Abstract
Experimental data of thermal conductivity, thermal stability, specific heat capacity, viscosity, UV–vis (light transmittance) and FTIR (light absorption) of Multiwalled Carbon Nanotubes (MWCNTs) dispersed in glycols, alcohols and water with the addition of sodium dodecylbenzene sulfonate (SDBS) surfactant for 0.5 wt % concentration along a temperature range of 25 °C to 200 °C were verified using Artificial Neural Networks (ANNs). In this research, an ANN approach was proposed using experimental datasets to predict the relative thermophysical properties of the tested nanofluids in the available literature. Throughout the designed network, 65% and 25% of data points were comprehended in the training and testing set while the other 10% was utilized as a validation set. The parameters such as temperature, concentration, size and time were considered as inputs while the thermophysical properties were considered as outputs to develop ANN models of further predictions with unseen datasets. The results found to be satisfactory as the (coefficient of determination) R2 values are close to 1.0. The predicted results of the nanofluids’ thermophysical properties were then validated with experimental dataset values. The validation plots of all individual samples for all properties were graphically generated. A comparison study was conducted for the robustness of the proposed approach. This work may help to reduce the experimental time and cost in the future.
Highlights
In many industrial heating and cooling applications, convective heat transfer is very important.By changing the boundary conditions, flow geometry or fluids’ thermophysical properties can enhance the convective heat transfer rate of a thermal system
Purpose of absorption and transmission were investigated using in this research. The purpose of this this study is to evaluate the thermophysical properties of Multiwalled Carbon Nanotubes (MWCNTs) nanofluids with Artificial Neural Networks (ANNs)
During the development of the neural network (NN) model, the parameters such as diameter, material type (MWCNT), concentration, temperature and time were given as inputs whereas the properties of nanofluid were taken as outputs
Summary
By changing the boundary conditions, flow geometry or fluids’ thermophysical properties can enhance the convective heat transfer rate of a thermal system. The addition of nanoparticles to the base fluids is one of the promising ways of improving the thermophysical properties of fluids. Such kinds of suspensions are named as nanofluids. Nanofluids have attracted several researchers from all over the world because they have the ability to improve the heat transfer rate for different applications including electrical and electronics. The temperature and volume fraction were considered as important factors for developing the classical models of thermophysical properties especially thermal conductivity and viscosity [11,12,13,14].
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