STUDY ON FLOW FIELD PREDICTION OF TURBINE BLADES BY COUPLING SIMILARITY PRINCIPLE

Guo Zhendong; Cheng Hui; Chen Yun; Jiang Shoumin; Song Liming; Li Jun; Feng Zhenping

doi:10.6052/0459-1879-23-382

Volume 55 Issue 11

Nov. 2023

Turn off MathJax

Article Contents

Abstract

References

Chinese Journal of Theoretical and Applied Mechanics > 2023 > 55(11): 2647-2660. > DOI: 10.6052/0459-1879-23-382 CSTR: 32045.14.0459-1879-23-382

Guo Zhendong, Cheng Hui, Chen Yun, Jiang Shoumin, Song Liming, Li Jun, Feng Zhenping. Study on flow field prediction of turbine blades by coupling similarity principle. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(11): 2647-2660. DOI: 10.6052/0459-1879-23-382

Citation:

PDF (5441 KB)

STUDY ON FLOW FIELD PREDICTION OF TURBINE BLADES BY COUPLING SIMILARITY PRINCIPLE

*.
School of Energy and Power, Xi’an Jiaotong University, Xi’an 710049, China
†.
AECC Shenyang Engine Research Institute, Shenyang 110015, China

Received Date: August 07, 2023
Accepted Date: September 06, 2023
Available Online: September 07, 2023

Graphical Abstract

Abstract

Abstract

Computational fluid dynamics (CFD) is an important tool to evaluate the performance of turbine blades and etc. in the design stage. However, the numerical simulation of turbine blades that based on CFD method can be very time-consuming, which makes it rather difficult to meet the need of rapid iteration in the design process of turbine blades. In order to evaluate the performance of turbine blades rapidly and overcome the problem of insufficient generalization ability of pure data-driven prediction models as well, inspired by the concept of physics augmented machine learning, a novel method for turbine blade flow field prediction with strong generalization ability is proposed, by combining the similarity principle with deep learning model. Taking the prediction of the isentropic Mach number distribution at the surface of turbine blades as an example, we propose to make use of the similarity principle to normalize the geometric variables and aerodynamic parameters of turbine blades, and then prepare the training sample set and train the deep learning-based prediction model in the normalized parameter space. And accordingly, a unified prediction model based deep learning can be obtained, which can quickly predict the aerodynamic performance of turbine blades that in very different geometric size and have different boundary condition values. After finishing the model training, the trained prediction model is used to predict the flow fields of the turbine blades that works under different operation condition and of different shape in normalized design space, the flow fields of real-world blades of different size/different working conditions, and the flow fields of different section profiles of GE-E³ low-pressure turbines. The results showed that the predicted results were in good agreement with the CFD evaluation results, and the averaged relative error was less than 1.0%, which verify the accuracy and generalization ability of the proposed flow field prediction model coupling the similarity principle.

FullText(HTML)

References (33)

