[γ-Reθt]转捩模型在跨声速涡轮叶栅中的应用

Application of [γ-Reθt] Transition Model in Transonic Turbine Cascades

关键词:  涡轮叶栅  转捩模型  换热系数  湍流强度  粘性比

Key words:  Turbine cascades  Transition model  Heat transfer coefficient  Turbulence intensity  Viscosity ratio

摘要:

为了解涡轮叶栅在跨声速条件下的流动特性和准确预测涡轮叶栅外换热情况，对[γ-Reθt]转捩模型，依据零压力梯度时自由流湍流度衰减实验结果，给出了一种直接估算来流粘性比的方法，以保证叶片前缘附近具有正确的自由流湍流度分布，提高换热预测准确度；同时减少试算次数。对MARK II与VKI两种叶栅通道跨声速工况下的流动换热情况使用CFX软件，选取层流模型、SST k-ω模型以及缺省粘性比和设定合理粘性比的[γ-Reθt]转捩模型进行了数值模拟验证。计算结果与实验数据的对比表明:转捩模型优于其他模型；而采用本文方法给定进口粘性比，能准确预测转捩位置，同时显著改善[γ-Reθt]转捩模型对不同来流湍流度下涡轮叶栅表面换热的预测精度；当入口湍流度较高，相比采用缺省粘性比情况，压力面上换热系数的相对误差降低30%以上，控制在7%左右。

Abstract:

In order to understand the flow characteristic and accurately predict the heat transfer coefficient of turbine cascades at transonic condition， according to the experimental freestream turbulence intensity and its decay rate of zero pressure gradient test cases， a simple method for estimating free-stream viscosity ratio for [γ-Reθt] transition model has been given， to ensure the reasonable turbulence intensity near the blade leading edge and improve the predictive accuracy of heat transfer. It does not need many times of?trial?calculation. The flow and heat transfer over turbine cascades of MARK II and VKI has been studied with laminar model， shear stress transport (SST) low Reynolds model and [γ-Reθt] transition model which had default viscosity ratio and reasonable viscosity ratio， by using computational fluids dynamics software CFX. The comparative analyses for numerical results and experimental data indicate that [γ-Reθt] transition model is better than the others and also indicate that setting proper free-stream viscosity ratio can accurately predict the boundary layer transition position and can effectively improve the [γ-Reθt] transition model’s prediction accuracy of heat transfer coefficient. When the inlet turbulence intensity is high， the relative error of heat transfer coefficient on the pressure side will be reduced by more than 30% compared to using the default viscosity ratio， control about 7%.