基于线性遗传规划的航空发动机NO_(x)排放开环控制  

An Open-Loop Control Method of NO_(x) Emission of Aeroengine Based on Linear Genetic Programming

作　　者：侯廙 谭建国[1] 刘瑶 张冬冬 蒯子函 王欣尧 HOU Yi;TAN Jian-guo;LIU Yao;ZHANG Dong-dong;KUAI Zi-han;WANG Xin-yao(College of Aerospace Science and Engineering,National University of Defense Technology,Changsha 410000,China;School of Energy and Power Engineering,Beihang University,Beijing 100191,China)

机构地区：[1]国防科技大学空天科学学院,湖南长沙410000 [2]北京航空航天大学能源与动力工程学院,北京100191

出　　处：《推进技术》2023年第2期289-295,共7页Journal of Propulsion Technology

摘　　要：传统航空发动机减排方法主要优化发动机空间结构,这使得其结构变得很复杂,且面临回火、燃烧不稳定、宽范围适应性差等诸多问题。本文使用基于线性遗传规划在时域上调制燃料流量的方法来实现NO_(x)排放控制。实验基于双旋流的单头部燃烧室,搭建了开环控制实验系统;使用线性遗传规划算法对中心燃料控制律进行迭代优化;最后结合机器学习,分析了控制律的优化过程。结果表明:线性遗传规划算法通过对重要搜索路径的选取来获得最优控制律,在最优控制律下,NO_(x)排放量相较于无控制状态有效下降了43.1%。Optimising the spacial structure is a traditional method to reduce aeroengine emission, which makes the structure complicate and leads to many problems like tempering, instable combustion and poor widerange adaptability. Based on linear genetic programming, this paper uses a method that adjusts the fuel flow in time domain to control the emission of NO_(x). The experiment is conducted in a dual-swirl cylindrical burner, and an open-loop controlled experimental system is established. Linear genetic programming is used to iteratively optimize the control law of the inner fuel. Combined with machine learning, the optimization process is analysed.The results show that linear genetic programming algorithm obtains the optimal control law by selecting major search paths, and using the optimal control law, the emission of NO_(x)effectively decreases by 43.1% compared with the uncontrolled status.

关 键 词：航空发动机 NO_(x)排放 线性遗传规划 开环控制 机器学习 

分 类 号：V231.1[航空宇航科学与技术—航空宇航推进理论与工程]