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| Barbed labyrinth channel optimization based on constrained multi-objective particle swarm algorithm |
| MA Ruijia, WEI Zhengying*, CHEN Xueli, MA Shengli |
| State Key Lab for Manufacturing System Engineering, Xi′an Jiaotong University, Xi′an, Shaanxi 710049, China |
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Abstract In order to improve the comprehensive performance of drip irrigation emitter, a barbed labyrinth path was designed according to the boundary layer separation condition, and the channel structure was optimized. The same size channel model was fabricated and the water flow in the model was experimented by Particle Image Velocimetry(PIV)to validate the accuracy of numerical simulation. In view of the hydraulic performance and anti-blockage performance, the three geometrical variables were taken as experimental factors, and the flow rate, flow pattern index and particle-passing rate as performance parameters were examined, then 25 orthogonal experiments were carried out. The influences of these structural variables on the performance parameters were summarized. Three empirical correlations between the structural variables and performance parameters were obtained by regression analysis. The streamline plots were investigated and the effects of vortices in the corners and downstream the barbs on both internal flow field and particle motion were analyzed. In the end, the flow was taken as the constraint condition, and the flow pattern index and particle passing rate, which were with the minimum and maximum polarity respectively, were taken as the objective function. As a result, the labyrinth path was optimized by using constrained particle swarm optimization algorithm under a multi-objective condition. The unique solution of the Pareto solution set was obtained in equal weights, which was subject to 0.483 flow pattern index, 0.955 particle-passing rate and 2.17 L/h flow rate. By using the constrained optimization design method, the optimized channel structure satisfies the flow constraint condition and has the optimal comprehensive performance.
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Received: 26 April 2017
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