Bent pipe flow reconstruction based on improved ultrasound Doppler velocimetry and radial basis function neural network

Abstract

In piping systems, the flow through a bend is typically undeveloped, unsteady, and complicated. In this study, we proposed a novel method to accurately predict downstream flow in bent pipes based on radial basis function neural network (RBFNN) and ultrasound Doppler velocimetry (UDV). An improved UDV method was developed for the measurement of complex flow in bent pipes. The downstream flow characteristics of three different bend configurations were investigated using computational fluid dynamics (CFD). The proposed RBFNN models established the relationships between the velocity profile along the symmetric axis and the velocity distribution over the pipe cross-section. Numerical data were used for model training and validation, whereas the combined datasets with UDV data as inputs and the corresponding CFD data as outputs were used for model modification. The model prediction performance was then evaluated with new UDV inputs by comparing the predicted velocities with the associated CFD results. The results indicated that the velocity profiles obtained using modified UDV agreed well with the present numerical simulation. Finally, the trained model exhibited satisfactory flow reconstruction performance and high flowrate prediction accuracy, with a maximum error of approximately 3%. This work contributes to the application of the UDV method in complex flow measurements and further demonstrates that artificial neural networks (ANNs) are promising for modeling fluid mechanics.