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Numerical shape optimization of a centrifugal pump impeller using artificial bee colony algorithm

机译:基于人工蜂群算法的离心泵叶轮数值形状优化

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摘要

Centrifugal pumps consume huge amounts of energy in various industrial applications. Therefore for these pumps, the improvement of machine efficiency has become a major challenge. Since the hydraulic performance of a centrifugal pump strictly depends on its impeller shape, in the present work, an efficient and original approach has been developed and applied to the design of centrifugal pump impellers in order to achieve a higher efficiency. A global optimization method based on the Artificial Neural Networks (ANNs) and Artificial Bee Colony (ABC) algorithm has been used along with a validated 3D Navier-Stokes flow solver to redesign the impeller geometry and improve the performance of a Berkeh 32-160 pump as a case study. In the next step, to verify the optimization results, all the domains within the centrifugal pump were simulated using the CFD method. The complete numerical characteristic curves of the pump with the optimized impeller were compared to the validated (using the available experimental data) numerical characteristic curves of the initial pump. The numerical results show an efficiency improvement of 3.59% at only 6.89 m increase of total pressure difference for the Berkeh 32-160 centrifugal pump. The new impeller geometry presents much more changes in the meridional channel and blade profile. The results indicate a reasonable improvement in the optimal design of pump impeller and a higher performance using the ABC algorithm.
机译:离心泵在各种工业应用中消耗大量能量。因此,对于这些泵,提高机器效率已成为主要挑战。由于离心泵的水力性能严格取决于其叶轮的形状,因此在当前工作中,已经开发出一种有效且独创的方法并将其应用于离心泵叶轮的设计中,以实现更高的效率。基于人工神经网络(ANN)和人工蜂群(ABC)算法的全局优化方法已与经过验证的3D Navier-Stokes流量求解器一起使用,以重新设计叶轮几何形状并改善Berkeh 32-160泵的性能作为案例研究。下一步,为验证优化结果,使用CFD方法对离心泵内的所有域进行了仿真。将具有优化叶轮的泵的完整数值特性曲线与初始泵的经过验证的数值特性曲线(使用可用的实验数据)进行比较。数值结果表明,伯克32-160离心泵的总压差仅增加6.89 m时,效率提高了3.59%。新的叶轮几何形状在子午通道和叶片轮廓方面呈现出更多变化。结果表明,使用ABC算法可以合理地改善泵叶轮的最佳设计,并获得更高的性能。

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