Controllable pitch propeller optimization through meta-heuristic algorithm

被引:21
作者
Bacciaglia, Antonio [1 ]
Ceruti, Alessandro [1 ]
Liverani, Alfredo [1 ]
机构
[1] Univ Bologna, Sch Engn & Architecture, DIN Dept, Viale Risorgimento 2, Bologna, Italy
关键词
Design propeller; Particle swarm optimization; Controllable pitch propeller; CAD; DESIGN;
D O I
10.1007/s00366-020-00938-8
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper describes a methodology to design and optimize a controllable pitch propeller suitable for small leisure ship boats. A proper range for design parameters has to be set by the user. An optimization based on the Particle Swarm Optimization algorithm is carried out to minimize a fitness function representing the engine's fuel consumption. The OpenProp code has been integrated in the procedure to compute thrust and torque. Blade's geometry and tables about pitch, thrust and consumption are the main output of the optimization process. A case study has been included to show how the procedure can be implemented in the design process. A case study shows that the procedure allows a designer to sketch a controllable pitch propeller with optimal efficiency; computational times are compatible with the design conceptual phase where several scenarios must be investigated to set the most suitable for the following detailed design. A drawback of this approach is given by the need for a quite skilled user in charge of defining the allowable ranges for design parameters, and the need for data about the engine and boat to be designed.
引用
收藏
页码:2257 / 2271
页数:15
相关论文
共 32 条
[11]  
Fishman G.S., 1995, Monte Carlo: Concepts, algorithms, and applications
[12]   Optimum design of B-series marine propellers [J].
Gaafary, M. M. ;
El-Kilani, H. S. ;
Moustafa, M. M. .
ALEXANDRIA ENGINEERING JOURNAL, 2011, 50 (01) :13-18
[13]   Efficient and multi-objective cavitating propeller optimization: An application to a high-speed craft [J].
Gaggero, Stefano ;
Tani, Giorgio ;
Villa, Diego ;
Viviani, Michele ;
Ausonio, Pierluigi ;
Travi, Piero ;
Bizzarri, Giovanni ;
Serra, Francesco .
APPLIED OCEAN RESEARCH, 2017, 64 :31-57
[14]   Solving symmetric and asymmetric TSPs by Ant Colonies [J].
Gambardella, LM ;
Dorigo, M .
1996 IEEE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION (ICEC '96), PROCEEDINGS OF, 1996, :622-627
[15]  
Golberg D. E., 1989, Genetic Algorithms in Search, Optimization, and Machine Learning
[16]   A design method for high-speed propulsor blades [J].
Griffin, PE ;
Kinnas, SA .
JOURNAL OF FLUIDS ENGINEERING-TRANSACTIONS OF THE ASME, 1998, 120 (03) :556-562
[17]  
Huismann J, 2017, P 5 INT S MAR PROP S
[18]  
Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/icnn.1995.488968
[19]  
Kerwin J, 2007, Hydrofoils and Propellers
[20]   Gradient based design optimization under uncertainty via stochastic expansion methods [J].
Keshavarzzadeh, Vahid ;
Meidani, Hadi ;
Tortorelli, Daniel A. .
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2016, 306 :47-76