References

[1]	Viana FAC, Simpson TW, Balabanov V, et al. Special section on multidisciplinary design optimization: metamodeling in multidisciplinary design optimization: how far have we really come? AIAA Journal, 2014, 52(4): 670-690 doi: 10.2514/1.J052375
[2]	LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444 doi: 10.1038/nature14539
[3]	陈海昕, 邓凯文, 李润泽. 机器学习技术在气动优化中的应用. 航空学报, 2019, 40(1): 52-68 (Chen Haixin, Deng Kaiwen, Li Runze. Utilization of machine learning technology in aerodynamic optimization. Acta Aeronautica et Astronautica Sinica, 2019, 40(1): 52-68 (in Chinese) Chen Haixin, Deng Kaiwen, Li Runze. Utilization of machine learning technology in aerodynamic optimization. Acta Aeronautica et Astronautica Sinica, 2019, 40(1): 522480-5224809 (in Chinese)
[4]	孙刚, 王聪, 王立悦等. 人工智能在气动设计中的应用与展望. 民用飞机设计与研究, 2021, 142: 1-9, 147 (Sun Gang, Wang Cong, Wang Liyue, et al. Application and prospect of artificial intelligence in aerodynamic design. Civil Aircraft Design &Research, 2021, 142: 1-9, 147 (in Chinese) Sun Gang, Wang Cong, Wang Liyue, et al. Application and prospect of artificial intelligence in aerodynamic design. Civil Aircraft Design & Research, 2021, No. 142(03): 1-9 + 147 (in Chinese))
[5]	刘浩, 李国庆, 张深等. 机器学习在涡轮机械中的应用进展. 工程热物理学报, 2023, 44(4): 938-951 (Liu Hao, Li Guoqing, Zhang Shen, et al. Journal of Engineering Thermophysics. , 2023, 44(4): 938-951 (in Chinese) Liu Hao, Li Guoqing, Zhang Shen, et al. Journal of Engineering Thermophysics, 2023, 44(04): 938-951 (in Chinese)
[6]	王怡星, 韩仁坤, 刘子扬等. 流体力学深度学习建模技术研究进展. 航空学报, 2021, 42(4): 231-250 (Wang Yixing, Han Renkun, Liu Ziyang, et al. Progress of deep learning modeling technology for fluid mechanics. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 231-250 (in Chinese) Wang Yixing, Han Renkun, Liu Ziyang, et al. Progress of deep learning modeling technology for fluid mechanics. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 231-250 (in Chinese)
[7]	张伟伟, 寇家庆, 刘溢浪. 智能赋能流体力学展望. 航空学报, 2021, 42(4): 524689 (Zhang Weiwei, Kou Jiaqing, Liu Yilang. Prospect of artificial intelligence empowered fluid mechanics. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524689 (in Chinese) Zhang Weiwei, Kou Jiaqing, Liu Yilang. Prospect of artificial intelligence empowered fluid mechanics. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524689 (in Chinese)
[8]	金晓威, 赖马树金, 李惠. 物理增强的流场深度学习建模与模拟方法. 力学学报, 2021, 53(10): 2616-2629 (Jin Xiaowei, Laima Shujinn, Li Hui. Physics-enhanced deep learning methods for modelling and simulating flow fields. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(10): 2616-2629 (in Chinese) Jin Xiaowei, Laima Shujinn, Li Hui. Physics-enhanced deep learning methods for modelling and simulating flow fields. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(10): 2616-2629 (in Chinese))
[9]	陈皓, 郭明明, 田野等. 卷积神经网络在流场重构研究中的进展. 力学学报, 2022, 54(9): 2343-2360 (Chen Hao, Guo Mingming, Tian Ye, et al. Progress of convolution neural networks in flow field reconstruction. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(9): 2343-2360 (in Chinese) Chen Hao, Guo Mingming, Tian Ye, et al. Progress of convolution neural networks in flow field reconstruction. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(09): 2343-2360 (in Chinese)
