Multi-objective optimization of a flow straightener in a large capacity firefighting water cannon
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In the present study, a multi-objective optimization of a flow straightener in a firefighting water cannon is performed by using the surrogate modeling and a hybrid multi-objective genetic algorithm to increase the jet range of the water cannon. Based on analysis using the three-dimensional Reynolds-averaged Navier-Stokes equations, the optimization is carried with a surrogate model and the radial basis neural network. Three geometric design variables, i.e., the length, the thickness of the blade, and the radius of the outer pipe of the flow straightener, are selected for the optimization. The pressure drop through the water cannon and the area-averaged turbulent kinetic energy at the outlet of the water cannon, which are closely related to the jet range of the water cannon, are selected as the objective functions to be minimized. The design space is determined through a parametric study, and the Latin hypercube sampling method is used to select the design points in the design space. The Pareto-optimal solutions are obtained through the optimization. Five representative Pareto-optimal solutions are selected to study the trade-off between two objectives.
Key wordsFirefighting water cannon multi-objective optimization surrogate model turbulence kinetic energy hydraulic loss
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This work was supported by the key science and technology project of Shanghai city (Grant No.13231203200). Thanks are also due to Dr. Arshad Afzal for helpful suggestions.
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