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The mathematical model will be able to predict the operated condition (required tube diameters, heat input and submergence ratio….). That will result in a successful bubble pump design and hence a refrigeration unit. In the present work a one-dimensional two-fluid model of boiling mixing ammonia-water under constant heat flux is developed. The present model is used to predict the outlet liquid and vapor velocities and pumping ratio for different heat flux input to pump. The influence of operated conditions such as: ammonia fraction in inlet solution and tube diameter on the functioning of the bubble pump is presented and discussed. It was found that, the liquid velocity and pumping ratio increase with increasing heat flux, and then it decreases. Optimal heat flux depends namely on tube diameter variations. Vapour velocity increases linearly with increasing heat flux under designed conditions

In the present study, the ammonia-water mixing flow in a bubble pump is numerically simulated. The flow patterns of a two-phase flow in a bubble pump were studied under different conditions of heat flux and tube diameter. A one-dimensional two-fluid model was developed under constant heat flux. This model was used to predict the variations in void fraction and liquid and vapor velocities throughout the tube. Then, the void fraction profile and the curve of liquid velocity versus vapor velocity were used to predict the flow patterns along the tube length. It was found that at heat fluxes below 15 kW m−2, bubbly, slug, and churn flows are the dominating regimes, and the length of these flow regimes depends on the tube diameter. For heat fluxes higher than 15 kW m−2, the bubble pump operates under the churn and annular regimes, and the bubble pump performance is improved when the tube diameter increases.

a b s t r a c tAn experimental study has been performed to evaluate the single slope hybrid solar still integrated with heat pump (SSDHP). The purpose of this study is to determine the effectiveness of solar still and its modeling using artificial neural networks (ANNs) with the help of experimental data. Most influencing parameters (the solar radiation, glass cover temperature, basin temperature, water temperature and temperature of the evaporator) at an hour interval on the performance of hybrid solar still using ANNs model are discussed in this paper. Effect of an air compressor on the productivity of SSDHP and assess the sensitivity of the ANN predictions for different combinations of input parameters as well as to determine the minimum amount of inputs necessary to accurately model solar still a performance for the prediction of actual distiller output results. The experimental result SSDHP with air will give 100% higher yield as compared to the SSDHP without air but SSDHP dramatically maintains its lead by 25% at 9 h. While this duration maximum difference in yield of SSDHP with and without air observed that SSDHP with air gives 34.61% higher yield as compared to without air during 11 to 12 hour due to the influence of basin temperature. SSDHP with air was recorded 33.33% higher yield as compared to the SSDHP without air. For training, validation, test and all, value of R is equal to 0.99454, 0.99121, 0.99974 and 0.99374 respectively in ANNs proposed model which shows very good agreement with the experimental result. Satisfactory results for the SSDHP with air will pave the way to predict performance result for different climate regimes, with sufficient input data, the ANN method could be extended to predict the performance of other solar still designs also.

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