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CFD-BASED ENERGY IMPROVEMENT OF A PARAMETRIC BLADE MODEL FOR A FRANCIS TURBINE RUNNER

机译:基于CFD的竞技汽轮机跑步运动员模型的能量改进

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The computational fluid dynamic (CFD) based energy improvement of the parametric blade model for a Francis turbine runner is presented. The evaluation of the energy improved uses the results of CFD based optimization of a hydraulic Francis turbine runner. The parametric runner model used by the CFD based optimization process was obtained by applying a parametric blade modeller for turbomachinery based on a geometric reference model. This parametric runner model and the optimization process were computed by using a three dimensional Navier-Stoke commercial turbomachinery oriented CFD code. The flow within hydraulic turbines has a thin boundary layer and noticeable pressure gradients. Hence, the CFD computations were carried out using the Sparlat-Allmaras turbulence model. The aim of the optimization process was improve the performance of the machine. This process was computed by a CFD code integrated environment which combines genetic algorithms and a trained artificial neural network. After optimization cycle convergence, an increment not only in efficiency but also in power was obtained. The energy that is transferred to the runner blade and transformed in torque and power was obtained by using CFD results. From pressure distribution along the normalized arc length of the runner blade for three operating conditions (100%, 85% and, 75% of load) the energy distribution was computed not only for the reference runner but also for the optimized parametric model of the turbine runner. Finally, the averaged energy saved for the same operating conditions was evaluated. Results have shown that application of CFD based optimization can modify and improve runners design so as to increase the efficiency and power of installed hydraulic power stations.
机译:介绍了弗朗西斯汽轮机转轮的参数叶片模型的基于计算流体动态(CFD)的能量改进。能量改善的评估利用基于CFD的液压涡轮涡轮赛道优化的结果。基于CFD基于优化过程使用的参数流转换器模型是通过基于几何参考模型应用用于涡轮机的参数叶片制动器获得。通过使用三维Navier-Stoke商用涡轮机导向的CFD代码来计算该参数旋流器模型和优化过程。液压涡轮机内的流动具有薄的边界层和明显的压力梯度。因此,使用Sparlat-Allmaras湍流模型进行CFD计算。优化过程的目的是提高机器的性能。该过程由CFD码集成环境计算,该辅助码环境结合了遗传算法和培训的人工神经网络。在优化周期收敛之后,不仅获得了效率而且获得了电力的增量。通过使用CFD结果获得转移到转轮叶片并在扭矩和功率转换的能量。从压力分布沿着轨道叶片的归一化弧长,三个操作条件(100%,85%和75%的负载)不仅可以针对参考流行者计算的能量分布,而且还用于涡轮机的优化参数模型来计算能量分布赛跑者。最后,评估了相同操作条件的平均能量。结果表明,基于CFD优化的应用可以修改和改进跑步者设计,以提高安装的液压电站的效率和功率。

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