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Predicting and optimizing the heat transfer performance of radiator with functionalized graphene nanofluids

Accurately predicting the heat transfer characteristics of coolants used in thermal management of energy systems like heat exchangers, power electronics, and heating, ventilation, and air conditioning is indispensable in maintaining its operating conditions within safety limits. Apart from safety, factors such as power consumption and operating cost are the most important constraints to be considered in designing an energy‐efficient and cost‐effective cooling solution. In this study, the experimental data available from previous research on the use of functionalized graphene‐based nanofluids in compact heat exchangers such as the automotive radiator is used to optimize the heat transfer performance parameters like Nusselt number of the nanofluid, the friction factor, and effectiveness of the heat exchanger. A supervised machine learning technique like the artificial neural network is used to obtain the objective functions of the response variables in terms of input features such as Reynolds number, Prandtl number, the volume concentration of nanoparticles in the base fluid, number of transfer units, heat capacity, the density of nanofluid, pressure drop and velocity. On the current dataset, it is found that by using the Bayesian regularization training algorithm and tangent sigmoidal activation function in the neural network, the best accuracies in the prediction can be achieved. Well‐known nature‐inspired optimization algorithms like genetic algorithms and simulated annealing are used in optimizing the above‐mentioned response variables. Both algorithms converged to the same values of the objective functions. The optimum values of Nusselt number, effectiveness, and friction factor are 105.65, 0.506, and 0.0038, respectively, for the given composition of the nanofluid and radiator configuration.