Rapid diagnosis method for rotating stall of axial flow compressor based on determined learning theory

A technology for determining learning theory and axial flow compressors, applied in biological models, electrical digital data processing, special data processing applications, etc., and can solve problems such as difficulty in satisfying continuous excitation conditions

Inactive Publication Date: 2010-11-17
SOUTH CHINA UNIV OF TECH
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  • Description
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  • Application Information

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Problems solved by technology

However, it is very difficult to satisfy the continuous excitation conditi

Method used

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  • Rapid diagnosis method for rotating stall of axial flow compressor based on determined learning theory
  • Rapid diagnosis method for rotating stall of axial flow compressor based on determined learning theory
  • Rapid diagnosis method for rotating stall of axial flow compressor based on determined learning theory

Examples

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[0047] Example

[0048] The schematic diagram of the overall structure of the aviation turbofan engine is shown in Figure (1). The air enters the compressor from the front air inlet of the engine through the guide vane cascade (where P T Represents the external atmospheric pressure), the compressor pressurizes the low-pressure gas in the combustion chamber to burn with fuel (such as gasoline) to form high-temperature and high-pressure gas, and the high-temperature and high-pressure gas is ejected from the tail nozzle to form a rebound force to provide the required power for the aircraft. Consider the following Mansoux model (finite-dimensional high-order dynamic system) for the rotating stall of an axial compressor:

[0049] E φ · = - Aφ + Ψ c ( φ ) - T ψ ‾ ψ ‾ · = 1 4 l c B 2 ( Sφ - Φ T ( ψ ) ) - - - ( 1 )

[0050] Where φ=[φ 1 φ 2 …Φ M ] T Is the flow sta...

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Abstract

The invention discloses a rapid diagnosis method for the rotating stall of an axial flow compressor based on a determined learning theory, which comprises the following steps of: establishing a literal radial basis function (RBF) neural network model of the rotating stall of the axial flow compressor; establishing the mode library (including modes from a pre stall, a stall initial stage to a complete stall) of a plurality of rotating stalls; establishing a dynamic estimator of the rotating stall by utilizing a literal RBF neural network; establishing the residual error of the state of the dynamic estimator and the flow state of a diagnosed compressor; and estimating the residual error, and realizing the rapid diagnosis for the rotating stall of the axial flow compressor. The method is applicable to the rapid diagnosis for the rotating stall of the axial flow compressor, can carry out learning identification and establish the mode library for the axial flow compressor in the whole process from a normal working condition (the pre stall) to a fault working condition (the complete stall), thereby rapidly identifying the working conditions (the pre stall, the stall initial stage and the complete stall) of the axial flow compressor in the operating process in real time to realize the rapid diagnosis for the rotating stall.

Description

[0001] technology neighborhood [0002] The invention relates to a rapid diagnosis method for the rotational stall of an axial flow compressor, in particular to a rapid diagnosis method for the rotational stall of an axial flow compressor based on deterministic learning theory. Background technique [0003] Axial flow compressor plays an important role in turbomachinery and is one of the core components of turbofan engine and turbojet engine. The rotating stall of the axial flow compressor will not only significantly reduce the performance of the engine, but also cause the engine's turbine and combustion chamber to overheat and damage the turbine blades and other disastrous consequences, such as when the aircraft is in certain flight conditions (climbing and large maneuvering flight, etc.) The occurrence of rotating stall can easily cause serious accidents; rotating stall may also cause compressor surge (rotating stall is generally regarded as the precursor of surge). Therefo...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N3/00
Inventor 王聪彭滔陈填锐袁汉文王勇
Owner SOUTH CHINA UNIV OF TECH
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