Improvement of Propeller Hydrodynamic Prediction Model Based on Multitask ANN and Its Application in Optimization Design - Liang Li; Yihong Chen; Lu Huang; Qing Hai; Denghai Tang; Chao Wang (Journal of Marine Science and Engineering)
A multitask learning (MTL) model based on artificial neural networks (ANNs) is proposed in this study to improve the prediction accuracy and physical reliability of marine propeller hydrodynamic performance. The propeller’s comprehensive geometric features are used as inputs, and the coefficients of quadratic polynomials for the thrust coefficient (KT) and torque coefficient (10KQ) curves are predicted as outputs. The loss function is customized through a positive gradient penalty of the curves to accelerate training. When the single-task and multitask models were compared, the prediction errors were reduced; KT decreased from 2.61% to 2.07%, 10 KQ decreased from 3.58% to 2.31%, and the efficiency (η) decreased from 3.04% to 2.00%. Non-physical fluctuations in the performance curves were effectively mitigated by the multitask model, yielding predicted curvatures which closely matched the experimental data. Strong generalization was demonstrated when the model was tested on unseen propellers, with deviations of 2.2% for KT, 4.6% for 10 KQ, and 3.8% for η. Finally, the model was applied to optimize the propeller design for a 325,000 ton very large ore carrier ship, where a Pareto front with 58 non-dominant solutions for the maximum speed and fluctuating pressure was successfully generated and effectively verified by the model’s test results. The model enhanced the prediction of the propeller performance and contributed to optimization in the propeller’s design.