Evaluation of a Machine Learning Turbulence Model in a Square Transverse Jet in Crossflow

F. P. Costa, R. Díaz, Pedro M. Milani, J. T. Tomita, C. Bringhenti
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Abstract

Film cooling is an important technique to ensure safe operation and performance fulfillment of turbines. Its ultimate goal is to protect the axial turbine blades from high gas temperatures. An appropriate study is necessary in order to obtain a reliable representation of the flow characteristics involved in such phenomena. Because of the high computational cost of high-fidelity simulations, the low-fidelity simulation method Reynolds Averaged Navier Stokes (RANS) is commonly used in practical configurations. However, the majority of the current turbulent heat flux models fail to accurately predict heat transfer in film cooling flows. Recent work suggests the use of machine learning models to improve turbulent closure in these flows. In the present work, a machine learning model for spatially varying turbulent Prandtl number previously described in the literature is applied to a transverse film cooling flow consisting of a jet square channel. The results obtained in the present work were compared to adiabatic effectiveness experimental data available in the literature to assess the performance of the machine learning model. The results shown that for low blowing ratios (BR = 0.2 and BR = 0.4) the proposed machine learning model has poor performance. However, for the case with the highest blowing ratio (BR = 0.8), the proposed model presented better results. These results are then explained in terms of the resulting turbulent Prandtl number field and suggest that the training set is not appropriate for capturing the turbulent heat flux in fully attached jets in crossflow.
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横流中方形横喷流的机器学习湍流模型评价
气膜冷却是保证汽轮机安全运行和性能实现的重要技术。其最终目标是保护轴向涡轮叶片免受高温气体的影响。为了获得这种现象所涉及的流动特性的可靠表示,进行适当的研究是必要的。由于高保真仿真的计算成本高,在实际配置中常用的是低保真仿真方法Reynolds average Navier Stokes (RANS)。然而,目前大多数湍流热流模型都不能准确地预测膜冷却流中的传热。最近的工作建议使用机器学习模型来改善这些流中的湍流关闭。在本工作中,将先前文献中描述的空间变化湍流普朗特数的机器学习模型应用于由射流方形通道组成的横向膜冷却流。将本工作中获得的结果与文献中可用的绝热有效性实验数据进行比较,以评估机器学习模型的性能。结果表明,对于低吹气比(BR = 0.2和BR = 0.4),所提出的机器学习模型性能较差。然而,对于最高吹气比(BR = 0.8)的情况,所提出的模型效果较好。然后用得到的湍流普朗特数场解释了这些结果,并表明训练集不适用于捕获横流中完全附着射流中的湍流热通量。
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Film Cooling Performance on Turbine Blade Suction Side With Various Film Cooling Hole Arrangements Evaluation of a Machine Learning Turbulence Model in a Square Transverse Jet in Crossflow LES Study of the Effects of Oscillations in the Main Flow on Film Cooling Effects of Film Hole Shape and Turbulence Intensity on the Thermal Field Downstream of Single Row Film Holes Experimental Study of Full Coverage Film Cooling Effectiveness for a Turbine Blade With Compound Shaped Holes
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