Study on the Onset of Nucleate Boiling in Narrow Annular Channel by Using Wavelet and Wavelet Neural Network Available to Purchase

In this study, the local modulus maxima of cubic B-spline wavelet transform are introduced to determine the location of onset of nucleate boiling (ONB). Wavelet transformation has the ability of representing a function and revealing the properties of the function in the joint local regions of the time frequency space. Based on wavelet and artificial neural network, a Wavelet Neural Network (WNN) model predicting ONB for upward flow in vertical narrow annuli with bilateral heating has been developed. The WNN mode combining the properties of the wavelet transform and the advantages of Artificial Neural Networks (ANN) has some advantages of solving non-linear problem. The methods of establishing the model and training of wavelet neural network are discussed particularly in the article. The ONB prediction is investigated by WNN with distilled water flowing upward through narrow annular channels with 0.95 mm, 1.5 mm and 2.0mm gaps, respectively. The WNN prediction results have a good agreement with experimental data. At last, the main parametric trends of the ONB are analyzed by applying WNN. The influences of system pressure, mass flow velocity and wall superheat on ONB are obtained. Simulation and analysis results show that the network model can effectually predict ONB.