Identification method of flow pattern of dense-phase pneumatic conveying two-phase flow

Abstract

The invention discloses an identification method of the flow pattern of a dense-phase pneumatic conveying two-phase flow. The method includes steps: acquiring 4 different flow patterns of static signals of gas-solid two-phase flow at a horizontal pipe of a dense-phase pneumatic conveying pulverized coal system; performing empirical mode decomposition processing on the acquired 4 flow patterns of static signals, and obtaining a Hurst index H; dividing the static signals into a plurality of different scales according to the value and change of the Hurst index H; calculating different scale energy proportions of the static signals; drawing a multi-scale energy proportion distribution map of 4 flow patterns; and calculating the multi-scale energy proportion of an unknown flow pattern, referring to the distribution map, and realizing the identification of the flow pattern of the dense-phase pneumatic conveying two-phase flow. According to the method, the practicality is high, the signal decomposition method and the flowing nature of the dense-phase pneumatic conveying two-phase flow are combined, and the accuracy is high.

Description

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AI Technical Summary

Benefits of technology

This technology allows for better understanding of both solid phase and liquid phases flows by analyzing their electrical properties such as voltage or current levels during movement through them. It can identify specific types of fluid movements based on these measurements, making it useful in industries like transportation systems that require precise control over particle motion.

Problems solved by technology

This patented technical solution describes how different techniques may help identify or measure fluid properties accurately during transportation systems like pipelines. However, current approaches require significant effort due to factors including poorly understood particles' velocities, density differences caused by various physical processes involved, and complex interactions among these components. Therefore there needs improvement over existing solutions to better analyze and improve the accuracy of flow measurements and control behavior.

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