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A New Pipe Routing Approach for Aero-Engines by Octree Modeling and Modified Max-Min Ant System Optimization Algorithm

Published online by Cambridge University Press:  19 September 2016

Y.-F. Qu ,
D. Jiang  and
X.-L. Zhang
Y.-F. Qu*
Affiliation:
Engineering Training CenterShanghai Polytechnic UniversityShanghai, China School of Mechanical EngineeringShanghai Jiaotong UniversityShanghai, China
D. Jiang
Affiliation:
School of Mechanical EngineeringShanghai Jiaotong UniversityShanghai, China
X.-L. Zhang
Affiliation:
School of Mechanical EngineeringShanghai Jiaotong UniversityShanghai, China
*
*Corresponding author ([email protected])
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Abstract

Aero-engines usually contain a lot of pipes and cables which have an important influence on product performance and reliability. In this paper, a new pipe routing approach for aero-engines is proposed. First, an adaptive octree modeling method is presented according to the characteristics of the layout space. After considering three types of engineering constraints, the total length of pipelines, the total number of bends and the natural frequency of pipelines are modeled as the optimal objective. Then, a Modified Max-Min Ant System optimization algorithm (MMMAS), which uses layered node selection and dynamic update mechanism, is proposed for pipe routing. For branch pipelines, ant colony searches in groups and parallel to improve the solution quality and speed up the convergence greatly. Finally, numerical comparisons with other current approaches in literatures demonstrate the efficiency and effectiveness of the proposed approach. And a case study of pipe routing for aero-engines is conducted to validate this approach.

Keywords

Pipe routingOctree modelLayered node selectionAnt colony optimization
Type
Research Article
Information
Journal of Mechanics , Volume 34 , Issue 1 , February 2018 , pp. 11 - 19
Copyright
Copyright © The Society of Theoretical and Applied Mechanics 2018 

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References

1. Liu, Q. and Wang, C., “Pipe-Assembly Approach for Aero-Engines by Modified Particle Swarm Optimization,” Assembly Automation, 30, pp. 365–377 (2010).Google Scholar
2. Yin, Y. H., Li, D. X., Bi, Z. Chen, H. and Zhou, C., “Novel Human-Machine Collaborative Interface for Aero-Engine Pipe Routing,” IEEE Transactions on Industrial Informatics, 9, pp. 2187–2199 (2013).Google Scholar
3. Yin, Y. H., Zhou, C. and Zhu, J. Y., “A Pipe Route Design Methodology by Imitating Human Imaginal Thinking,” CIRP Annals-Manufacturing Technology, 59, pp. 167–170 (2010).Google Scholar
4. Liu, Q. and Wang, C. E., “A Modified Particle Swarm Optimizer for Pipe Route Design,” The 11th IEEE International Conference on Computational Science and Engineering-Workshops, Washington, U.S. (2008).Google Scholar
5. Ito, T., “Piping Layout Wizard: Basic Concepts and Its Potential for Pipe Route Planning,” Methodology and Tools in Knowledge-Based Systems, pp. 438–447 (1998).Google Scholar
6. Ren, T., et. al., “A New Pipe Routing Method for Aero-Engines Based on Genetic Algorithm,” Journal of Aerospace Engineering, 228, pp. 424–434 (2014).Google Scholar
7. Lee, C. Y., “An Algorithm for Path Connections and Its Applications,” IRE Transactions on Electronic Computers, 10, pp. 346–365 (1961).Google Scholar
8. Ito, T., “A Genetic Algorithm Approach to Piping Route Path Planning,” Journal of Intelligent Manufacturing, 10, pp. 103–114 (1999).CrossRefGoogle Scholar
9. Fan, X. N., Lin, Y. and Ji, Z. S., “The Ant Colony Optimization for Ship Pipe Route Design in 3D Space”, Proceedings of the 6th IEEE World Congress on Intelligent Control and Automation, 1, pp. 3103–3108 (2006).Google Scholar
10. Rezaei, G., Afshar, H. M. and Rohani, M., “Layout Optimization of Looped Networks by Constrained Ant Colony Optimisation Algorithm,” Advances in Engineering Software, 70, pp. 123–133 (2014).Google Scholar
11. Park, J. H. and Storch, R. L., “Pipe-Routing Algorithm Development: Case Study of a Ship Engine Room Design,” Expert Systems with Applications, 23, pp. 299–309 (2002).Google Scholar
12. Kim, S. H, Ruy, W. S. and Jang, B. S., “The Development of a Practical Pipe Auto-Routing System in a Shipbuilding Cad Environment Using Network Optimization,” International Journal of Naval Architecture and Ocean Engineering, 5, pp. 468–477 (2013).Google Scholar
13. Liu, Q. and Wang, C. E., “A Discrete Particle Swarm Optimization Algorithm for Rectilinear Branch Pipe Routing,” Assembly Automation, 31, pp. 363–368 (2011).Google Scholar
14. Bai, X. L, Zhang, Y., Xie, H. L. and Ren, T., “Spatial Information Extraction for Automatic Layout Routing in Complex Product Design,” Chinese Control and Decision Conference, Guilin, China (2009).Google Scholar
15. Ouyang, X. P., Gao, F., Yang, H. Y. and Wang, H. X., “Two-Dimensional Stress Analysis of the Aircraft Hydraulic System Pipeline,” Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 226, pp. 532–539 (2012).Google Scholar
16. An, C. and Su, J., “Dynamic Behavior of Pipes Conveying Gas–Liquid Two-Phase Flow,” Nuclear Engineering and Design, 292, pp. 204–212 (2015).Google Scholar
17. Dorigo, M. and Blum, C., “Ant Colony Optimization Theory: A Survey,” Theoretical Computer Science, 344, pp. 243–278(2005).Google Scholar
18. Hu, X. B., “Research on Principles, Theory and Application of Ant Colony Optimization.” Ph.D. Dissertation, Chongqing University, Chongqing, China (2004).Google Scholar
19. Dorigo, M. and Gambardella, L. M., “Ant Colonies for the Travelling Salesman Problem,” BioSystems, 43, pp. 73–81 (1997).Google Scholar
20. Bullnheimer, B., Hartl, R.F. and Strauss, C., “A New Rank Based Version of the Ant System-a Computational Study,” Central European Journal for Operations Research and Economics, 7, pp. 25–38 (1999).Google Scholar