[10]	惠心雨, 袁泽龙, 白俊强等. 基于深度学习的非定常周期性流动预测方法. 空气动力学学报, 2019, 37(3): 462-469 (Hui Xinyu, Yuan Zelong, Bai Junqiang, et al. A method of unsteady periodic flow field prediction based on the deep learning. Acta Aeronautica et Astronautica Sinica, 2019, 37(3): 462-469 (in Chinese) Hui Xinyu, Yuan Zelong, Bai Junqiang, et al. A method of unsteady periodic flow field prediction based on the deep learning. Acta Aeronautica et Astronautica Sinica, 2019, 37(3): 462-469 (in Chinese)
[11]	Sekar V, Jiang Q, Shu C, et al. Fast flow field prediction over airfoils using deep learning approach. Physics of Fluids, 2019, 31(5): 057103 doi: 10.1063/1.5094943
[12]	李凯, 杨静媛, 高传强等. 基于POD和代理模型的静气动弹性分析方法. 力学学报, 2023, 55: 1-10 (Li Kai, Yang Jingyuan, Gao Chuanqiang, et al. Static aeroelastic analysis based on proper orthogonal decomposition and surrogate model. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55: 1-10 (in Chinese) Li Kai, Yang Jingyuan, Gao Chuanqiang, et al. Static aeroelastic analysis based on proper orthogonal decomposition and surrogate model. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55: 1-10 (in Chinese)
[13]	Jin Y, Li S, Jung O. Prediction of flow properties on turbine vane airfoil surface from 3D geometry with convolutional neural network//Turbo Expo: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2019
[14]	杜周, 徐全勇, 宋振寿等. 基于深度学习的压气机叶型气动特性预测. 航空动力学报, 2023, 38(9): 2251-2260 (Du Zhou, Xu Quanyong, Song Zhenshou, et al. Prediction of aerodynamic characteristics of compressor blades based on deep learning. Journal of Aerospace Power, 2023, 38(9): 2251-2260 (in Chinese) Du Zhou, Xu Quanyong, Song Zhenshou, et al. Prediction of aerodynamic characteristics of compressor blades based on deep learning[J]. Journal of Aerospace Power, 2023, 38(9): 2251-2260 (in Chinese)
[15]	Wang Q, Yang L, Rao Y. Establishment of a generalizable model on a small-scale dataset to predict the surface pressure distribution of gas turbine blades. Energy, 2021, 214: 118878 doi: 10.1016/j.energy.2020.118878
[16]	Chen J, Liu C, Xuan L, et al. Knowledge-based turbomachinery design system via a deep neural network and multi-output Gaussian process. Knowledge-Based Systems, 2022, 252: 109352 doi: 10.1016/j.knosys.2022.109352
[17]	Li J, Liu T, Wang Y, et al. Integrated graph deep learning framework for flow field reconstruction and performance prediction of turbomachinery. Energy, 2022, 254: 124440 doi: 10.1016/j.energy.2022.124440
[18]	金晓威. 物理启发的钝体绕流场机器学习计算方法. [博士论文]. 哈尔滨: 哈尔滨工业大学, 2021 Jin Xiaowei. Physics-inspired machine learning methods for flow field around a bluff body. [PhD Thesis]. Harbin: Harbin Institute of Technology, 2021 (in Chinese)
[19]	Raissi M, Perdikaris P, Karniadakis GE. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 2019, 378: 686-707 doi: 10.1016/j.jcp.2018.10.045
[20]	陈苏, 丁毅, 孙浩等. 物理驱动深度学习波动数值模拟方法及应用. 力学学报, 2023, 55(1): 272-282 (Chen Su, Ding Yi, Sun Hao, et al. Methods and applications of physical information deep learning in wave numerical simulation. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(1): 272-282 (in Chinese) Chen Su, Ding Yi, Sun Hao, et al. Methods and applications of physical information deep learning in wave numerical simulation. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(1): 272-282 (in Chinese)
[21]	刘子俊, 冯勇, 陈景龙等. 基于多源数据的液体火箭发动机智能异常检测. 火箭推进, 2022, 48(3): 79-86 (Liu Zijun, Feng Yong, Chen Jinglong, et al. Intelligent anomaly detection of liquid rocket engine with multi-source data. Journal of Rocket Propulsion, 2022, 48(3): 79-86 (in Chinese) doi: 10.3969/j.issn.1672-9374.2022.03.010 Liu Zijun, Feng Yong, Chen Jinglong, et al. Intelligent anomaly detection of liquid rocket engine with multi-source data[J]. Journal of Rocket Propulsion, 2022, 48(03): 79-86 (in Chinese) doi: 10.3969/j.issn.1672-9374.2022.03.010
