Inducers show generally a positive influence on the performance of centrifugal pumps in the two-phase regime, since they produce more uniform mixtures and increase the pressure before the impeller. However, the effect is much more pronounced in part-load compared to overload conditions. In this study, the air–water two-phase flow behavior in a pump inducer was numerically investigated. The main objectives were to clarify the effect of the inducer, the effective operating range, and to examine flow mixing. Several flow conditions were studied, covering part-load, optimal, and overload pumping conditions, together with different relevant gas volume fractions (1%, 3%, and 5%). The simulations were performed using a transient setup and a moving-mesh approach. Two-phase air–water interactions were modeled by the volume of fluid (VOF) method. After checking the proper discretization in space and time, the model was validated against experimental results, revealing excellent agreement. The numerical analysis was able to explain different effects of inducers in part-load and overload conditions. Under overload conditions, the flow separates, leading to the generation of axial vortices and to a negative pressure change across the inducer; additionally, the residence time is reduced, hindering mixing. These vortices are intensified as the gas volume fraction increases, reducing further the pressure downstream of the inducer. This is the reason why inducers can mainly be used in part-load and near optimal conditions in order to improve pumping of two-phase flows.
Pump inducers are usually employed within a limited flow rate range since the performance is known to drop out significantly far from their design point. Therefore, finding an optimal geometry that ensures efficient operation for a relatively wide range of flow rates is challenging. The present study tackles this problem using multi-objective optimization to identify optimal inducer configurations, delivering high performance for a wide flow range. 3D RANS single-phase turbulent simulations were performed using the $$k-\omega$$ k - ω turbulence model. The optimization was done by employing the Non-dominated Sorting Genetic Algorithm (NSGA-II) coupled with computational fluid dynamics (CFD). An established in-house flow optimization library (OPAL++) was used to automatically control the numerical simulations. The objective is to optimize the inducer geometrical parameters to simultaneously maximize the efficiency and pressure head curves, considering different flow rates, i.e., 80% (part-load), 100% (nominal), and 150% (overload) of the optimal flow rate for the considered pump. The optimization involves 8 most relevant design parameters, i.e., the axial blade length, blade sweep angle, blade pitch, hub taper angle, tip clearance gap, blade thickness at the hub, blade thickness at the tip, and the number of blades. A total of 5178 simulations over 37 generations have been needed to get a Pareto front containing 5 optimal configurations. This article discusses quantitatively the influence of each geometrical parameter on flow behavior and inducer performance. The results reveal in general that blade length, blade sweep angle, tip clearance gap, and blade thickness should be kept low for the considered application; inducers with high hub taper angles and 3 blades lead to optimal performance.
This study investigates the influence of various inducer configurations upstream of a pump impeller on the single and two-phase flow performance. Three pitch values (P = 0.151, 0.251, and 0.351 m), as well as three different numbers of blades (N = 2, 3, and 4 blades), were studied, leading to a total of 9 different inducer geometries. The main objective of the present study is to analyze and compare the corresponding performances and the two-phase mixing behavior, which is necessary for improving the two-phase pumping ability. 3D steady-state simulations using the Moving Reference Frame (MRF) approach were applied for single-phase flow, while a transient setup using a moving-mesh approach was employed for two-phase simulations. Turbulence was modeled by the Reynolds Stress Model (RSM), whereas the Volume of Fluid (VOF) method was applied to model air-water interactions. The results show that the increase in the number of blades leads to a high performance drop at overload (high-flow) conditions, but only to a slight performance enhancement at part-load (low-flow) conditions. Additionally, the effective flow range of the inducer corresponding to high efficiency becomes narrower for a higher number of blades. Concerning the inducer pitch, at part-load conditions, a lower pitch is slightly beneficial to smoothly suck the flow and damp the low-flow vortices; employing a high pitch at these conditions results in intensified flow vortices, reducing slightly the performance. On the other hand, the blade pitch is very influential for the performance at optimal and overload conditions, where a lower pitch causes flow blockage, leading to significant performance deterioration and a very limited range of applications. Generally, it was found that a modification of the inducer configuration affects the performance much more at overload compared to part-load conditions. Concerning two-phase mixing performance, the highest pitch provides the best mixing since the inducer is able to effectively churn the two phases. Similarly, an increase in the number of blades amplifies the turbulence between the two phases, thus improving mixing. Overall, a higher inducer pitch and a low to moderate number of inducer blades best ensure high performance, wide working range, and efficient two-phase mixing.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.