Investigation of pool boiling of nanofluids using artificial neural networks and correlation development techniques

Abstract

The nucleate pool boiling heat transfer characteristics of TiO2 nanofluids are investigated to determine the important parameters' effects on the heat transfer coefficient and also to have reliable empirical correlations based on the neural network analysis. Nanofluids with various concentrations of 0.0001, 0.0005, 0.005, and 0.01vol.% are employed. The horizontal circular test plate, made from copper with different roughness values of 0.2, 2.5 and 4μm, is used as a heating surface. The artificial neural network (ANN) training sets have the experimental data of nucleate pool boiling tests, including temperature differences between the temperatures of the average heater surface and the liquid saturation from 5.8 to 25.21K, heat fluxes from 28.14 to 948.03kWm−2. The pool boiling heat transfer coefficient is calculated using the measured results such as current, voltage, and temperatures from the experiments. Input of the ANNs are the 8 numbers of dimensional and dimensionless values of the test section, such as thermal conductivity, particle size, physical properties of the fluid, surface roughness, concentration rate of nanoparticles and wall superheating, while the outputs of the ANNs are the heat flux and experimental pool boiling heat transfer coefficient from the analysis. The nucleate pool boiling heat transfer characteristics of TiO2 nanofluids are modeled to decide the best approach, using several ANN methods such as multi-layer perceptron (MLP), generalized regression neural network (GRNN) and radial basis networks (RBF). Elimination process of the ANN methods is performed together with the copper and aluminum test sections by means of a 4-fold cross validation algorithm. The ANNs performances are measured by mean relative error criteria with the use of unknown test sets. The performance of the method of MLP with 10-20-1 architecture, GRNN with the spread coefficient 0.7 and RBFs with the spread coefficient of 1000 and a hidden layer neuron number of 80 are found to be in good agreement, predicting the experimental pool boiling heat transfer coefficient with deviations within the range of ±5% for all tested conditions. Dependency of output of the ANNs from input values is investigated and new ANN based heat transfer coefficient correlations are developed, taking into account the input parameters of ANNs in the paper.