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

机译:基于CFD的弗朗西斯汽轮机转子参数叶片模型的能量改进

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摘要

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 conditionsrn(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:提出了基于计算流体动力学(CFD)的弗朗西斯涡轮转子参数叶片模型的能量改进。通过基于CFD的液压混流式水轮机转轮优化结果,可以对提高的能量进行评估。通过应用基于几何参考模型的涡轮机械参数叶片建模器,获得了基于CFD优化过程使用的参数流道模型。该参数运行器模型和优化过程是通过使用三维Navier-Stoke商业涡轮机械定向CFD代码计算的。水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湍流模型进行了CFD计算。优化过程的目的是提高机器性能。这个过程是由CFD代码集成环境计算的,该环境结合了遗传算法和训练有素的人工神经网络。在优化周期收敛之后,不仅效率提高,而且功率提高。通过使用CFD结果获得传递到叶轮叶片上的能量以及扭矩和功率的转换。根据在三个工况rn(负载的100%,85%和75%)下沿叶轮叶片归一化弧长的压力分布,不仅可以计算参考叶轮的能量分布,还可以计算涡轮机的优化参数模型的能量分布跑步者。最后,评估了在相同操作条件下节省的平均能量。结果表明,基于CFD的优化应用可以修改和改进流道设计,从而提高已安装水力发电站的效率和功率。

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