An inverse design method for body surface of given target pressure distribution

Tu, Haiwen; Yang, Yisi; Lyu, Xujian; Xie, De; Gao, Xianjiao; Sun, Jichao

doi:10.1016/j.oceaneng.2018.05.040

Cited by 2 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researchers [30][31][32] have undertaken a significant amount of work on the estimation method of the power curve. In the paper by Tu in 2018, the fixed power exponent of admiralty coefficient is replaced by 2/3 × (C b + C w ), and the power curves of the container ship is estimated by the modified admiralty formula.…”

Section: Comparison Of the Rbfnn And The Modified Admiralty Coefficientmentioning

confidence: 99%

Research on Accurate Prediction of the Container Ship Resistance by RBFNN and Other Machine Learning Algorithms

Yang

Song

et al. 2021

JMSE

Self Cite

View full text Add to dashboard Cite

Resistance is one of the important performance indicators of ships. In this paper, a prediction method based on the Radial Basis Function neural network (RBFNN) is proposed to predict the resistance of a 13500 transmission extension unit (13500TEU) container ship at different drafts. The predicted draft state in the known range is called interpolation prediction; otherwise, it is extrapolation prediction. First, ship features are extracted to make the resistance Rt prediction. The resistance prediction results show that the performance of the RBFNN is significantly better than the other four machine learning models, backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Then, the ship data is processed in a dimensionless manner, and the models mentioned above are used to predict the total resistance coefficient Ct of the container ship. The prediction results show that the RBFNN prediction model still performs well. Good results can be obtained by RBFNN in interpolation prediction, even when using part of dimensionless features. Finally, the accuracy of the prediction method based on RBFNN is greatly improved compared with the modified admiralty coefficient.

show abstract

Section: Comparison Of the Rbfnn And The Modified Admiralty Coefficientmentioning

confidence: 99%