Prediction of Nanofluid Heat Transfer Characteristic and Pressure Drop in Helical Coil Via Artificial Neural Networks

In this paper, experimental data of Nusselt number and pressure drop utilizing TiO2/water, ZnO/water, and Ag/water nanofluids in helical coils under isothermal conditions were used to design an artificial neural network to predict Nusselt number and pressure drop for experimental and predict data. Feed Forward Neural Network (FFNN) and Generalized Regression Neural Network (GRNN) were designed. All predicted results were compared with the original experimental data and the results showed that both networks could be used to predict Nusselt number and pressure drop. Root Mean Square Error (RMSE) was used to measure the accuracy of each neural network. GRNN showed more accuracy than FFNN. The maximum deviation between trained data and experimental data for FFNN and GRNN were ±15.5 % and ±0.02 % respectively. The maximum deviation between predicted data and experimental data for FFNN and GRNN were ±13.1 % and ±1.6 % respectively. Both the generated networks to predict non-experimental data showed good and logical behavior and prove that they are sufficient in Nusselt number and pressure drop prediction