Thermal Behavior of Magnetite Nanofluid under Magnetic Field: An Experimental Study and Development of Predictive Model to Predict Thermal Conductivity

摘要

Heat transfer characteristic of nanofluid has fascinated numerous researchers. Several parameters affecting it are being investigated. In this study, the thermal conductivity of low concentrated magnetite nanofluid under magnetic field is determined experimentally. The nanofluid is prepared using two-step method and its thermal conductivity is measured at different temperature (10 degrees C-70 degrees C), particle concentration (0.01%-0.1%) and magnetic field (0-700 G). Maximum rise of 24% in thermal conductivity is observed compared to water. Despite the fact that the magnetic field increases the thermal conductivity of the nanofluid, it is theoretically determined that the fluid's mobility is impeded. As a result, coming to a strong judgement is challenging. As experimental investigation to determine the thermal conductivity is time-consuming and cost-ineffective. This study also proposes a novel method based on Gaussian process regression to predict the thermal conductivity. While developing this model, various kernel functions are tested, and the Matern kernel function is found to be the best based on root mean square value and regression coefficient value. Another model is developed using Artificial neural network (ANN) model. While developing the ANN model different number of neurons in hidden layer has been tested along with different training function. Prediction is found to be best with six neurons in the hidden layer and Levenberg-Marquardt as the training function. The accuracy of the developed model may be used for the prediction of the thermal conductivity under different conditions which may reduce the experimental run cost.