Acoustic measurement of velocity filed using improved radial basic function neural network

Abstract

In coal-fired boilers, the aerodynamic field directly affects the combustion conditions of the boiler and safety of the furnace. In this work, a new method based on acoustic tomography (AT) is proposed to measure the velocity field by using radial basis function neural network (RBFNN). First, we establish the proposed RBFNN model to reconstruct the velocity field and study the influence of various parameters including the number and arrangement of sensors, the shape parameters and the number of center points in RBF, and the hyper parameters in the neural network to improve the reconstruction performance. Second, a novel improved RBFNN acoustic algorithm is used to reconstruct different two-dimensional velocity fields based on numerical simulations. The reconstruction results obtained using the proposed algorithms have higher accuracy, better real-time performance, and noise immunity as compared with other classical acoustic algorithms. Third, the physical experiments are conducted in lab to further assess the proposed algorithm. The results exhibit that the proposed RBFNN acoustic method provided satisfactory consistency as compared to the traditional measurement approaches. This proves the effectiveness of the proposed acoustic method in practical velocity field measurement.