Interference recommendation for the pump sizing process in progressive cavity pumps using graph neural networks.

Pump sizing is the process of dimensional matching of an impeller and stator to provide a satisfactory performance test result and good service life during the operation of progressive cavity pumps. In this process, historical data analysis and dimensional monitoring are done manually, consuming a large number of man-hours and requiring a deep knowledge of progressive cavity pump behavior. This paper proposes the use of graph neural networks in the construction of a prototype to recommend interference during the pump sizing process in a progressive cavity pump.