[22]	王珺, 吕海鑫, 陈景龙等. 液体火箭发动机健康状态智能检测方法. 火箭推进, 2021, 47(4): 52-58 (Wang Jun, Lyu Haixin, Chen Jinglong, et al. Intelligent detection method of liquid rocket engine health state. Journal of Rocket Propulsion, 2021, 47(4): 52-58 (in Chinese) doi: 10.3969/j.issn.1672-9374.2021.04.008 Wang Jun, Lv Haixin, Chen Jinglong, et al. Intelligent detection method of liquid rocket engine health state[J]. Journal of Rocket Propulsion, 2021, 47(04): 52-58(in Chinese) doi: 10.3969/j.issn.1672-9374.2021.04.008
[23]	Anderson JD, Wendt J. Computational Fluid Dynamics. New York: McGraw-Hill, 1995
[24]	季路成. 高性能叶轮机全3维叶片技术趋势展望. 航空发动机, 2013, 39(4): 9-18, 31 (Ji Lucheng. Trend of full three-dimensional blading techniques for high performance turbomachinery. Aeroengine, 2013, 39(4): 9-18, 31 (in Chinese) doi: 10.3969/j.issn.1672-3147.2013.04.003 Ji Lucheng. Trend of full three-dimensional blading techniques for high performance turbomachinery. Aeroengine, 2013, 39(04): 9-18 + 31 (in Chinese) doi: 10.3969/j.issn.1672-3147.2013.04.003
[25]	陈云, 宋立明, 王雷等. 自动优化技术在涡轮设计中的应用. 航空发动机, 2012, 47(4): 59-66 (Chen Yun, Song Liming, Wang Lei, et al. Application of automatic optimization technology in turbine design. Aeroengine, 2012, 47(4): 59-66 (in Chinese) doi: 10.3969/j.issn.1672-3147.2012.04.014 Chen Yun, Song Liming, Wang Lei, et al. Application of automatic optimization technology in turbine design[J]. Aeroengine, 2012, 47(04): 59-66 (in Chinese) doi: 10.3969/j.issn.1672-3147.2012.04.014
[26]	Xie J, Sage M, Zhao YF. Feature selection and feature learning in machine learning applications for gas turbines: A review. Engineering Applications of Artificial Intelligence, 2023, 117: 105591 doi: 10.1016/j.engappai.2022.105591
[27]	Korakianitis T. Hierarchical development of three direct-design methods for two-dimensional axial-turbomachinery cascades. Journal of Turbomachinery-Transactions of the ASME, 1993, 115: 314-324 doi: 10.1115/1.2929237
[28]	Zannetti L, Pandolfi M. Inverse design technique for cascades. NASA, 1984
[29]	邹正平, 叶建, 刘火星等. 低压涡轮内部流动及其气动设计研究进展. 力学进展, 2007, 37(4): 551-562 (Zhou Zhengping, Ye Jian, Liu Huoxing. Research progress on low pressure turbine internal flows and related aerodynamic design. Advances in Mechanics, 2007, 37(4): 551-562 (in Chinese) doi: 10.6052/1000-0992-2007-4-J2006-131 Zhou Zhengping, Ye Jian, Liu Huoxing. Research progress on low pressure turbine internal flows and related aerodynamic design[J]. Advances in Mechanics, 2007, 37(4): 551-562(in Chinese) doi: 10.6052/1000-0992-2007-4-J2006-131
[30]	Pritchard LJ. An eleven parameter axial turbine airfoil geometry model//Proceedings of the ASME 1985 International Gas Turbine Conference and Exhibit. Volume 1: Aircraft Engine, Marine, Turbomachinery. Microturbines and Small Turbomachinery. Houston, Texas, USA. March 18-21, 1985
[31]	Maaten L, Hinton G. Visualizing data using t-SNE. Journal of Machine Learning Research, 2008, 9: 2579-2605
[32]	Ning F. MAP: A CFD package for turbomachinery flow simulation and aerodynamic design optimization//Proceedings of the ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. Volume 2B: Turbomachinery. Düsseldorf, Germany. June 16-20, 2014
[33]	Rosenblatt F. The perceptron—A perceiving and recognizing automaton project para. Cornell Aeronautical Laboratory, Ithaca, New York, 1957: 85-460-1

