Predicting method of film cooling effectiveness distribution based on constrained neural network

被引：0

作者：

Zhu J. ^{[1
]}

Li D. ^{[1
]}

Tao Z. ^{[1
]}

Qiu L. ^{[1
]}

Cheng Z. ^{[1
]}

机构：

[1] School of Energy and Power Engineering, Beihang University, Beijing

来源：

Hangkong Dongli Xuebao/Journal of Aerospace Power | 2023年 / 38卷 / 07期

关键词：

cooling effectiveness distribution; flat-plate film; Gaussian function; neural network; superposition principle;

D O I：

10.13224/j.cnki.jasp.20220685

中图分类号：

学科分类号：

摘要：

To improve the design efficiency of film cooling in turbine vane， a neural network constrained by Gaussian function was proposed to predict the cooling effectiveness distribution of single film row，and the distribution of multi film rows was predicted by combining the modified superposition principle. For a single film row on the flat-plate，three coefficients of the Gaussian function were predicted with the main flow turbulence degree，density ratio，blowing ratio，film inclined angle，length-width ratio and dimensionless flow direction distance taken as inputs，and then the dimensionless lateral distance was substituted into the predicted Gaussian function to calculate the cooling effectiveness. The prediction error of the Gauss function constrained neural network for the average cooling effectiveness of the test samples was only 5.70%，which was 67% lower than that of network without constraints. Based on the model of single film row and the correction coefficient of multi film rows superposition principle predicted according to the blowing ratio and dimensionless coordinates， the cooling effectiveness distribution of multi film rows was calculated. The prediction errors of the average cooling effectiveness were only 6.19% and 12.19% with the blowing ratio at 0.5 and 1， respectively. According to the results， the proposed method can predict the film cooling effectiveness distribution accurately. © 2023 BUAA Press. All rights reserved.

引用

页码：767 / 775

页数：8

共 19 条

[1] JOHNSON B，, TIAN Wei, ZHANG Kai, Et al., An experimental study of density ratio effects on the film cooling injection from discrete holes by using PIV and PSP techniques, International Journal of Heat and Mass Transfer, 76, pp. 337-349, (2014)
[2] WU P S, TSAI S T, Et al., Effect of turbulence intensity on cross-injection film cooling at a stepped or smooth endwall of a gas turbine vane passage, The Scientific World Journal, 2014, (2014)
[3] LIU Gaowen, LIU Songling, Effect of injection angle on film cooling heat transfer of turbine cascade endwall, Journal of Propulsion Technology, 23, 6, pp. 496-499, (2002)
[4] GHORAB M G, HASSAN I G, LUCAS T., An experimental investigation of film cooling performance of louver scheme, Journal of Heat and Mass Transfer, 54, pp. 1387-1399, (2011)
[5] (2005)
[6] (1985)
[7] BALDAUF S, SCHEURLEN M, SCHULZ A, Et al., Correlation of film cooling effectiveness from thermographic measurements at engine like conditions, (2002)
[8] WALTERS D K., A systematic computational methodology applied to a three-dimensional film-cooling flow field, Journal of Turbomachinery, 119, 4, pp. 777-785, (1997)
[9] LI Xueying, REN Jing, JIANG Hongde, Algebraic anisotropic eddy-viscosity modeling for application to turbulent film cooling flows, (2011)
[10] NAGHASHNEJAD M, AMANIFARD N, DEYLAMI H M., A predictive model based on a 3-D computational approach for film cooling effectiveness over a flat plate using GMDH-type neural networks, Heat and Mass Transfer, 50, 1, pp. 139-149, (2014)

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