Heat transfer evaluation of combined nanofluids in vertical annulus: An experimental and artificial neural network approach

Abstract

Enhancing the thermal properties of the working fluid is a practical way to improve heat transfer performance. One of the suggested fluids for this purpose is nanofluids, which are suspensions of nanoparticles within a base fluid. Nanofluids have the potential to operate as a primary coolant or as an emergency coolant in a nuclear reactor. This paper investigates the thermal performance of a homogeneous hybrid nanofluid of Al2O3 and TiO2 in deionized water. The thermo-hydraulic performance of the nanofluid is analyzed in a high-pressure test loop capable of 25 bar pressure. The test section has an annular geometry, generating a cosine heat flux pattern. This flux pattern is representative of nuclear reactor cores. The experimental study is conducted to investigate the effects of nanoparticle concentration and Reynolds number on the heat transfer performance of nanofluids. The results show that increasing these two parameters significantly improved heat transfer. Specifically, raising the nanoparticle concentration from 0% to 1% leads to a reduction in surface temperature of 19% and 8% at Reynolds numbers of 825 and 4128, respectively. The calculated Nusselt numbers are compared with correlations from previous studies. Furthermore, an artificial neural network model is developed to predict heat transfer data for the studied system. The model consists of a single-layer feed-forward neural network trained with the stochastic gradient descent algorithm. To determine the optimal network, the number of neurons is optimized and evaluated based on the network's predictability. The neural network model demonstrates substantial improvements over previous experimental correlations (MAPE <2.5% and MSE <0.001).