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

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...

AI Technical Summary

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 rotational stall of an axial flow compressor based on a stacked long short-term memory network (StackedLSTM, Stacked Long Short-Term Memory)

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