Prediction of Thermoacoustic Instability in a Single-injector Model Rocket Combustor

被引：0

作者：

Wang Z. ^{[1
]}

Chen P. ^{[1
]}

Lin W. ^{[1
]}

Tong Y. ^{[1
]}

Guo K. ^{[1
]}

Huang W. ^{[1
]}

Nie W. ^{[1
]}

机构：

[1] Department of Aerospace Science and Technology, Space Engineering University, Beijing

来源：

Yuhang Xuebao/Journal of Astronautics | 2024年 / 45卷 / 03期

关键词：

Chaotic analysis; Combustion instability; Deep learning; Rocket engine;

D O I：

10.3873/j.issn.1000-1328.2024.03.015

中图分类号：

学科分类号：

摘要：

To achieve early detection and prediction of high-frequency unstable combustion in model rocket engines，a data-driven prediction framework is established. This framework extracts recurrence matrices for nonlinear feature extraction of combustion noise，and utilizes deep learning models for training and prediction. Based on the dynamic pressure signal measured from the hot-fire tests of a single-injectors rocket combustor，the prediction of combustion instability is carried out，which can predict the occurrence of combustion instability approximately 35 ms in advance. Altogether 25 groups of dynamic pressure datasets are used，including experiments with different combustion chamber geometries. The results of cross validation upon the prediction framework show that the prediction accuracy of the framework was above 95%，which indicates the effectiveness and robustness of the prediction framework. © 2024 Chinese Society of Astronautics. All rights reserved.

引用

页码：478 / 486

页数：8

共 22 条

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