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This paper investigated the performance of a regenerative heat and mass exchanger (RHMX) for indirect evaporative cooling. A numerical model for RHMX was developed and validated with experimental data from the literature. Then, a data-driven model based on artificial neural network (ANN) algorithm was presented which was derived from the simulation results generated from the numerical model. The comparison between ANN prediction and experiment data showed good prediction accuracy. The average prediction error between the predicted and tested data was around 4% based on the air temperature change across the dry channel. With the data-driven model, parametric analyses were made to investigate the performance of the RHMX under different operating conditions. Finally, a design optimisation of the extraction air ratio was conducted under different ambient conditions. It was found that the optimal extraction air ratio decreased with the ambient temperature and/or relative humidity which ranged from 0.3 to 0.36. [ABSTRACT FROM AUTHOR]

The adoptions of mini-channel and nanofluid are potential technologies to improve the heat transfer capability. The present study experimentally investigated the flow and thermal performance of EG/water based ZnO nanofluid inside a mini-channel with serrated fins with an equivalent diameter of 1.59 mm. EG/water mixture with a mass fraction of 40%/60% served as the base fluid. Nanofluids with a volumetric concentration of 0.75 vol% and 1.5 vol% were prepared. The thermophysical properties of viscosity, density and thermal conductivity were measured. It reveals that although the adding of nanoparticles is able to improve the thermal conductivity by 4.2% on average, the viscosity also enlarges remarkably by up to 18.9%. The measured density and thermal conductivity can be predicted accurately by existing formulations. As all correlations underestimate the viscosity, a new one is proposed for the fabricated nanofluid with a small Mean Absolute Relative Deviation (MARD) of 0.1%. The Nusselt number can be averagely improved by 10.6% and 13.2% for the 0.75 vol% and 1.5 vol% nanofluid with a corresponding sacrifice of friction factor augmentation of 31.2% and 47.3% respectively due to the significant augmentation of viscosity, which results in a relatively small heat transfer coefficient enhancement ratio of 0.92–1.09 under the same pumping power. In addition, the heat transfer enhancement shows a nonlinear increment with the concentration and a higher relative enhancement is found at a lower concentration. This indirectly indicates that the heat transfer does not always increase with the concentration. Even though some published correlations present acceptable predictions for the tested flow and thermal performance of EG/water based ZnO nanofluid, the newly developed Backpropagation Artificial Neural Networks (BP-ANNs) show even better prediction accuracies for Nusselt number and friction factor with the MARDs of 0.39% and 0.35% respectively. Our study is expected to give valuable data sources and inspiration for future investigation on hydrodynamic and thermal behavior of nanofluids. • Flow and heat transfer behavior of ZnO-EG/water nanofluid were studied. • Thermal properties of nanofluid were measured and compared with correlations. • A new correlation for viscosity was proposed for ZnO-EG/water nanofluid. • Empirical Nusselt number correlations were evaluated by measured data. • Novel BP-ANNs were proposed to predict Nusselt number and friction. [ABSTRACT FROM AUTHOR]

• Both flow boiling and condensation in mini channels with segregated fins were investigated; • Machine learning based performance prediction models were developed; • The influential factors of different dimensionless parameters were analyzed. Two phase flow boiling and condensation heat transfer in mini channels are promising technologies in handling the challenges of modern thermal management in various fields. In this paper, experimental systems were developed to generate data for flow boiling and condensation of refrigerant R134a in two mini channels with serrated fins under various working conditions. Based on these data, machine learning based Artificial Neural Network (ANN) models were trained to predict the heat transfer performance for both flow boiling and condensation. The proposed ANN models predict the heat transfer coefficient satisfactorily with a Mean Absolute Relative Deviation (MARD) of 11.41% and 6.06% for boiling and condensation respectively compared with experimental data. The test results also indicate that using the most important parameters rather than all available parameters is able to formulate a reasonable ANN model. It also reveals that the developed ANN model can be transferred in a different dataset from the same domains but shows an unacceptable performance for different domains. The proposed ANN model can illuminate future study on two phase flow and be used in practical heat exchanger design, which will benefit the advanced thermal management system. [ABSTRACT FROM AUTHOR]

• Local condensation heat transfer in smooth channels and pin fin array is presented. • The effect of operating parameters on the heat transfer characteristics was analyzed. • Sensitivity analysis is performed of heat transfer to identify the critical parameters. • Condensation heat transfer increases with heat flux in the range of vapor quality above 0.4. In this paper, an experimental study of flow condensation was carried out of R134a in a multiport mini channel and a mini channel with pin fin array. The former comprised 20 parallel rectangular channels with an equivalent diameter of 0.64 mm. The latter is a narrow rectangular mini channel containing 10 rows of diamond pin fins with a staggered configuration, and height and longitudinal/transverse pitch of 0.5 mm and 2 mm respectively. To acquire the local value of heat flux and heat transfer coefficient, air jet impingement cooling devices were adopted to condense the vapor of refrigerants. The effects of vapor quality, mass flux, heat flux, and saturation pressure on flow condensation heat transfer coefficient were investigated with the operating conditions: vapor quality from 1 to 0, mass flux from 160 to 450 kg/(m2s), heat flux from 10.0 to 39.8 kW/m2, and saturation pressure from 600 to 1500 kPa. The experimental results indicated that the pin fin array significantly improves the condensation heat transfer coefficient up to nearly 186 % compared to the multiport mini channel in the high vapor quality region. For both channels, the local heat transfer coefficient increases with an increase in vapor quality, mass flux, and heat flux whereas decreases with increase in saturation pressure. The influence of heat flux and mass flux on heat transfer coefficient was more pronounced in the high vapor quality region than in the low vapor quality region. A sensitivity analysis was performed at different vapor quality levels. The most influential parameters in the smooth channel and the pin fin array are saturation pressure and mass flux, respectively. The relative contribution of heat flux is more obvious in the high vapor quality region. Some of the existing correlations have good prediction performance for the smooth channel experimental data in this paper, and the corresponding MAD is within 20 %. However, their deviations are much greater for the applications of pin fin array. [ABSTRACT FROM AUTHOR]