基于离子电流的爆震波压力非线性模型

推进技术 ›› 2012, Vol. 33 ›› Issue (2): 322-326.

基于离子电流的爆震波压力非线性模型

潘慕绚,黄金泉

南京航空航天大学能源与动力学院,江苏南京 210016;南京航空航天大学能源与动力学院,江苏南京 210016

发布日期:2021-08-15
作者简介:潘慕绚(1977—),女,博士生,副教授,研究领域为脉冲爆震发动机测控技术。E-mail:pan_muxuan@163.com
基金资助:
南京航空航天大学基本科研业务费专项科研项目中青年创新基金(NS2010043)。

Nonlinear Model of Detonation Wave Pressure Based on Ion Current

College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Published:2021-08-15

摘要/Abstract

摘要： 为了实现爆震波压力的软测量,依据碳氢焰离子形成原理及爆震波高速传播的特点,在分析爆震波离子形成机理的基础上,根据爆震波离子电流与压力信号的相似性,提出基于离子电流的爆震波压力非线性模型建模思路。采用RBF网络建立爆震波非线性模型,并给出网络结构、样本选取原则和预处理方法。开展了单次脉冲爆震试验,利用试验数据建立该压力非线性模型,并通过试验数据和模型输出的对比校核模型的有效性和准确性。 

关键词: 脉冲爆震发动机;离子电流;压力非线性模型;爆震波;RBF神经网络

Abstract: The ion formation mechanism in the detonation wave is analyzed based on the ion formation mechanism of the hydrocarbon flame and the character of high propagation speed of the detonation wave. Because of the similarity between the pressure signal of detonation wave and the ion current signal, the nonlinear pressure modeling is proposed. The RBF neural network is employed to establish the nonlinear pressure model by using the data from the single-detonation experiments. The network structure, the principle of training data selection and the data preprocess method are proposed. The model is validated by comparison between the experimental data and the network output. 

Key words: Pulse detonation engine;Ion current；Pressure nonlinear model; Detonation wave, RBF neural network

潘慕绚,黄金泉. 基于离子电流的爆震波压力非线性模型[J]. 推进技术, 2012, 33(2): 322-326.

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