Stacked long short-term memory network-based axial flow compressor rotation stall prediction method

A technology of axial flow compressor and long-term and short-term memory, applied in neural learning methods, biological neural network models, stochastic CAD, etc., can solve problems such as low accuracy and poor reliability, and achieve the effect of improving prediction stability and accuracy

Active Publication Date: 2021-12-24
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

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  • Stacked long short-term memory network-based axial flow compressor rotation stall prediction method
  • Stacked long short-term memory network-based axial flow compressor rotation stall prediction method
  • Stacked long short-term memory network-based axial flow compressor rotation stall prediction method

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Embodiment Construction

[0055] 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 rotational stall prediction method based on the stacked long-term and short-term memory network is as follows: figure 1 shown.

[0056] figure 2 It is a flow chart of data preprocessing, and the steps of data preprocessing are as follows:

[0057] S1. Preprocessing the aero-engine surge data.

[0058] S1.1. Acquire the experimental data of a certain type of aero-engine surge, and eliminate the invalid data due to sensor failure in the experimental data; there are 16 groups of experimental data, and each group of experiments includes 10 measurement points from normal to surge for a total of 10s The dynamic pressure value of the sensor measurement frequency is 6kHz, and the 10 measurement points are respectively located at:...

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Abstract

The invention provides an axial flow compressor rotation stall prediction method based on a stacked long short-term memory network, and belongs to the technical field of aero-engine modeling and simulation. The method comprises the following steps: firstly, selecting and preprocessing surge experiment data of a certain type of aero-engine, and dividing the data into a training set and a test set; secondly, building and training a Stacked LSTM model, carrying out the real-time prediction on a test set by utilizing the finally trained model, and giving model loss and evaluation indexes; finally, carrying out real-time prediction on the test data by adopting a Stacked LSTM prediction model, and giving a change trend of the surge probability along with time according to a time sequence. According to the method, the time domain statistical characteristics and the change trend are integrated, and the prediction precision is improved; and the active control performance of the engine can be improved, and certain universality is achieved.

Description

technical field [0001] The invention belongs to the technical field of aero-engine modeling and simulation, and relates to a method for predicting rotational stall of an axial flow compressor based on a stacked long-short-term memory network. Background technique [0002] The aero engine is known as the "heart" of the aircraft. Both military aircraft and civil aircraft with competitive advantages rely on high-performance aero engines. The compressor is an important part of the aero engine. It is important for the stability, reliability and safety of the aero engine. It plays a vital role, and rotating stall is a common failure of compressors. It is an unstable flow phenomenon and one of the systematic instability of engine internal flow, which will significantly reduce the performance of aeroengines, and it is generally believed that rotating stall It is a precursor to surge. Because it is extremely difficult to control the rotating stall, and the unstable state will cause s...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06N3/04G06N3/08G06F111/08
CPCG06F30/27G06N3/08G06F2111/08G06N3/048G06N3/044
Inventor 孙希明弓子勤全福祥李英顺
Owner DALIAN UNIV OF TECH
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