The insight flow characteristics of concentrated MWCNT in water base fluid: experimental study and ANN modelling

CNT based nanofluids have great potential in the field of heat transfer due to their higher thermal conductivity compared to other categories of nanofluids. However, their applicability to different flow conditions is unknown. The flow behaviour of MWCNT/water nanofluids was investigated in this study under a variety of conditions, including concentration, temperature, and shear stress (0–35 Pa). Non-Newtonian flow properties of prepared samples have been found by experiments. MWCNT/Water nanofluids have shown that flow behaviour is strongly influenced by concentration. This contrasting rheological activity of MWCNT/water nanofluid at various concentrations was also attributed to SDS surfactant. The concept of molecular association of MWCNT and SDS molecules over the various structures formed by MWCNT at different concentrations and shear conditions is used to describe the insight flow characteristics of MWCNT. Power-law model-based curve fitting was used to study the variations in flow behaviour of MWCNT/water nanofluid. On the basis of qualitative results, this model was found to be the best-fitting model. Furthermore, an optimal Artificial Neural Network (ANN) was used to predict the complex viscosity of MWCNT/water nanofluid over flow behaviour variation, which is difficult to predict using traditional models. The influence of different parameters such as the weight percent concentration of nanofluid, temperature, shear time, and shear stress are all taken into account in this model. The model was trained on a dataset from current research and demonstrated outstanding accuracy in predicting viscosity (for the testing data, obtained R2 and RMSE are 0.9993 and 0.0035).