(untitled) (Journal of Naval Architecture and Marine Engineering, 2015)
Heat transfer due to forced convection of copper water based nanofluid in the presence of buoyancy has been predicted by the Artificial Neural network (ANN) Gene Expression Programming (GEP). The present nanofluid is formed by mixing copper nano particles in water and the volume fractions are considered here are 0% to 15% and the Reynolds number are varying from 80 to 180. The buoyancy effect is done by introducing Richardson number (Ri) as 1 and -1. The back propagation algorithm is used to train the network. The present ANN and GEP models are trained by the input and output data which has been obtained from the numerical simulation, performed in finite volume based Computational Fluid Dynamics (CFD) commercial software Fluent. The numerical simulation based results are compared with the back propagation based ANN and GEP results. It is found that the mixed convection heat transfer of water based nanofluid can be predicted correctly by both ANN and GEP but GEP is found more efficient. It is also observed that the back propagation ANN and GEP both can predict the heat transfer characteristics of nanofluid very quickly compared to standard CFD method.