典型文献
Hydrodynamic Performance Analysis of a Submersible Surface Ship and Resistance Forecasting Based on BP Neural Networks
文献摘要：
This paper investigated the resistance performance of a submersible surface ship(SSS)in different working cases and scales to analyze the hydrodynamic performance characteristics of an SSS at different speeds and diving depths for engineering applications.First,a hydrostatic resistance performance test of the SSS was carried out in a towing tank.Second,the scale effect of the hydrodynamic pressure coefficient and wave-making resistance was analyzed.The differences between the three-dimensional real-scale ship resistance prediction and numerical methods were explained.Finally,the advantages of genetic algorithm(GA)and neural network were combined to predict the resistance of SSS.Back propagation neural network(BPNN)and GA-BPNN were utilized to predict the SSS resistance.We also studied neural network parameter optimization,including connection weights and thresholds,using K-fold cross-validation.The results showed that when a SSS sails at low and medium speeds,the influence of various underwater cases on resistance is not obvious,while at high speeds,the resistance of water surface cases increases sharply with an increase in speed.After improving the weights and thresholds through K-fold cross-validation and GA,the prediction results of BPNN have high consistency with the actual values.The research results can provide a theoretical reference for the optimal design of the resistance of SSS in practical applications.
文献关键词：
中图分类号：
作者姓名：
Yuejin Wan;Yuanhang Hou;Chao Gong;Yuqi Zhang;Yonglong Zhang;Yeping Xiong
作者机构：
Naval Architecture and Ocean Engineering College,Dalian Maritime University,Dalian 116026,China;Faculty of Engineering and Physical Sciences,University of Southampton,Boldrewood Innovation Campus,SO16 7QF Southampton,UK
文献出处：
引用格式：
[1]Yuejin Wan;Yuanhang Hou;Chao Gong;Yuqi Zhang;Yonglong Zhang;Yeping Xiong-.Hydrodynamic Performance Analysis of a Submersible Surface Ship and Resistance Forecasting Based on BP Neural Networks)[J].哈尔滨工程大学学报（英文版）,2022(02):34-46
A类：
towing
B类：
Hydrodynamic,Performance,Analysis,Submersible,Surface,Ship,Resistance,Forecasting,Based,Neural,Networks,This,paper,investigated,resistance,performance,submersible,surface,ship,SSS,different,working,cases,scales,hydrodynamic,characteristics,speeds,diving,depths,engineering,applications,First,hydrostatic,test,was,carried,out,tank,Second,effect,pressure,coefficient,wave,making,analyzed,differences,between,three,dimensional,real,prediction,numerical,methods,were,explained,Finally,advantages,genetic,algorithm,GA,neural,network,combined,Back,propagation,BPNN,utilized,We,also,studied,parameter,optimization,including,connection,weights,thresholds,using,fold,cross,validation,results,showed,that,when,sails,low,medium,influence,various,underwater,not,obvious,while,high,increases,sharply,After,improving,through,have,consistency,actual,values,research,can,provide,theoretical,reference,optimal,design,practical
AB值：
0.577939
相似文献
机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
