Thermal conductivity prediction of titania-water nanofluid: A case study using different machine learning algorithms

In this study, the thermal conductivity of titania (TiO2)-water nanofluid was predicted using five separate machine learning algorithms with their unique hyperparameters and logical functions. A detailed comparative study on the TiO2-water nanofluid dataset was performed using the artificial neural network (ANN), gradient boosting regression (GBR), support vector regression (SVR), decision tree regression (DTR), and random forest regression (RFR) algorithms. A complete data intelligence analysis of 228 collected data points on different shape and size of titania (TiO2)-water nanofluid was conducted. The dataset consisted of five parameters including size, shape, volume fraction of the nanoparticles, temperature, and thermal conductivity. The shape of the nanoparticles was considered to be altogether a new parameter for predicting the thermal conductivity of the nanofluids. The mean square error (MSE) and R-square (R-2) were used to compare these models. From this comparative study of these algorithms, it was found that the gradient boosting is the best algorithm [R-train(2) = 0.99, R-test(2) = 0.99, MSE.(train) = 0.0003 and MSE.(test) = 0.0002] for thermal conductivity predictions with test and train accuracies of 99%. It was observed that, the shape of the nanoparticles could influence the thermal conductivity predictions of the nanofluids, substantially.

Automated summary

In this study, the thermal conductivity of titania-water nanofluid was predicted using five different machine learning algorithms. The shape of the nanoparticles was found to significantly influence the thermal conductivity predictions. Gradient boosting was determined to be the best algorithm for predicting thermal conductivity.