Applying Ensemble Kalman Filter to Transonic Flows Through a Two-Dimensional Turbine Cascade
Applying Ensemble Kalman Filter to Transonic Flows Through a Two-Dimensional Turbine Cascade
复制标题
将集成卡尔曼滤波器应用于通过二维涡轮叶栅的跨音速流
DOI：
10.1115/1.4052472
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Manabe Kaito
中科院分区：
文献类型：
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作者：
Ito Sasuga;Furukawa Masato;Yamada Kazutoyo;Manabe Kaito
湍流是流体力学中最重要的现象之一。大涡模拟（LES）通常允许我们分析比使用基于非定常雷诺平均Navier-Stokes方程（URANS）的模拟时更小的涡流。此外，大涡模拟的数值解与实验和直接数值模拟的数值解也有很好的一致性。然而，URANS模拟在学术界和工业界经常使用，因为LES计算与URANS模拟相比要昂贵得多。本文针对T106 A低压涡轮机叶栅跨音速绕流问题，对k-ω两方程模型的待解系数进行了优化，以提高URANS湍流预报的精度。在优化方法上，将二维URANS与资料同化技术中的集合卡尔曼滤波相结合。在同化过程中，时间和展向平均LES结果被用作伪实验数据。三维URANS模拟进行的优化效果的评价。URANS模拟也适用于不同的涡轮机叶栅流的优化系数的鲁棒性的评价。这些URANS结果证实了优化系数提高了湍流预报的准确性。
Turbulence is one of the most important phenomena in fluid dynamics. Large eddy simulation (LES) generally allows us to analyze smaller eddies than when using simulations based on unsteady Reynolds-averaged Navier–Stokes equations (URANS). In addition, the numerical solutions of LES show good agreements with experiments and numerical solutions based on direct numerical simulation. URANS simulations are, however, frequently used in academia and industry because LES computations are much more expensive compared with URANS simulations. In this investigation, an optimization of unsolved coefficients of the k–ωtwo equations model is performed on the transonic flow around T106A low-pressure turbine cascade to improve the accuracy of turbulence prediction with URANS. For the optimization approach, two-dimensional URANS is combined with ensemble Kalman filter which is one of the data assimilation techniques. In the assimilation process, a time- and spanwise-averaged LES result is used as pseudo-experimental data. Three-dimensional URANS simulations are performed for the evaluation of the optimization effect. URANS simulations are also applied to a different turbine cascade flow for the evaluation of the robustness of the optimized coefficients. These URANS results confirmed that the optimized coefficients improve the accuracy of turbulence prediction.