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Structural optimization of film cooling holes based on neural networks and genetic algorithms
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Jianming WANG, Yunhao WANG, Heng LIN, Guangchao LI
Thermal Power Generation | 2026, 55(5) : 178 - 186
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Thermal Power Generation | 2026, 55(5): 178-186
Thermal energy science research
Structural optimization of film cooling holes based on neural networks and genetic algorithms
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Jianming WANG, Yunhao WANG, Heng LIN, Guangchao LI
Affiliations
  • Liaoning Key Lab of Advanced Test Technology for Aerospace Propulsion System, School of Aero Engine, Shenyang Aerospace University, Shenyang 110136, China
Published: 2026-05-25 doi: 10.19666/j.rlfd.202510036
Outline
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[Objective]

High-efficiency film cooling technology is an important means of increasing the turbine inlet temperature of gas turbines, and how to obtain the optimal film cooling hole and flow channel geometries has become a critical engineering issue to enhance the film cooling effectiveness of hot-section components in gas turbines.

[Methods]

In this study, the hole geometry of film cooling holes was parameterized, wherein relative coordinates and angles were adopted as the input parameters. Sampling was performed within the selected ranges of input parameters, and parameter optimization was conducted by coupling the BP neural network with the genetic algorithm, with the objective of maximizing the film cooling effectiveness of the film cooling holes. The influences of the shape and inclination angle of the film cooling hole channel on the film cooling effectiveness were investigated.

[Results]

Compared with cylindrical holes, the film cooling hole with the optimal configuration has a bigger spanwise width, smaller edge expansion angles on both spanwise sides, the same streamwise length as the cylindrical hole, and small-angle protrusion at the trailing edge. Under the condition of the same spanwise width and smooth flow channel, the area-averaged film cooling effectiveness of the optimal configuration is 18.28% higher than that of the dustpan-shaped hole. For the optimal configuration, the area-averaged film cooling effectiveness of the smooth flow channel is 5.3% higher than that of the unsmooth case. The optimized film cooling hole suppresses the trend of the mainstream entraining the cooling flow from both sides below, and alters the rotation direction of the kidney vortex pairs. Specifically, under the condition of the blowing ratio of 1 and hole inclination angles of 30°, 45° and 60° respectively, the area-averaged film cooling effectiveness of this configuration is 814.6%, 1 002.4% and 772.7% higher than that of the cylindrical hole.

[Conclusion]

The novel film cooling holes finally optimized in this study can reduce the intensity of kidney vortex pairs, enhance the wall adherence of coolant, and simultaneously delay the damping of film cooling effectiveness on the flat plate downstream of the film cooling hole. The optimized configuration maintains high film cooling effectiveness on the flat plate wall even in case of an increasing hole inclination angle, thus exhibiting broader adaptability to hole inclination angles. The optimal configuration with a smooth flow channel obtained in this study has certain engineering reference value.

film cooling  /  numerical simulation  /  machine learning  /  neural network  /  genetic algorithm
Jianming WANG, Yunhao WANG, Heng LIN, Guangchao LI. Structural optimization of film cooling holes based on neural networks and genetic algorithms[J]. Thermal Power Generation, 2026 , 55 (5) : 178 -186 . DOI: 10.19666/j.rlfd.202510036
  • National Natural Science Foundation of China(51406124)
Year 2026 volume 55 Issue 5
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Article Info
doi: 10.19666/j.rlfd.202510036
  • Receive Date:2025-10-15
  • Online Date:2026-08-14
  • Published:2026-05-25
Article Data
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History
  • Received:2025-10-15
  • Revised:2025-11-05
  • Accepted:2025-11-18
Funding
National Natural Science Foundation of China(51406124)
Affiliations
    Liaoning Key Lab of Advanced Test Technology for Aerospace Propulsion System, School of Aero Engine, Shenyang Aerospace University, Shenyang 110136, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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