In this paper, a three-dimensional numerical analysis was employed to investigate the flow and thermal fields over a leading edge of AGTB-B1 high-pressure turbine blade. The effect of different parameters on the film cooling was studied. The computational methodology includes the use of a structured, non-uniform hexahedral grid consisting of the main flow channel, the coolant delivery tube and the feeding plenum. The SIMPLEC algorithm was implemented for pressure–velocity coupling. Computations were carried out for the following range of film cooling parameters: lateral position of sister holes 3, 3.5 and 4.75 mm; lateral angle of sister holes of − 8°, 0° and 8°; blowing ratio of 0.7, 1.1 and 1.5; and diameter of injection hole of 2, 2.5 and 3. Adiabatic effectiveness was used as a criterion to judge the performance of film cooling. The results show a good agreement compared with previous experimental and numerical data. Finally, the GMDH-type neural network was successfully employed for modeling and presenting a correlation for film cooling effectiveness as a function of effective parameters.

中文翻译：

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GMDH型神经网络模型，用于预测姊妹孔参数对涡轮叶片上的薄膜冷却效率的影响

本文采用三维数值分析方法研究了AGTB-B1高压涡轮叶片前缘的流场和热场。研究了不同参数对薄膜冷却的影响。计算方法包括使用结构化，不均匀的六面体网格，该网格由主流路，冷却液输送管和进料气室组成。SIMPLEC算法用于压力-速度耦合。针对以下范围的膜冷却参数进行计算：姐妹孔3、3.5和4.75 mm的横向位置；姐妹孔的侧角为− 8°，0°和8°；吹塑比为0.7、1.1和1.5; 喷孔的直径分别为2、2.5和3。绝热效果用作判断薄膜冷却性能的标准。与以前的实验和数值数据相比，结果显示出良好的一致性。最后，GMDH型神经网络已成功用于建模，并提出了薄膜冷却效果与有效参数的关系。