Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Axial flow compressor stall surge prediction method based on deep learning

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

Active Publication Date: 2020-11-27
DALIAN UNIV OF TECH
View PDF4 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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 stall and surge of an axial compressor based on deep learning

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • 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

Examples

Experimental program
Comparison scheme
Effect test

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...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

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 the steps of: 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 the data dimension according to the input requirement of the LR-WaveNet prediction model; according to the time sequence, adopting an LR-WaveNet prediction model to give the surge predictionprobability of each sample; and adopting an LR-WaveNet prediction model to give the surge probability of data with noise points along with time, and testing 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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com