|
基于数字孪生航空发动机的数学建模综述
|
Abstract:
在航空工业中,航空发动机可以说是飞机中最为重要的一部分,承担着产生推力、提供电力和气源等重要任务。因此,对于发动机的性能表现和工况分析十分关键,而数学建模技术是一种有效的分析工具,具有十分广泛的应用前景。航空发动机模型是航空工业中不可或缺的工具,通过该模型可以进行发动机设计、性能优化和工况仿真分析等,对于提高发动机性能和降低运营成本具有十分重要的作用。本文将以航空发动机为研究对象,探讨建立数学模型进行发动机性能、燃油消耗和排放等方面的分析和优化,并对其运用的算法进行归纳整理。
In the aviation industry, aircraft engines can be said to be the most important part of the air-plane, responsible for generating thrust, providing power and air supply, and other important tasks. Therefore, the performance and operating condition analysis of the engine are crucial, and mathematical modeling is an effective analytical tool with a wide range of applications. The engine model is an indispensable tool in the aviation industry. Through this model, engine design, performance optimization, and simulation analysis of operating conditions can be performed, which plays a sig-nificant role in improving engine performance and reducing operational costs. This article will focus on aircraft engines as the research object, exploring the establishment of mathematical models to analyze and optimize engine performance, fuel consumption, and emissions. We will also summarize and organize the algorithms used in the process.
[1] | 曹增义, 单继东, 王昭阳, 陈贺利. 面向航空发动机制造的数字孪生应用架构探索与实践[J]. 航空制造技术, 2022, 65(19): 40-49. https://doi.org/10.16080/j.issn1671-833x.2022.19.040 |
[2] | Kiakojoori, S. and Khorasani, K. (2016) Dynamic Neural Networks for Gas Turbine Engine Degradation Prediction, Health Monitoring and Prognosis. Neural Computing and Applications, 27, 2157-2192.
https://doi.org/10.1007/s00521-015-1990-0 |
[3] | 孙绍辉, 王华伟, 陈福立. 多元退化信息的航空发动机可靠性预测[J]. 火力与指挥控制, 2013, 38(11): 32-35.
https://doi.org/10.3969/j.issn.1002-0640.2013.11.008 |
[4] | Chen, T.Q. and Guestrin, C. (2016) Xgboost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 13-17 August 2016, 785-794. https://doi.org/10.1145/2939672.2939785 |
[5] | 徐文英, 王大军, 卢朝阳, 等. 基于XGBoost算法的终端区进场航空器飞行时间预测[J]. 北京交通大学学报, 2022, 46(6): 72-79. |
[6] | 贾皓阳, 钱宇. 基于贝叶斯优化XGBoost算法的变压器故障诊断[J]. 黄河水利职业技术学院学报, 2023, 35(2): 37-43. https://doi.org/10.13681/j.cnki.cn41-1282/tv.2023.02.008 |
[7] | 樊智勇, 王振良, 刘哲旭. 基于XGBoost的民航飞机发动机性能参数预测模型[J/OL]. 计算机测量与控制: 1-8.
http://kns.cnki.net/kcms/detail/11.4762.TP.20230118.1153.012.html, 2023-05-07. |
[8] | 张家齐, 马怡灼, 周运森. 一种神经网络优化的非线性滤波算法[J/OL]. 控制工程: 1-9.
https://doi.org/10.14107/j.cnki.kzgc.20220521, 2023-05-09. |
[9] | Wang, C., Li, Y.G. and Yang, B.Y. (2017) Transient Performance Simulation of Aircraft Engine Integrated with Fuel and Control Systems. Applied Thermal Engineering, 114, 1029-1037.
https://doi.org/10.1016/j.applthermaleng.2016.12.036. |
[10] | Li, J., Cheng, J.-H., Shi, J.-Y. and Huang, F. (2012) Brief Introduction of Back Propagation (BP) Neural Network Algorithm and Its Improvement. In: Jin, D. and Lin, S., Eds., Advances in Computer Science and Information Engineering, Vol. 169, Springer, Berlin, Heidelberg, 553-558. https://doi.org/10.1007/978-3-642-30223-7_87 |
[11] | 陶理, 王晓宇, 谢园. 基于BP神经网络的飞机爬升段燃油消耗模型研究[J]. 科技视界, 2022(20): 45-47.
https://doi.org/10.19694/j.cnki.issn2095-2457.2022.20.13 |
[12] | Filippone, A. and Bojdo, N. (2018) Statistical Model for Gas Turbine Engines Exhaust Emissions. Transportation Research Part D: Transport and Environment, 59, 451-463. https://doi.org/10.1016/j.trd.2018.01.019 |
[13] | Liu, Y., Sun, X., Sethi, V., Nalianda, D., Li, Y-G. and Wang, L. (2017) Review of Modern Low Emissions Combustion Technologies for Aero Gas Turbine Engines. Progress in Aerospace Sciences, 94, 12-45.
https://doi.org/10.1016/j.paerosci.2017.08.001 |
[14] | 曹惠玲, 高建忠, 梁大敏. 民航发动机巡航阶段排放扩散模型研究[J]. 环境科学与技术, 2014, 37(S1): 444-447. |