Abstract
In order to obtain a propeller with good efficiency and cavitation performance, the propeller sections were optimization designed using particle swarm optimization (PSO) method. An interactive calculation method was used in design process for the circulation distribution of designed propeller was not coincident with optimization circulation. The difference of circulation was defined as correction factor to adjust lift coefficients of sections. PSO method was used to optimize sections to improve the lift-to-drag ratio and pressure distribution. The convergence condition was the circulation distribution fulfilled optimum circulation distribution form. A MAU propeller was optimized using the method. Hydrodynamic performances of propeller and sections’ pressure distribution of original propeller were compared with optimized propeller. It indicates from the results that compared with traditional method, the PSO method is simpler in theory and cost less computing time. The open water efficiency of optimized propeller advanced obviously. The min negative pressure is smaller which means the cavitation performance is better.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kennedy, J., Ebehtart, R., Shi, Y.: Swarm Intelligence. Morgan Kaufmann, San rnaeiseo (2010)
Xu, P., Jiang, C.: Aerodynamic Optimization Design of Airfoil Based on Particle Swarm Optimization. Aircraft Design 128(15), 6–9 (2008)
Eberhart, R.C., Shi, Y.H.: Particle swarm optimization: development, applications and resources. In: Seoul, Korea: Proceedings of the 2001 Congress on Evolutionary Computation, pp. 81–86 (2001)
Luo, D.L., Yang, Z., Duan, H.B.: Heuristic particle swarm optimization algorithm for air combat decision making on CMTA. Transactions of Nanjing University of Aeronautics & Aeronauts 23(1), 20–27 (2006)
Zhang, Y., Song, B.: Aerodynamic optimization design and experimental study of a high-lift airfoil. Flight Dynamics 24(4), 70–72 (2006)
The research of optimization design of propeller performance based on genetic algorithm. Dalian University of Technology
Wang, X., Gao, Z.: Aerodynamic optimization design of airfoil based on genetic algorithm. Acta Aerodynamic Sinica 21(3), 70–75 (2000)
Chang, X., Guo, C.: Design of hydrofoil section based on particle swarm optimization. Ship Engineering 32(5), 1–3 (2010)
Xu, W., Wang, C., Huang, S., Yu, K., Zhou, B.: The application of Particle swarm optimization theory in the hydrofoil design. Journal of SHIP Mechanics 15(6), 598–604 (2011)
Gao, S.: Theory and Applications of Ant Colony Algorithm and Hybridizing Other Algorithms. In: A Dissertation Submitted to Nanjing University of Science and Technology for the Degree of Doctor of Philosophy, Nanjing (2005)
Hicks, R.M., Henne, P.A.: Wing Design by Numerical Optimization. Journal of Aircraft 15(7), 407–412 (1978)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, C., Wang, G., Ren, W., Guo, C., Zhou, B. (2013). The Application of Particle Swarm Optimization Arithmetic in Propeller Design. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38703-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-38703-6_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38702-9
Online ISBN: 978-3-642-38703-6
eBook Packages: Computer ScienceComputer Science (R0)