[1]	Jiang Yifan, Li Shuaihui, Li Zhihui, Zhang Jun. ANALYSIS AND OPTIMIZATION OF AERODYNAMIC CHARACTERISTICS FOR VERY LOW EARTH ORBIT SATELLITES[J]. Chinese Journal of Theoretical and Applied Mechanics, 2025, 57(3): 616-626. DOI: 10.6052/0459-1879-24-582
[2]	Wan Yunyi, Huang Rui, Liu Haojie. TRANSONIC FLUTTER ANALYSIS OF A MORPHING WING VIA DATA DRIVEN METHOD[J]. Chinese Journal of Theoretical and Applied Mechanics, 2025, 57(2): 523-534. DOI: 10.6052/0459-1879-24-475
[3]	Peng Jing, Luo Can, Ma Jia, Li Kexian, Huang Zhu. GENETIC ALGORITHM-BASED OPTIMIZATION OF THE BARREL-PROJECTILE CONTACT/IMPACT NEURAL NETWORK MODEL[J]. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(10): 2974-2986. DOI: 10.6052/0459-1879-24-212
[4]	Guo Xu, Lian Yanping. Preface of theme articles on data-driven structure analysis and design[J]. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(7): 1838-1839. DOI: 10.6052/0459-1879-24-340
[5]	Fu Yang’aoxiao, Liu Qingzong, Ding Mingsong, Jiang Tao, Li Peng, Dong Weizhong, Xu Yong, Gao Tiesuo. ANALYSIS OF GAS MODEL’S INFLUENCE ON AERODYNAMIC CHARACTERISTICS FOR HYPERSONIC VEHICLE OVER HOT JET INTERACTION FLOW FIELD[J]. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(5): 1229-1241. DOI: 10.6052/0459-1879-21-685
[6]	Wang Yingxue, Gao Bo, Ren Wenqiang. AERODYNAMIC LOAD AND STRUCTURE STRESS ANALYSIS ON HOOD OF HIGH-SPEED RAILWAY TUNNEL[J]. Chinese Journal of Theoretical and Applied Mechanics, 2017, 49(1): 48-54. DOI: 10.6052/0459-1879-16-343
[7]	Wu Xiaojing, Zhang Weiwei, Song Shufang, Ye Zhengyin. UNCERTAINTY QUANTIFICATION AND GLOBAL SENSITIVITY ANALYSIS OF TRANSONIC AERODYNAMICS ABOUT AIRFOIL[J]. Chinese Journal of Theoretical and Applied Mechanics, 2015, 47(4): 587-595. DOI: 10.6052/0459-1879-14-372
[8]	Measured data analysis of urban expressway and research on traffic flow models[J]. Chinese Journal of Theoretical and Applied Mechanics, 2010, 42(4): 789-803. DOI: 10.6052/0459-1879-2010-4-lxxb2009-091
[9]	Tieqiao Tang, Haijun Huang, S.C. Wong, Rui Jiang. Lane changing analysis for two-lane traffic flow[J]. Chinese Journal of Theoretical and Applied Mechanics, 2007, 23(1): 49-54. DOI: 10.6052/0459-1879-2007-1-2006-282
[10]	样条积分方程法分析弹塑性板弯曲[J]. Chinese Journal of Theoretical and Applied Mechanics, 1990, 22(2): 241-245. DOI: 10.6052/0459-1879-1990-2-1995-940

Cited By

Cited by

Periodical cited type(2)

1.	杜东海，周利霖，李道奎，陈晨，廖一寰 . 热环境下多种材料圆柱壳动特性畸变相似研究. 力学学报. 2025(03): 627-643 . 本站查看
2.	吴昊恺，陈耀然，周岱，陈文礼，曹勇. 基于CNN与GAN深度学习模型近壁面湍流场超分辨率重构的精细化研究. 力学学报. 2024(08): 2231-2242 . 本站查看

Other cited types(1)

Get Citation

PDF

XML

Article Metrics

Article views (531) PDF downloads (82) Cited by(3)

STUDY ON FLOW FIELD PREDICTION OF TURBINE BLADES BY COUPLING SIMILARITY PRINCIPLE

Abstract

References

Related Articles

Cited by

Periodical cited type(2)

Other cited types(1)

Catalog

Article Metrics

Related

Download Center

Author Center

STUDY ON FLOW FIELD PREDICTION OF TURBINE BLADES BY COUPLING SIMILARITY PRINCIPLE

Abstract

References

Related Articles

Cited by

Periodical cited type(2)

Other cited types(1)

Catalog

Article Metrics

Related

Download Center

Author Center

Export File

Citation

Format

Content