Prediction of Pressure Drop of Al2O3-Water Nanofluid in Flat Tubes Using CFD and Artificial Neural Networks

Abstract

In the present study, Computational Fluid Dynamics (CFD) techniques and Artificial Neural Networks (ANN) are used to predict the pressure drop value (Δp ) of Al2O3-water nanofluid in flat tubes. Δp is predicted taking into account five input variables: tube flattening (H), inlet volumetric flow rate (Qi ), wall heat flux (qnw ), nanoparticle volume fraction (Φ) and nanoparticle diameter (dp ). The required output data for training the ANN are taken from the results of numerical simulations. The numerical simulations of nanofluid are performed using two phase mixture model by FORTRAN programming language. The flow regime and the wall boundary conditions are assumed to be laminar and constant heat flux respectively. The ANN results are compared with the numerical simulated one and excellent agreement is observed. To view the accuracy of ANN model, statistical measures R2 , RMSE and MAPE are used and it is seen that the ANN model has high accuracy in predicting the (Δp ) values.

Keywords

• ANN
• GMDH
• Mixture model
• Nanofluid
• Pressure drop