Simulation of nucleate boiling under ANSYS-FLUENT code by using RPI model coupling with artificial neural networks

作     者：Brahim Mohamedi Salah Hanini Abdelrahmane Ararem Nacim Mellel 

作者机构：Birine Nuclear Research Center B.P.180Ain Oussera 17200 Algrie LBMPT Dr Yahia Fars University Birine Nuclear Research Center B.P.180Ain Oussera 17200 Algri 

出 版 物：《Nuclear Science and Techniques》 (核技术（英文）)

年 卷 期：2015年第26卷第4期

页      面：95-101页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 082701[工学-核能科学与工程] 0827[工学-核科学与技术] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by Algerian Atomic Energy Commission 

主　　题：人工神经网络 神经网络模拟 耦合模型 过冷沸腾 RPI 代码 IAPWS-IF97 CFD软件 

摘      要：The present study is to develop a new user-defined function using artificial neural networks intent Computational Fluid Dynamics(CFD)simulation for the prediction of water-vapor multiphase flows through fuel assemblies of nuclear ***,the provision of accurate material data especially for water and steam over a wider range of temperatures and pressures is an essential requirement for conducting CFD simulations in nuclear engineering thermal *** to the commercial CFD solver ANSYS-CFX,where the industrial standard IAPWS-IF97(International Association for the Properties of Water and Steam-Industrial Formulation 1997)is implemented in the ANSYS-CFX internal material database,the solver ANSYS-FLUENT provides only the possibility to use equation of state(EOS),like ideal gas law,Redlich-Kwong EOS and piecewise polynomial *** that purpose,new approach is used to implement the thermophysical properties of water and steam for subcooled water in CFD solver *** technique is based on artificial neural networks of multi-layer type to accurately predict 10 thermodynamic and transport properties of the density,specific heat,dynamic viscosity,thermal conductivity and speed of sound on saturated liquid and saturated *** is used as single input parameter,the maximum absolute error predicted by the artificial neural networks ANNs,was around 3%.Thus,the numerical investigation under CFD solver ANSYSFLUENT becomes competitive with other CFD codes of which ANSYS-CFX in this *** fact,the coupling of the Rensselaer Polytechnical Institute(RPI)wall boiling model and the developed Neural-UDF(User Defined Function)was found to be useful in predicting the vapor volume fraction in subcooled boiling flow.