A Rotating Stall Prediction Method of Axial Compressor Based on Stacked Long Short-Term Memory Network

Abstract

The invention provides a method for predicting the rotational stall of an axial flow compressor based on a stacked long-term and short-term memory network, which belongs to the technical field of aero-engine modeling and simulation. First, using a certain type of aero-engine surge experimental data, the data is selected and preprocessed, and the data is divided into training set and test set. Secondly, build a Stacked LSTM model and train it, use the final trained model to perform real-time prediction on the test set, and give the model loss and evaluation indicators. Finally, the StackedLSTM prediction model is used to predict the test data in real time, and the trend of surge probability over time is given in time sequence. The invention synthesizes the time-domain statistical features and changing trends, improves the prediction accuracy, is beneficial to improving the performance of the active control of the engine, and has certain universality.

Description

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

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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 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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