Learning for System Identification of Ship Free-Running Manoeuvres from CFD-Based Synthetic Data: A Comparative Study
- 采用正则化回归处理系数共线性问题，提高模型稳定性。
- 大角度舵角机动数据能更好识别船舶水动力系数，提升预测准确率。
- 岭回归在效率与精度间表现最佳，适合实际工程应用。
本研究探讨了基于自由航行模拟的CFD数据，利用监督学习方法识别船舶水动力系数。针对Abkowitz型操纵模型，比较了普通最小二乘法与正则化回归方法。训练与验证数据来自对锯齿形和回转圈操纵的URANS仿真，其结果经实验基准数据验证。分析评估了系数集大小、最小训练长度及操纵组合对模型性能的影响。结果表明，在合理选择系数、使用适当回归模型或增加输入数据多样性以缓解多重共线性前提下，大角度锯齿形操纵具备良好的系统辨识能力。更大的系数集虽提升模型适应性但更易受共线性影响；正则化回归显著降低共线性干扰，有效提升预测精度；引入更多样化的操纵数据亦可进一步优化性能。在对比模型中，岭回归在计算效率与预测精度间取得最优平衡。
原文摘要 · Abstract (English)
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.
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