A multidisciplinary optimization was conducted to simultaneously improve the efficiency and reduce the radial force of a single-channel pump for wastewater treatment. A hybrid multi-objective evolutionary algorithm wa...A multidisciplinary optimization was conducted to simultaneously improve the efficiency and reduce the radial force of a single-channel pump for wastewater treatment. A hybrid multi-objective evolutionary algorithm was coupled with a surrogate model to optimize the geometry of the single-channel pump volute. Steady and unsteady Reynolds-averaged Navier-Stokes equations with a shear stress transport turbulence model were discretized using finite volume approximations and were then solved on tetrahedral grids to analyze the flow in the single-channel pump. The three objective functions represented the total efficiency, the sweep area of the radial force during one revolution, and the distance of the mass center of sweep area from the origin while the two design variables were related to the cross-sectional area of the internal flow of the volute. Latin hypercube sampling was employed to generate twelve design points within the design space, and response surface approximation models were constructed as surrogate models for the objectives based on the values of the objective function at the given design points. A fast non-dominated sorting genetic algorithm for local search was coupled with the surrogate models to determine the global Pareto-optimal solutions. The trade-off between the objectives was determined and was described in terms of the Pareto-optimal solutions. The results of the multi-objective optimization showed that the optimum design simultaneously improved the efficiency and reduced the radial force relative to those of the reference design.展开更多
文摘A multidisciplinary optimization was conducted to simultaneously improve the efficiency and reduce the radial force of a single-channel pump for wastewater treatment. A hybrid multi-objective evolutionary algorithm was coupled with a surrogate model to optimize the geometry of the single-channel pump volute. Steady and unsteady Reynolds-averaged Navier-Stokes equations with a shear stress transport turbulence model were discretized using finite volume approximations and were then solved on tetrahedral grids to analyze the flow in the single-channel pump. The three objective functions represented the total efficiency, the sweep area of the radial force during one revolution, and the distance of the mass center of sweep area from the origin while the two design variables were related to the cross-sectional area of the internal flow of the volute. Latin hypercube sampling was employed to generate twelve design points within the design space, and response surface approximation models were constructed as surrogate models for the objectives based on the values of the objective function at the given design points. A fast non-dominated sorting genetic algorithm for local search was coupled with the surrogate models to determine the global Pareto-optimal solutions. The trade-off between the objectives was determined and was described in terms of the Pareto-optimal solutions. The results of the multi-objective optimization showed that the optimum design simultaneously improved the efficiency and reduced the radial force relative to those of the reference design.
