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.
