Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2015, Vol. 26 ›› Issue (4): 040601 doi: 10.13538/j.1001-8042/nst.26.040601

• NUCLEAR ENERGY SCIENCE AND ENGINEERING • Previous Articles     Next Articles

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

Brahim Mohamedi,1, 2 Salah Hanini,2 Abdelrahmane Ararem,1  Nacim Mellel1   

  1. 1Birine Nuclear Research Center B.P.180, Ain Oussera 17200, Algérie
    2LBMPT Dr Yahia Farés University, Médéa 26000, Algérie
  • Contact: Brahim Mohamedi E-mail:mohammedi brahim@hotmail.com
  • Supported by:

    Supported by Algerian Atomic Energy Commission

PDF ShareIt Export Citation
Brahim Mohamedi, Salah Hanini, Abdelrahmane Ararem, Nacim Mellel. Simulation of nucleate boiling under ANSYS-FLUENT code by using RPI model coupling with artificial neural networks.Nuclear Science and Techniques, 2015, 26(4): 040601     doi: 10.13538/j.1001-8042/nst.26.040601

Abstract:

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 reactor. Indeed, 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 hydraulics. Contrary 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 interpolations. For that purpose, new approach is used to implement the thermophysical properties of water and steam for subcooled water in CFD solver ANSYS-FLUENT. The 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 vapor. Temperature 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 area. In 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.

Key words: User defined function (UDF), Computational fluid dynamics, IAPWS-IF97, ANSYS-FLUENT, Multilayer perceptron (MLP), Rensselaer polytechnical institute