|Table of Contents|

Citation:
 Hanyang Gong,Ruhua Yuan,Xiaodong Xing,et al.Optimized Design for the Plow of a Submarine Plowing Trencher[J].Journal of Marine Science and Application,2013,(1):98-105.[doi:10.1007/s11804-013-1170-0]
Click and Copy

Optimized Design for the Plow of a Submarine Plowing Trencher

Info

Title:
Optimized Design for the Plow of a Submarine Plowing Trencher
Author(s):
Hanyang Gong Ruhua Yuan Xiaodong Xing Liquan Wang Zhipeng Wang and Haixia Gong
Affilations:
Author(s):
Hanyang Gong Ruhua Yuan Xiaodong Xing Liquan Wang Zhipeng Wang and Haixia Gong
1. College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China 2. China Offshore Oil Engineering Co. Ltd., Tianjin 300000, China
Keywords:
submarine plowing trencher moldboard surface plow surface optimized design multi-objective genetic algorithm surface smoothness
分类号:
-
DOI:
10.1007/s11804-013-1170-0
Abstract:
The plow of the submarine plowing trencher is one of the main functional mechanisms, and its optimization is very important. The design parameters play a very significant role in determining the requirements of the towing force of a vessel. A multi-objective genetic algorithm based on analytical models of the plow surface has been examined and applied in efforts to obtain optimal design of the plow. For a specific soil condition, the draft force and moldboard surface area which are the key parameters in the working process of the plow are optimized by finding the corresponding optimal values of the plow blade penetration angle and two surface angles of the main cutting blade of the plow. Parameters such as the moldboard side angle of deviation, moldboard lift angle, angular variation of the tangent line, and the spanning length are also analyzed with respect to the force of the moldboard surface along soil flow direction. Results show that the optimized plow has an improved plow performance. The draft forces of the main cutting blade and the moldboard are 10.6% and 7%, respectively, less than the original design. The standard deviation of Gaussian curvature of moldboard is lowered by 64.5%, which implies that the smoothness of the optimized moldboard surface is much greater than the original.

References:

Aguilar MA, Aguilar FJ, Aguera F, Carvajal F (2005). The evaluation of close-range photogrammetry for the modeling of moldboard plow surfaces. Biosystems Engineering, 90(4), 397-407.
Bialek J (2011). Relationship between plough body shape and its specific resistance. Journal of Research and Applications in Agricultural Engineering, 56(2), 13-15.
Brown MJ, Bransby MF, Simon-Soberon F (2006). The characteristics of a model pipeline plough. Proceedings of the 6th International Conference on Physical Modelling in Geotechnics, Hong Kong, 4-6.
China Oilfield Services Ltd. (2006). Geological survey final report of submarine pipeline routing engineering. Department of Geophysical Survey Service Center, China Oilfield Services Ltd., Tianjin, China.
Chinese Academy of Agricultural Mechanization Sciences (2007). Agricultural machinery designing handbook. China Agricultural Science and Technology Press, Beijing, China, 188-189.
Chipperfield AJ, Fleming PJ (1995). The matlab genetic algorithm toolbox. IEE Colloquium on Applied Control Techniques Using MATLAB, Digest, 14-17.
Deb K (1999). Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary Computation, 7(3), 205-230.
Godwin RJ, O’Dogherty MJ, Saunders C, Balafoutis AT (2007). A force prediction model for mouldboard ploughs incorporating the effects of soil characteristic properties, plough geometric factors and ploughing speed. Biosystems Engineering, 97(1), 117-129.
Goldberg DE (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, USA, 1-2.
Gong HY, Wang LQ, Xing XD, Gong HX (2012). Plowing body modeling and soil cracking research of submarine pipe plow. Chinese Journal of Mechanical Engineering, 48(19), 134-140.
Gutierrez de Rave E, Jimenez-Hornero FJ, Munoz-Piorno JM, Giraldez JV (2011). The geometric characterization of mouldboard plough surfaces by using splines. Soil & Tillage Research, 112(1), 98-105.
Holland JH (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artific. The University of Michigan Press, Ann Arbor, US, 1.
Lauder K, Bransby F, Brown M (2008). Experimental testing of the performance of pipeline ploughs. Proceedings of the International Offshore and Polar Engineering Conference, Vancouver, 211-216.
Li N, Lu JY, Luo YZ (2011). Fairing and configuration optimization method of free-form space grid structures based on energy method. Engineering Mechanics, 28(10), 243-249.
MathWorks (2012). Genetic algorithm and direct search toolbox for use with MATLAB. The MathWorks, US, 9-17.
Peng MX (2008). Coulumb’s unified solution of Passive earth pressure on retaining wall. Chinese Journal of Geotechnical Engineering, 30(12), 1783-1788.
Qin SW, Pan GR, Gu C, Shi GG (2010). Fitting of spatial cylindrical surface based on genetic algorithm. Journal of Tongji University (Natural Science), 38(4), 604-607, 618.
Shrestha DS, Singh G, Gebresenbet G (2001). PM—Power and Machinery: Optimizing design parameters of a mouldboard plough. Journal Agricultural Engineering Research, 78(4), 377-389.
Xue DY, Chen YQ (2008). Advanced applied mathematical problem solutions with MATLAB. Tsinghua University Press, Beijing, China, 381-382.
Yang Q, Yang WC (2003). Working out optimization model of high-speed moldboard plow surface by using improved genetic algorithm. Transactions of the Chinese Society of Argicultural Engineering, (1), 80-83.
Zhao WG, Zhou Y, Li XL (2009). Methods for optimum design of curve and surface based on energy. Machinery Design & Manufacture, (6), 208-210.

Memo

Memo:
Supported the National Natural Science Foundation of China (No. 51179040) and Natural Science Foundation of Heilongjiang Province (No. E200904).
Last Update: 2013-03-14