Research Papers

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

[+] Author and Article Information
Songjing Li1

Department of Fluid Control and Automation, Harbin Institute of Technology, Harbin 150001, P.R.C.lisongjing@hit.edu.cn

Chifu Yang, Dan Jiang

Department of Fluid Control and Automation, Harbin Institute of Technology, Harbin 150001, P.R.C.


Corresponding author.

J. Appl. Mech 75(4), 041012 (May 14, 2008) (8 pages) doi:10.1115/1.2912934 History: Received February 26, 2007; Revised March 27, 2008; Published May 14, 2008

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 20h. However, it takes about 204h 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.

Copyright © 2008 by American Society of Mechanical Engineers
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Figure 8

Comparison of simulation and experimental pressure pulsations from the second transducer

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Figure 9

Pipeline filled with gas-liquid mixture: (a) initial situation and (b) under compression

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Figure 10

Predicted pressure pulsations when the element number is 20, 40, and 80, respectively (Vingas=0.1%V0)

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Figure 11

Tested pressure pulsations in three repeated experiments

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Figure 1

Studied pipeline

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Figure 2

Pressure pulsations with different initial volumes of gas bubbles

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Figure 3

Pressure pulsations with different time constants of gas releasing and resolving (see Table 2)

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Figure 4

Basic cluster computing configuration

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Figure 5

Test rig for pressure pulsation

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Figure 6

Fitness value with generation

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Figure 7

Comparison of simulation and experimental pressure pulsations from the first transducer



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