Sensitivity analysis and application of machine learning methods to predict the heat transfer performance of CNT/water nanofluid flows through coils

Nowadays, nanofluids are broadly utilized for various engineering and industrial systems including heat exchangers, power plants, air-conditioning, etc. The helically coiled tube heat exchangers are of the most interesting and efficient kinds of heat exchangers. The current study has focused on proposing model to predict Nusselt number by considering Prandtl number, volumetric concentration, and helical number of helically coiled heat exchanger as input variables. The investigated heat exchanger utilizes water carbon nanofluid. To propose an accurate model, a multilayer perceptron artificial neural network (MLP-ANN), adaptive neuro-fuzzy inference system (ANFIS), and least squares support vector machine (LSSVM) models are used. 72 experimental data are utilized as input data. Results indicate that LSSVM approach has the best performance and the proposed model by this approach has R-squared value equals to 1.