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

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.

Particle dispersion modeling in ventilated room using artificial neural network

Due to insufficiency of a platform based on experimental results for numerical simulation validation using computational fluid dynamic method(CFD) for different geometries and conditions,in this paper we propose a modeling approach based on the artificial neural network(ANN) to describe spatial distribution of the particles concentration in an indoor environment.This study was performed for a stationary flow regime.The database used to build the ANN model was deducted from bibliography literature and composed by 261 points of experimental measurement.Multilayer perceptron-type neural network(MLP-ANN) model was developed to map the relation between the input variables and the outputs.Several training algorithms were tested to give a choice of the Fletcher conjugate gradient algorithm(TrainCgf).The predictive ability of the results determined by simulation of the ANN model was compared with the results simulated by the CFD approach.The developed neural network was beneficial and easy to predict the particle dispersion curves compared to CFD model.The average absolute error given by the ANN model does not reach 5%against 18%by the Lagrangian model and 28% by the Euler drift-flux model of the CFD approach.