Maximizing the performance of pump inducers using CFD-based multi-objective optimization

Abstract

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 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.