Flow profile identification with multipath transducers

Abstract

Accuracy of flow rate determination is very critical to an ultrasonic gas flowmeter, which is sensitive to the flow profile under measurement and cannot be obviously improved with traditional weighted integration methods for multipath transducers. Therefore, on one hand, more attentions are paid to intelligent learning algorithms (e.g. artificial neural networks) in recent studies to accurately construct the mapping relation between multipath velocities and the flow rate. However, the bottleneck that a trained network is only customized for a certain flow profile greatly restricts its application. On the other hand, many researchers turn to reconstruct the flow field but so far all flow visualization methods cost too much and are difficult to realize online. In this paper, an intelligent method of flow profile identification for multipath ultrasonic flowmeters is developed to solve the above predicament. Based on support vector machine, a multiclass identifier is constructed to automatically identify the flow profile to be measured from 65 typical flow patterns. Extremely high identification accuracy of 99.49% for test and 100% for training is achieved while the test result still reaches 88.46% even when a measurement uncertainty of ±2% is considered. A comparison with extreme leaning machine further reveals the identification performance and robustness of the proposed flow profile identifier. Different piping configurations, installation positions and angles can be therefore accurately identified, which indirectly realizes the flow visualization and can comprise an intelligent system of ultrasonic flow measurement when combined with the intelligent flow rate determination algorithms.