Experimental study on the thermal and flow characteristics of ZnO/water nanofluid in mini-channels integrated with GA-optimized ANN prediction and CFD simulation

Abstract The thermal and flow characteristics of ZnO/water nanofluid in two multiport mini-channels were experimentally and numerically studied. Nanofluids with volumetric concentrations of 0.75 and 1.5% were used. Experimentally, the influences of concentration, Reynolds number (100–3750) and channel size (1.22 and 1.42 mm) on performance were identified. A novel Genetic Algorithm-optimized Backpropagation-Artificial Neural Network (GA-optimized BP-ANN) was proposed for Nusselt number prediction. Numerically, the performance using single-phase and mixture model with different turbulence models were evaluated. Results reveal that the nanofluids show better heat transfer performance and higher pressure drop than that of water. Additionally, the improvement is more obvious in laminar/turbulent transition region at a higher concentration. A better heat transfer performance is observed in a smaller channel after laminar flow region. For thermal performance factor, enhancement only appears at higher Reynolds numbers after flow transition. The most remarkable enhancements are nearly 1.3 and 1.48 for the two channels at the Reynolds numbers of 1600 and 1430, respectively. The developed GA-optimized BP-ANN shows an extremely high prediction accuracy with a Mean Absolute Relative Deviation (MARD) of 2.70%. Numerically, the single-phase model combined with the Lam-Bremhorst model exhibits better simulation results for nanofluid in mini-channel with a MARD of 9.0% than the mixture model.