Modeling of Hydraulic Pipeline Transients Accompanied With Cavitation and Gas Bubbles Using Parallel Genetic Algorithms

Mathematical models of pressure transients accompanied with cavitation and gas bubbles are studied in this paper to describe the flow behavior in a hydraulic pipeline. The reasonable prediction for pressure transients in a low pressure hydraulic pipeline largely depends on several unknown parameters involved in the mathematical models, including the initial gas bubble volumes in hydraulic oils, gas releasing and resolving time constants. In order to identify the parameters in the mathematical models and to shorten the computation time of the identification, a new method-parallel genetic algorithm (PGA)-is applied in this paper. Based on the least-square errors between the experimental data and simulation results, the fitness function of parallel genetic algorithms is programed and implemented. The global optimal parameters for hydraulic pipeline pressure transient models are obtained. The computation time of parallel genetic algorithms is much shorter than that of serial genetic algorithms. By using PGAs, the executing time is 20 h. However, it takes about 204 h by using GAs. Simulation results with identified parameters obtained by parallel genetic algorithms agree well with the experimental data. The comparison between simulation results and the experimental data indicates that parallel genetic algorithms are feasible and efficient to estimate the unknown parameters in hydraulic pipeline transient models accompanied with cavitation and gas bubbles.

[1]  D. C. Wiggert,et al.  The Effect of Gaseous Cavitation on Fluid Transients , 1979 .

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  E. Benjamin Wylie,et al.  Fluid Transients in Systems , 1993 .

[4]  K A Edge,et al.  Simulation of hydraulic pipeline pressure transients using Matlab Simulink , 2005 .

[5]  Dalila Megherbi,et al.  Implementation of a parallel Genetic Algorithm on a cluster of workstations: Traveling Salesman Problem, a case study , 2001, Future Gener. Comput. Syst..

[6]  Angus R. Simpson,et al.  Pipeline column separation flow regimes , 1999 .

[7]  Reza Barkhi,et al.  Platform impact on performance of parallel genetic algorithms: Design and implementation considerations , 2006, Eng. Appl. Artif. Intell..

[8]  Mauricio Solar,et al.  A parallel genetic algorithm to solve the set-covering problem , 2002, Comput. Oper. Res..

[9]  Victor Szebehely,et al.  Gas Evolution in Liquids and Cavitation , 1950 .

[10]  Enrique Alba,et al.  Analyzing synchronous and asynchronous parallel distributed genetic algorithms , 2001, Future Gener. Comput. Syst..

[11]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[12]  Yiming Li,et al.  Parallel genetic algorithm for SPICE model parameter extraction , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.

[13]  Angus R. Simpson,et al.  Water hammer with column separation: A historical review , 2006 .

[14]  Xiaodong Li,et al.  The effects of varying population density in a fine-grained parallel genetic algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[15]  Jian-Jun Shu,et al.  Modelling vaporous cavitation on fluid transients , 2014 .

[16]  K A Edge,et al.  Pressure pulsations in reciprocating pump piping systems Part 1: Modelling , 1997, 1410.0803.

[17]  T. Takenaka,et al.  On the Transient Behavior of Oil Flow under Negative Pressure , 1985 .