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
In order to maintain a uniform distribution of pareto-front solutions, a modified NSGA-II algorithm coupled with a dynamic crowding distance(DCD) method is proposed for the multi-objective optimization of a mixed-flow...In order to maintain a uniform distribution of pareto-front solutions, a modified NSGA-II algorithm coupled with a dynamic crowding distance(DCD) method is proposed for the multi-objective optimization of a mixed-flow pump impeller. With the pump meridional section fixed, ten variables along the shroud and hub are selected to control the blade load by using a three-dimensional inverse design method. Hydraulic efficiency, along with impeller head, is applied as an optimization objective; and a radial basis neural network(RBNN) is adopted to approximate the objective function with 82 training samples. Local sensitivity analysis shows that decision variables have different impacts on the optimization objectives. Instead of randomly selecting one solution to implement, a technique for ordering preferences by similarity to ideal solution(TOPSIS) is introduced to select the best compromise solution(BCS) from pareto-front sets. The proposed method is applied to optimize the baseline model, i.e. a mixed- flow waterjet pump whose specific speed is 508 min?1?m3s?1?m. The performance of the waterjet pump was experimentally tested. Compared with the baseline model, the optimized impeller has a better hydraulic efficiency of 92% as well as a higher impeller head at the design operation point. Furthermore, the off-design performance is improved with a wider highefficiency operation range. After optimization, velocity gradients on the suction surface are smoother and flow separations are eliminated at the blade inlet part. Thus, the authors believe the proposed method is helpful for optimizing the mixed-flow pumps.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.
基金supported by the National Natural Science Foundation of China(Grant Nos.5137610051306018+4 种基金51206087and 51179091)the National Key Technology Research and Development Program(Grant No.2011BAF03B01)State Key Laboratory for Hydroscience and Engineering(Grant Nos.2014-KY-05 and 2015-E-03)Laboratory of Science and Technology on Waterjet Propulsion
文摘In order to maintain a uniform distribution of pareto-front solutions, a modified NSGA-II algorithm coupled with a dynamic crowding distance(DCD) method is proposed for the multi-objective optimization of a mixed-flow pump impeller. With the pump meridional section fixed, ten variables along the shroud and hub are selected to control the blade load by using a three-dimensional inverse design method. Hydraulic efficiency, along with impeller head, is applied as an optimization objective; and a radial basis neural network(RBNN) is adopted to approximate the objective function with 82 training samples. Local sensitivity analysis shows that decision variables have different impacts on the optimization objectives. Instead of randomly selecting one solution to implement, a technique for ordering preferences by similarity to ideal solution(TOPSIS) is introduced to select the best compromise solution(BCS) from pareto-front sets. The proposed method is applied to optimize the baseline model, i.e. a mixed- flow waterjet pump whose specific speed is 508 min?1?m3s?1?m. The performance of the waterjet pump was experimentally tested. Compared with the baseline model, the optimized impeller has a better hydraulic efficiency of 92% as well as a higher impeller head at the design operation point. Furthermore, the off-design performance is improved with a wider highefficiency operation range. After optimization, velocity gradients on the suction surface are smoother and flow separations are eliminated at the blade inlet part. Thus, the authors believe the proposed method is helpful for optimizing the mixed-flow pumps.
