Method for identifying flow type of soft grain two-phase turbulence based on artificial intelligence

A flow pattern recognition and artificial intelligence technology, applied in neural learning methods, flow characteristics, biological neural network models, etc., can solve the problems of poor applicability, low reliability, low accuracy, etc., to improve efficiency and accuracy, The effect of reducing costs and reducing subjectivity factors

Active Publication Date: 2011-03-09
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0005] In order to overcome the shortcomings of the existing soft abrasive particle two-phase turbulent flow pattern identification method, which have low accuracy, poor applicability, low reliability, and inability to meet the requirements of online identification, the present invention

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  • Method for identifying flow type of soft grain two-phase turbulence based on artificial intelligence
  • Method for identifying flow type of soft grain two-phase turbulence based on artificial intelligence
  • Method for identifying flow type of soft grain two-phase turbulence based on artificial intelligence

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[0032] In conjunction with the accompanying drawings, the present invention will be described in detail below.

[0033] refer to Figure 1~Figure 6 , an artificial intelligence-based method for identifying two-phase turbulent flow patterns of soft abrasive particles. By using the simulation technology that combines the Euler model and the renormalization group (RNG) two-equation model, it can obtain the two-phase turbulent flow of soft abrasive particles. The objective mathematical description of various flow pattern characteristics and the change law of characteristic parameters in the process of flow pattern transformation, according to the quantitative analysis of wavelet packets, the eigenvectors formed by the obtained parameters are used to input probabilistic neural network (PNN) for training and identification, and realize turbulent flow. Type objective identification and classification. It generally includes the acquisition of soft abrasive two-phase turbulent pressur...

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Abstract

The invention relates to a method for identifying the flow type of soft grain two-phase turbulence based on artificial intelligence, comprising the following steps: 1), collecting the pressure signals of the soft grain two-phase turbulence; 2), extracting and analyzing the pressure signal characteristics: carrying out multi-level division on a frequency band by using a small wave packet method, further decomposing a high-frequency part without dispersed multiple resolution ratios, adaptively selecting corresponding frequency bands according to the characteristics of the analyzed signals so as to match the corresponding frequency bands with signal frequency spectrums; and 3), training and identifying flow type samples composed of defined characteristic parameters by using a probabilistic neural network, wherein learning samples are characteristic vectors of information entropy of a small wave packet after normalization according to a relationship between a probabilistic neural network structure and the learning samples, determining the structure of the probabilistic neural network for flow type identification as well as carrying out relative set on the network and learning training, and identifying the samples with different flow types by using the probabilistic neural network. The method has high veracity, good applicability and high reliability, and can effectively meet on-line identification requirements.

Description

technical field [0001] The invention relates to a two-phase flow flow pattern recognition method, more specifically, an artificial intelligence-based soft abrasive particle two-phase turbulent flow pattern recognition method. Background technique [0002] The flow pattern (characterizing the flow form of the fluid) can be divided into laminar flow, turbulent flow and transition flow in between. It represents the trajectory and velocity distribution of fluid particle motion. The laminar flow pattern in the circular tube is a parabolic velocity distribution; the velocity distribution of the turbulent flow pattern obeys the Karman-Prand 1 / 7 exponential law. The two-phase flow pattern also characterizes the phase interface distribution of the two-phase flow medium. At present, two-phase flow conditions are involved in many production equipment in power, chemical, nuclear energy, refrigeration, petroleum and metallurgy industries, and even have applications in the precision mac...

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Application Information

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IPC IPC(8): G01N11/00G06N3/08
Inventor 计时鸣王迎春谭大鹏张利袁巧玲章定钟佳奇兰信鸿
Owner ZHEJIANG UNIV OF TECH
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