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Axial flow compressor stall surge prediction method based on deep learning

A technology of axial flow compressor and deep learning, applied in neural learning methods, special data processing applications, biological neural network models, etc., can solve problems such as low accuracy and poor reliability

Inactive Publication Date: 2020-10-02
DALIAN UNIV OF TECH
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Problems solved by technology

[0004] Aiming at the problems of low accuracy and poor reliability in the prior art, the present invention provides a method for predicting stall and surge of an axial compressor based on deep learning

Method used

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  • Axial flow compressor stall surge prediction method based on deep learning
  • Axial flow compressor stall surge prediction method based on deep learning
  • Axial flow compressor stall surge prediction method based on deep learning

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Embodiment Construction

[0076] The present invention will be further described below in conjunction with the accompanying drawings. The background of the present invention is the surge experimental data of a certain type of aeroengine, and the process flow of the axial compressor stall surge prediction method based on deep learning is as follows: figure 1 shown.

[0077] figure 2 It is a flow chart of data preprocessing, and the steps of data preprocessing are as follows:

[0078] S1. In order to ensure the objectivity of the test results, before processing the experimental data, divide the experimental data into a test data set and a training data set. There are 16 sets of experimental data. Each set of experiments includes the dynamic pressure values ​​from normal to surge measured by 10 measurement points. The sensor measurement frequency is 6kHz. The tip of the first stage stator, the tip of the first stage stator (three in the circumferential direction), the tip of the second stage stator, th...

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Abstract

The invention discloses an axial flow compressor stall surge prediction method based on deep learning, and belongs to the technical field of aero-engine modeling and simulation. The method comprises:firstly, preprocessing aircraft engine surge data, and dividing a test data set and a training data set from experimental data; secondly, sequentially constructing an LR branch network module, a WaveNet branch network module and an LR-WaveNet prediction model; and finally, performing real-time prediction on the test data: firstly, preprocessing the test set data in the same way, and adjusting thedata dimension according to the input requirement of the LR-WaveNet prediction model; according to the time sequence, by adopting an LR-WaveNet prediction model, giving the surge prediction probability of each sample; and by adopting an LR-WaveNet prediction mode, giving the surge probability of data with noise points along with time, and testng the anti-interference performance of the model. According to the method, the time domain statistical characteristics and the change trend are integrated, the prediction precision is improved, and certain anti-interference performance is achieved; and the active control performance of the engine can be improved, and certain universality is achieved.

Description

technical field [0001] The invention relates to a method for predicting stall and surge of an axial flow compressor based on deep learning, and belongs to the technical field of aeroengine modeling and simulation. Background technique [0002] The aerodynamic stability of high-performance aero-engines mainly comes from the compressor. The working load capacity and stability of the compressor are crucial to the working efficiency and safety of the entire engine. The prediction of its instability has always been a research topic in the field of international aero-engines. Hotspots and Difficulties. Due to the high pressure ratio and fast acceleration of the high-pressure multi-stage axial flow compressor, the occurrence mechanism of the unsteady flow precursor is more complex and changes extremely rapidly. In addition, as the thrust-to-weight ratio of high-performance engines increases, the number of core engine stages decreases and the single-stage pressure ratio increases, ...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06F30/15G06N3/04G06N3/08
CPCG06F30/27G06F30/15G06N3/084G06N3/048G06N3/045Y02T90/00G06N3/08
Inventor 全福祥赵宏阳孙希明马艳华秦攀
Owner DALIAN UNIV OF TECH
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