Study on Post-Dryout Heat Transfer by Using Wavelet Neural Network

Author

Wei, Huiming ; Zhang, Xuan

Author_Institution

China Nucl. Power Simulation Technol. Co. Ltd., Shenzhen, China

fYear

2011

fDate

16-18 Dec. 2011

Firstpage

229

Lastpage

232

Abstract

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 the average post-dry out Nusselt number 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.

Keywords

computational fluid dynamics; confined flow; heat transfer; heating; mechanical engineering computing; neural nets; time-frequency analysis; wavelet transforms; artificial neural network; average post-dry out Nusselt number prediction; bilateral heating; nonlinear problem; post-dryout heat transfer; time frequency space; upward flow; vertical narrow annuli; wavelet neural network; wavelet transformation; Electron tubes; Heat transfer; Heating; Neural networks; Predictive models; Wavelet transforms; Narrow Annular Channel; Post-Dryout; Prediction; Wavelet Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovations in Bio-inspired Computing and Applications (IBICA), 2011 Second International Conference on

Conference_Location

Shenzhan

Print_ISBN

978-1-4577-1219-7

Type

conf

DOI

10.1109/IBICA.2011.60

Filename

6118603