预测扇形孔气膜分布的“核”参数模型

傅奕融; 李月茹; 陈榴; 戴韧

doi:10.19805/j.cnki.jcspe.2026.250282

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预测扇形孔气膜分布的“核”参数模型

动力设备与系统 | 更新时间：2026-10-10

- 预测扇形孔气膜分布的“核”参数模型
- A Parametric Kernel Model to Predict Film Effectiveness of Fan-shaped Holes
- 动力工程学报 2026年46卷第1期页码：51-58
- 作者机构：
  
  1. 上海理工大学能源与动力工程学院,上海,200093
  2. 上海电气燃气轮机有限公司,上海,200240
- 作者简介：
  
  傅奕融(2001—),男,上海人,硕士研究生,主要研究方向为燃气透平气动及传热
  戴韧(通信作者),男,教授,博士生导师,E-mail:dairen@usst.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(52276034);国家科技重大专项资助项目(2017-V-0016-0069)
- DOI：10.19805/j.cnki.jcspe.2026.250282
  中图分类号：
- 网络首发：2026-01-15，
  
  纸质出版：2026-01-15
- 稿件说明：
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傅奕融,李月茹,陈榴,戴韧. 预测扇形孔气膜分布的“核”参数模型动力工程学报, 2026, 46(1): 51-58 https://doi.

org/10.19805/j.cnki.jcspe.2026.250282
傅奕融,李月茹,陈榴,戴韧. 预测扇形孔气膜分布的“核”参数模型动力工程学报, 2026, 46(1): 51-58 https://doi. DOI： 10.19805/j.cnki.jcspe.2026.250282.

org/10.19805/j.cnki.jcspe.2026.250282 DOI：

摘要

在离散孔群(群孔)覆盖区域的气膜冷却效率计算中

常规计算流体动力学(CFD)模拟面临建模网格复杂与计算资源耗费大的问题。因此

提出了一种参数形式的核函数

以描述单个气膜孔的冷却效率分布特征

预测气膜冷却效率

并结合数据驱动的机器学习技术和Sellers气膜冷却效率叠加方法

实现对群孔覆盖域冷却效率分布的高效预测。基于该"核"参数模型

成功复现了7-7-7扇形气膜孔的单孔和三列顺排群孔的冷却效率分布

并在给定区域内

利用整数规划方法获得了区域面平均冷却效率最高的顺排群孔布局。结果表明:"核"参数模型能够准确反映气膜孔覆盖域内冷却效率的分布特征

克服了传统关联式仅预测展向平均冷却效率的局限性

而且显著减少了机器学习所需的样本数量

可适用于不同工况下的气膜冷却效率预测。

Abstract

In the calculation of a regional film cooling effectiveness covered by arrayed discrete holes

conventional computational fluid dynamics (CFD) simulations are confronted with the challenges of complex grid modeling and high computation consumption. A parameterized kernel function was proposed to describe the distribution characteristics of a single fan-shaped hole and then to predict the film cooling efficiency. Integrating data-driven machine learning technique with the Sellers film cooling efficiency superposition method

the efficient prediction of cooling efficiency distribution in the coverage area of group holes was realized. Based on the parametric "Kernel" model

the cooling effectiveness distribution for both single and triple in-line rows of 7-7-7 fan-shaped holes were successfully replicated. Furthermore

In the given region

the layout of in-line group holes with the highest average cooling efficiency in the region was obtained with integer programming method. Results show that the parametric "Kernel" model can accurately reflect the distribution characteristics of cooling efficiency in the film hole coverage area

overcoming the limitation of traditional correlation equations that only predict spanwise average cooling efficiency

and significantly reduce the number of samples required for machine learning

which can be applied to the prediction of film cooling efficiency under different working conditions.

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references

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