Multiobjective Optimization of a Counterrotating Type Pump-Turbine Unit Operated at Turbine Mode

Advances in Mechanical Engineering

Suh

Choi

et al. 2016

Self Cite

This article presents a multi-objective optimization to improve the hydrodynamic performance of a counter-rotating type pump-turbine operated in pump and turbine modes. The hub and tip blade angles of impellers/runners with four blades, which were extracted through a sensitivity test, were optimized using a hybrid multi-objective genetic algorithm with a surrogate model based on Latin hypercube sampling. Three-dimensional steady incompressible Reynolds-averaged Navier-Stokes equations with the shear stress transport turbulence model were discretized via finite volume approximations and solved on a hexahedral grid to analyze the flow in the pump-turbine domain. For the major hydrodynamic performance parameters, the pump and turbine efficiencies were selected as the objective functions. Global Pareto-optimal solutions were searched using the response surface approximation surrogate model with the non-dominated sorting genetic algorithm, which is a multi-objective genetic algorithm. The trade-off between the two objective functions was determined and described with regard to the Pareto-optimal solutions. As a result, the pump and turbine efficiencies for the arbitrarily selected optimum designs in the Pareto-optimal solutions were increased as compared with the reference design.

“…Counter-rotating type pump-turbine. 6 and 81.26%, respectively, in turbine mode. Detailed specifications for the pump and turbine modes are given in Tables 1 and 2, respectively.…”

Section: Counter-rotating Type Pump-turbinementioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Simultaneous efficiency improvement of pump and turbine modes for a counter-rotating type pump-turbine

Advances in Mechanical Engineering

Suh

Choi

et al. 2016

Self Cite

“…Several studies have been conducted on the counterrotating-type pump-turbine unit to increase the performance and understand the internal flow mechanism of the unit by experimental and simulation methods [1][2][3][4][5]. In addition, Murakami and Kanemoto [6] studied the tandem impellers of the counter-rotating type pump unit operated in the turbine mode.…”

Section: Introductionmentioning

confidence: 99%

Optimized Blade Design of Counter-Rotating-Type Pump-Turbine Unit Operating in Pump and Turbine Modes

International Journal of Rotating Machinery

Suh

Choi

et al. 2018

Self Cite

In this study, a counter-rotating-type pump-turbine unit was optimized to improve the pump and turbine mode efficiencies simultaneously. Numerical analysis was carried out by solving three-dimensional Reynolds-averaged Navier–Stokes equations using the shear stress turbulence model. The hub and tip blade angles of the rear impeller (in the pump mode) were selected as the design variables by conducting a sensitivity test. An optimization process based on steady flow analysis was conducted using a radial basis neural network surrogate model with Latin hypercube sampling. The pump and turbine mode efficiencies of the unit were selected as the objective functions and they combined into a single specific objective function with the weighting factors. Consequently, the pump and turbine mode efficiencies of the optimum design increased simultaneously at overall range of flow rate, except for low flow rate of turbine mode, compared to the reference design.

“…Ma et al [2] utilized the CFD to study the incompressible viscous flows of mixed flow pumps in the incidents of power failure. De et al [3] conducted studies utilizing the CFD and DOE for the optimization of single-blade impellers; Kim et al [4] conducted studies utilizing the CFD to improve the performance of counter-rotating type pump-turbine units; Cho et al [5] designed axial-type fans using optimization methods based on the gradient method; and Kim et al [6] studied the shape optimization of mixed-flow pumps' impellers and diffusers through the DOE.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the shape parameter effects on the performance of the mixed-flow fan using CFD & Factorial design

Jung

et al. 2016

J Mech Sci Technol

Self Cite

Fans are representative turbo-machinery widely used for ventilation throughout the industrial world. Recently, as the importance of energy saving has been magnified with the fans, the demand for the fans with high efficiency and performance has been increasing. The representative method for enhancing the performance includes design optimization; in practice, fan performance can be improved by changing the shape parameters such as those of meridional plane, impeller, and diffuser. Before optimizing the efficient design, a process of screening to select important design parameters is essential. The present study aimed to analyze the effects of mixed-flow fans' shape parameters on fan performance (static pressure and fan static efficiency) and derive optimum models based on the results. In this study, the shape parameters considered in the impeller domain are as follows: tip clearance, number of blades, beta angle of Leading edge (LE) in the blade, and beta angle of Trailing edge (TE) in the blade. The shape parameters considered in the diffuser domain are as follows: meridional length of the Guide vane (GV), number of GV, beta angle of LE in the GV and beta angle of TE in the GV. The effects of individual shape parameters were analyzed using the CFD (Computational fluid dynamic) and DOE (Design of experiments) methods. The reliability of CFD was verified through the comparison between preliminary fan model's experiment results and CFD results, and screening processes were implemented through 2 4-1 fractional factorial design. From the analysis of DOE results, it could be seen that the tip clearance and the number of blades in the impeller domain greatly affected the fan performance, and the beta angle of TE at the GV in the diffuser domain greatly affected the fan performance. Finally, the optimum models with improved fan performance were created using linear regression equations derived from 2 4-1 fractional factorial design.