Group-based multiple pipe routing method for aero-engine focusing on parallel layout

Abstract

External pipe routing for aero-engine in limited three-dimensional space is a typical nondeterministic polynomial hard problem, where the parallel layout of pipes plays an important role in improving the utilization of layout space, facilitating pipe assembly, and maintenance. This paper presents an automatic multiple pipe routing method for aero-engine that focuses on parallel layout. The compressed visibility graph construction algorithm is proposed first to determine rapidly the rough path and interference relationship of the pipes to be routed. Based on these rough paths, the information of pipe grouping and sequencing are obtained according to the difference degree and interference degree, respectively. Subsequently, a coevolutionary improved differential evolution algorithm, which adopts the coevolutionary strategy, is used to solve multiple pipe layout optimization problem. By using this algorithm, pipes in the same group share the layout space information with one another, and the optimal layout solution of pipes in this group can be obtained in the same evolutionary progress. Furthermore, to eliminate the minor angle deviation of parallel pipes that would cause assembly stress in actual assembly, an accurate parallelization processing method based on the simulated annealing algorithm is proposed. Finally, the simulation results on an aero-engine demonstrate the feasibility and effectiveness of the proposed method.

Key words： multiple pipe routing; optimization algorithm; aero-engine; pipe grouping; parallel layout

Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities, China (Grant No. N2003025) and the Major Projects of Aero-engines and Gas Turbines, China (Grant No. J2019-I-0008-0008).

References

1 YinY H, ZhouC, ZhuJ Y. A pipe route design methodology by imitating human imaginal thinking. CIRP Annals, 2010, 59( 1): 167– 170

2 YinY H, XuL D, BiZ M. A novel human–machine collaborative interface for aero-engine pipe routing. IEEE Transactions on Industrial Informatics, 2013, 9( 4): 2187– 2199

3 LeeC Y. An algorithm for path connections and its applications. IRE Transactions on Electronic Computers, 1961, EC-10( 3): 346– 365

4 Hightower D W. A solution to line–routing problems on the continuous plane. In: Proceedings of the 6th Annual Design Automation Conference. New York: Association for Computing Machinery, 1988, 11–34

5 Schmidt-TraubH, KösterM, HoltkötterT. Conceptual plant layout. Computers & Chemical Engineering, 1998, 22 : S499– S504

6 BurdorfA, KampczykB, LederhoseM. CAPD—computer-aided plant design. Computers & Chemical Engineering, 2004, 28( 1–2): 73– 81

7 YamadaY, TeraokaY. An optimal design of piping route in a CAD system for power plant. Computers & Mathematics with Applications (Oxford, England), 1998, 35( 6): 137– 149

8 KimS H, RuyW S, JangB 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, 2013, 5( 3): 468– 477

9 ShionoN, SuzukiH, SaruwatariY. A dynamic programming approach for the pipe network layout problem. European Journal of Operational Research, 2019, 277( 1): 52– 61

10 ItoT. A genetic algorithm approach to piping route path planning. Journal of Intelligent Manufacturing, 1999, 10( 1): 103– 114

11 SandurkarS, ChenW. GAPRUS—genetic algorithms based pipe routing using tessellated objects. Computers in Industry, 1999, 38( 3): 209– 223

12 LiuQ, Wang C. Multi-terminal pipe routing by Steiner minimal tree and particle swarm optimisation. Enterprise Information Systems, 2012, 6( 3): 315– 327

13 ThantulageG I F. Ant colony optimization based simulation of 3D automatic hose/pipe routing. Dissertation for the Doctoral Degree. Middlesex: Brunel University, 2009,

14 MoeiniR, AfsharM H. Layout and size optimization of sanitary sewer network using intelligent ants. Advances in Engineering Software, 2012, 51 : 49– 62

15 JiangW, LinY, Chen M. A co-evolutionary improved multi-ant colony optimization for ship multiple and branch pipe route design. Ocean Engineering, 2015, 102 : 63– 70

16 WangY, YuY, Li K. A human-computer cooperation improved ant colony optimization for ship pipe route design. Ocean Engineering, 2018, 150 : 12– 20

17 WangC, LiuQ. Projection and geodesic-based pipe routing algorithm. IEEE Transactions on Automation Science and Engineering, 2011, 8( 3): 641– 645

18 LiuQ, Wang C. A graph-based pipe routing algorithm in aero-engine rotational space. Journal of Intelligent Manufacturing, 2015, 26( 6): 1077– 1083

19 MaJ, Liu J, XuL. Method of automatic branch-pipe routing based LTL-PRM algorithm. Journal of Mechanical Engineering, 2018, 54( 15): 160– 170 (in Chinese)

20 WuB C, YoungG S, SchmidtW. Applying fuzzy functions and sequential coordination to optimization of machinery arrangement and pipe routing. Naval Engineers Journal, 1998, 110( 6): 43– 54

21 LeeK H, LeeK Y. Knowledge-based nonmonotonic reasoning process in ship compartment design system. Expert Systems with Applications, 1997, 13( 2): 145– 154

22 Zhu D, Latombe J C. Pipe routing-path planning (with many constraints). In: Proceedings of the 1991 IEEE International Conference on Robotics and Automation. Sacramento: IEEE Computer Society, 1991, 1940–1947

23 FanJ, Ma M, YangX. Research on automatic laying out for external pipeline of aero-engine. Journal of Machine Design, 2003, 20( 7): 21– 23 (in Chinese)

24 MinJ G, RuyW S, ParkC S. Faster pipe auto-routing using improved jump point search. International Journal of Naval Architecture and Ocean Engineering, 2020, 12 : 596– 604

25 LiuQ, Wang C. Pipe-assembly approach for aero-engines by modified particle swarm optimization. Assembly Automation, 2010, 30( 4): 365– 377

26 QuY, Jiang D, YangQ. Branch pipe routing based on 3D connection graph and concurrent ant colony optimization algorithm. Journal of Intelligent Manufacturing, 2018, 29( 7): 1647– 1657

27 SuiH, Niu W. Branch-pipe-routing approach for ships using improved genetic algorithm. Frontiers of Mechanical Engineering, 2016, 11( 3): 316– 323

28 HartP E, NilssonN J, RaphaelB. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 1968, 4( 2): 100– 107

29 WardJ H Jr. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 1963, 58( 301): 236– 244

30 Li W. Hydraulic Calculation Manual. 2nd ed. Beijing: China Water & Power Press, 2006, 10‒11 (in Chinese)

31 LiX, Wang L, JiangQ. Differential evolution algorithm with multi-population cooperation and multi-strategy integration. Neurocomputing, 2021, 421 : 285– 302

32 KirkpatrickS, GelattC D, VecchiM P. Optimization by simulated annealing. Science, 1983, 220( 4598): 671– 680

33 Lozano-PérezT, WesleyM A. An algorithm for planning collision-free paths among polyhedral obstacles. Communications of the ACM, 1979, 22( 10): 560– 570