|Table of Contents|

Citation:
　Yuejin Wan,Yuanhang Hou,Chao Gong,et al.Hydrodynamic Performance Analysis of a Submersible Surface Ship and Resistance Forecasting Based on BP Neural Networks[J].Journal of Marine Science and Application,2022,(2):34-46.[doi:10.1007/s11804-022-00278-7]
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Hydrodynamic Performance Analysis of a Submersible Surface Ship and Resistance Forecasting Based on BP Neural Networks

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Title:
Hydrodynamic Performance Analysis of a Submersible Surface Ship and Resistance Forecasting Based on BP Neural Networks
Author(s):
Yuejin Wan1;  Yuanhang Hou1; 2;  Chao Gong1;  Yuqi Zhang1;  Yonglong Zhang1;  Yeping Xiong2
Affilations:
Author(s):
Yuejin Wan1;  Yuanhang Hou1; 2;  Chao Gong1;  Yuqi Zhang1;  Yonglong Zhang1;  Yeping Xiong2
1. Naval Architecture and Ocean Engineering College, Dalian Maritime University, Dalian, 116026, China;
2. Faculty of Engineering and Physical Sciences, University of Southampton, Boldrewood Innovation Campus, SO16 7QF, Southampton, UK
Keywords:
Submersible surface ship|K-fold cross-validation|Scale effect|Genetic algorithm|BP neural network
分类号:
-
DOI:
10.1007/s11804-022-00278-7
Abstract:
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.

References:

Alleman P, Kleiner A, Steed C (2012) Development of a new unmanned semi-submersible (USS). OMB No. 0704-0188, National Maritime Intelligence Office, Washington
Chen P, Ma J, Yang Q (2013) Coupled-dynamic response investigation of taut-wire mooring systems for deepwater semi-subermersible platform. Journal of Dalian Maritime University 39(1):65-69
Ferreira C (2001) Gene expression programming in problem solving. Proceedings of the 6th Online World Conference on Soft Computing in Industrial Applications
Gurgen S, Altin I, Ozkok M (2018) Prediction of main particulars of a chemical tanker at preliminary ship design using artificial neural network. Ships and Offshore Structures 13(5-6):459-465
Hirayama T, Takayama T, Hirakawa Y, Koyama H, Kondo S, Akiyama M (2005a) Trial experiment on the submersible surface ship utilizing downward lift. Conference Proceedings of the Japan Society of Naval Architects and Ocean Engineers, 1, 233-234
Hirayama T, Takayama T, Hirakawa Y, Koyama H, Nishimura K, Kondo S (2005b) Trial experiment on the submersible surface ship utilizing downward lift. Conference proceedings, The Japan Society of Naval Architects of Japan, 5, 141-142
Huo C, Dong W (2016) Free running tests on navigation mode conversion of a latent high speed craft. Journal of Shanghai Jiao Tong University, 50(8):1180-1185
Khan A, Bil C, Marion KE (2005) Theory and application of artificial neural networks for the real time prediction of ship motion. International Conference on Knowledge-based Intelligent Information&Engineering Systems, Melbourne, Australia
Li B, Guan G, Guan G, Qi Z, Lin Y (2018) Design optimization of special semi-submersible unmanned vehicle. Ship Engineering 40(6):95-99, 105.
Ling J, Kurzawski A, Templeton J (2016) Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. Journal of Fluid Mechanics 807:155-166
Liu C, Gu Y, Zhang J (2021) Extreme short-term prediction of ship motions based on wavelet filter and LSTM neural network. Journal of Ship Mechanics 25(3):299-310
Liu Z, Xiong Y, Han B (2009) Computational grid and turbulent model for calculating submaring viscous flow field. Journal of Huazhong University of Science and Technology (Natural Science Edition), 37(7):98-101
Manley JE (2008) Unmanned surface vehicles, 15 years of development. OCEANS 2008 IEEE, 1-4
Miyanawala TP, Jaiman RK (2017) An efficient deep learning technique for the Navier-Stokes Equations:Application to unsteady wake flow dynamics. Computer Science
Nematollahi A, Dad Vand A, Dawoodian M (2015) An axisymmetric underwater vehicle-free surface interaction:A numerical study. Ocean Engineering 96(1):205-214
Nguyen TN, Yu, Y, Li J, Gowripalan N, Sirivivatnanon V (2019) Elastic modulus of ASR-affected concrete:An evaluation using artificial neural network. Comput. Concr. 24:541-553.
Sarkar T, Sayer PG, Fraser SM (2015) A study of autonomous underwater vehicle hull forms using computational fluid dynamics. International Journal for Numerical Methods in Fluids 25(11):1301-1313
Terziev M, Tezdogan T, Incecik A (2020) Scale effects and real-scale ship hydrodynamics:A review. Ocean Engineering 245:110496
Ueno M (2010) Hydrodynamic derivatives and motion response of a submersible surface ship in unbounded water. Ocean Engineering 37(10):879-890
Ueno M, Tsukada Y, Sawada H (2011) A prototype of submersible surface ship and its hydrodynamic characteristics. Ocean Engineering 38(14-15):1686-1695
Watkins LJ (2011) Self-propelled semi-submersibles:the next great threat to regional security and stability. PhD thesis, Naval Post-graduate School, California, 56-63
Xing Z, Mccue L (2010) Modeling Ship equations of roll motion using neural network. Naval Engineers Journal 122(3):49-60
Zhang N, Shen HC, Yao HZ (2007) Numerical simulation of flow around submarine operating close to the bottom or near surface. Chuan Bo Li Xue/Journal of Ship Mechanics 11(4):498-507
Zhang N, Ying LM, Yao HZ, Sheng HC, Gao QX (2005) Numerical simulation of free surface viscous flow around submarine. Chuan Bo Li Xue/Journal of Ship Mechanics 9(3):29-39
Zeng Li (2006) Numerical simulation of free surface turbulent flow and its application. PhD thesis, Harbin Engineering University, Harbin, China
Zhou Hongguang, Ma Aimin, Xia Lang (2009) A research on the development of the unmanned surface vehicles. National Defense Science&Technology 30(6):17-21
Zhou Yumiao (2008) Analysis of flow field around the stern of a submarine. PhD thesis, Huazhong University of Science and Technology, Wuhan, China, 35-45

Memo

Memo:
Received date: 2022-03-15;Accepted date:2022-06-14。
Corresponding author:Yuanhang Hou,E-mail:houyuanhang@dlmu.edu.cn
Last Update: 2022-08-17