Influence of inflow directions and setting angle of inlet guide vane on hydraulic performance of an axial-flow pump.

Inlet flow direction significantly affects the hydraulic performance of an axial-flow pump. Usually, the research papers ignore this phenomenon, resulting in discrepancies between simulation and experimental results. This study examines the influence of inflow direction in five cases (0%, 5%, 10%, 15%, and 30% pre-swirl intensities) to determine the relationship between the pre-swirl intensity and the hydraulic performance of the axial-flow pump. Based on this, changing the setting angle of the inlet guide vane (IGV) is proposed and thoroughly investigated to reduce the effect of inflow direction. In this study, the influence of clearances in IGV blades on hydraulic performance is also investigated in detail. Numerical simulations are performed using ANSYS-CFX and a shear stress transport reattachment modification (SST k-[Formula: see text]) turbulence model with small y+ values at all walls. Specifically, the hydraulic performance curves and internal flow characteristics, including contours and streamlines, are assessed and analyzed. The inflow direction significantly impacts the hydraulic efficiency of the axial-flow pump. Increased pre-swirl intensity causes more loss in the IGV passage. The internal flow field and performance are not affected by the clearance at the hub and shroud of the IGV. However, the tip clearance of the impeller causes a decrease in hydraulic efficiency due to the tip leakage vortex. By adjusting the setting angle of the IGV, the efficiency and head gradually increase from a negative to a positive setting angle. Additionally, 30° is considered the critical setting angle for IGV.

Single and Multi-Objective Optimization of a Three-Dimensional Unbalanced Split-and-Recombine Micromixer.

The three-dimensional geometry of a micromixer with an asymmetrical split-and-recombine mechanism was optimized to enhance the fluid-mixing capability at a Reynolds number of 20. Single and multi-objective optimizations were carried out by using particle swarm optimization and a genetic algorithm on a modeled surrogate surface. Surrogate modeling was performed using the computational results for the mixing. Mixing and flow analyses were carried out by solving the convection-diffusion equation in combination with the three-dimensional continuity and momentum equations. The optimization was carried out with two design variables related to dimensionless geometric parameters. The mixing effectiveness was chosen as the objective function for the single-objective optimization, and the pressure drop and mixing index at the outlet were chosen for the multi-objective optimization. The sampling points in the design space were determined using a design of experiment technique called Latin hypercube sampling. The surrogates for the objective functions were developed using a Kriging model. The single-objective optimization resulted in 58.9% enhancement of the mixing effectiveness compared to the reference design. The multi-objective optimization provided Pareto-optimal solutions that showed a maximum increase of 48.5% in the mixing index and a maximum decrease of 55.0% in the pressure drop in comparison to the reference design.

Multi-Objective Optimizations of a Serpentine Micromixer with Crossing Channels at Low and High Reynolds Numbers.

In order to maximize the mixing performance of a micromixer with an integrated three-dimensional serpentine and split-and-recombination configuration, multi-objective optimizations were performed at two different Reynolds numbers, 1 and 120, based on numerical simulation. Numerical analyses of fluid flow and mixing in the micromixer were performed using three-dimensional Navier-Stokes equations and convection-diffusion equation. Three dimensionless design variables that were related to the geometry of the micromixer were selected as design variables for optimization. Mixing index at the exit and pressure drop through the micromixer were employed as two objective functions. A parametric study was carried out to explore the effects of the design variables on the objective functions. Latin hypercube sampling method as a design-of-experiment technique has been used to select design points in the design space. Surrogate modeling of the objective functions was performed by using radial basis neural network. Concave Pareto-optimal curves comprising of Pareto-optimal solutions that represents the trade-off between the objective functions were obtained using a multi-objective genetic algorithm at Re = 1 and 120. Through the optimizations, maximum enhancements of 18.8% and 6.0% in mixing index were achieved at Re = 1 and 120, respectively.