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Flow boiling in micro pin fin arrays is an effective way to solve the heat dissipation problem. However, studies on the flow boiling performance of pin fin arrays with different structural parameters are still insufficient, especially for the application of long channels for practical evaporators in aircraft environmental control systems. In this paper, the flow boiling performance of R134a was experimentally investigated in a 300 mm long mini channel with micro pin fin arrays. The mass fluxes, heat fluxes, inlet saturation pressures and vapor qualities were in ranges of 200 -500 kg/(m2s), 15–30 kW/m2, 500–700 kPa and 0.05–0.90, respectively. The local heat transfer coefficient and pressure drop of staggered diamond pin fin arrays with different channel widths of 1.0, 1.2 and 1.4 mm were comprehensively studied. Subsequently, the performance analysis of the above channels was carried out. Results indicate that the nucleate boiling and convective boiling together govern the two-phase flow boiling heat transfer in the pin fin array. In particular, the increase in channel width delays the transition to convective boiling dominated flow. The local heat transfer coefficient increases with mass flux. However, in the high vapor quality region, the flow disturbance caused by a large mass flux may suppress the heat transfer, as high-speed gas destroys the continuous thin liquid film on the walls. Furthermore, the influence of saturation pressure on the heat transfer coefficient is reversed with the increase in vapor quality. Additionally, the performance analysis indicated better performance of the pin fin array at low vapor quality regions and small mass flux. Significantly, the best performance was obtained in pin fin array with a channel width of 1.4 mm, due to a drastic increase in two-phase pressure drop compared to heat transfer. Finally, a new correlation with the consideration of pin fin array structure parameters was proposed with an overall mean absolute deviation of 16.2%. This new correlation is proved to have an obvious accuracy improvement in predicting the flow boiling local heat transfer coefficient of R134a in a mini channel with pin fin arrays. The present work can provide critical information for the design optimization of a mini channel two-phase heat exchanger with pin fin arrays. [ABSTRACT FROM AUTHOR]

• An experimental dataset of flow boiling heat transfer in mini channels with pin fins was built. • A transfer learning framework was proposed to predict the heat transfer coefficient. • The effects of different factors on heat transfer coefficient prediction accuracy was identified. Flow boiling in mini channels with micro pin fins is a promising heat sink technique to achieve high-efficient aircraft thermal management. The accurate prediction of its heat transfer coefficient is critical for the practical design of two-phase heat exchanger based on mini channels with micro pin fins. Previous investigation shows that heat transfer coefficient prediction accuracy of the machine learning method is generally better than that of conventional empirical correlations. However, the machine learning method cannot guarantee its prediction accuracy among different data domains. To extend the application region of the conventional machine learning method, a transfer learning framework was proposed in present study. First of all, an experimental system was built to acquire test data from different sample domains (i.e., the diamond pin fins with different geometries). Then a conventional machine learning model was developed based on the deep learning method. Furthermore, the developed deep learning model was adjusted with transfer learning process, and the performance of these two kinds of models (i.e., conventional machine learning model and transfer learning model) was comprehensively evaluated. Results showed that the conventional machine learning model had a good prediction accuracy with an overall deviation of 4.11 % but was only confined in the same data domain as training data. Differently, the transfer learning model with 70 % data set from the new domain can achieve appropriate prediction in the new domains with deviation of 4.28 %. The results of this paper demonstrated that the conventional machine learning model can extend into different domains with reasonable prediction accuracy through transfer learning frameworks. It will benefit the practical design of high-efficiency heat exchangers for aircraft thermal management. [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]

The aircraft ice protection system that can guarantee flight safety consumes a part of the energy of the aircraft, which is necessary to be optimized. A study for the mechanism of the heat load reduction in the thermal anti-icing system under the evaporative mode was presented. Based on the relationship between the anti-icing heat load and the heating power distribution, an optimization method involved in the genetic algorithm was adopted to optimize the anti-icing heat load and obtain the optimal heating power distribution. An experiment carried out in an icing wind tunnel was conducted to validate the optimized results. The mechanism of the anti-icing heat load reduction was revealed by analyzing the influences of the key factors, such as the heating range, the surface temperature and the convective heat transfer coefficient. The results show that the reduction in the anti-icing heat load is actually the decrease in the convective heat load. In the evaporative mode, decreasing the heating range outside the water droplet impinging limit can reduce the convective heat load. Evaporating the runback water in the high-temperature region can lead to the less convective heat load. For the airfoil, the heating power distribution that has an opposite trend with the convective heat transfer coefficient can reduce the convective heat load. Thus, the optimal heating power distribution has such a trend that is low at the leading edge, high at the water droplet impinging limit and zero at the end of the protected area. [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]