• Energy Research

The boiling heat transfer performance of porous surfaces greatly depends on the morphological parameters, liquid thermophysical properties, and pool boiling conditions. Hence, to develop a predictive model valid for diverse working fluids, it is necessary to incorporate the effects of the most influential parameters into the architecture of the model. In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. The optimized model is then employed to perform sensitivity analysis for finding the most influential parameters in the boiling heat transfer assessment of sintered coated porous surfaces on copper substrate subjected to a variety of high- and low-wetting working fluids, including water, dielectric fluids, and refrigerants, under saturated pool boiling conditions and different surface inclination angles of the heater surface. The model with all the surface morphological features, liquid thermophysical properties, and pool boiling testing parameters demonstrates the highest correlation coefficient, R2 = 0.985, for HTC prediction. The superheated wall is noted to have the maximum effect on the predictive accuracy of the boiling heat transfer coefficient. For example, if the wall superheat is dropped from the modeling parameters, the lowest prediction of R2 (0.893) is achieved. The surface morphological features show relatively less influence compared to the liquid thermophysical properties. The proposed methodology is effective in determining the highly influencing surface and liquid parameters for the boiling heat transfer assessment of porous surfaces.

Dye solution temperature influences the elimination efficiency of water-soluble and anionic acid dye. Acid Blue 25 dye, using a gas–liquid electrical discharge system, was successfully investigated. The results showed an increase in the percentage of dye decolorization from 91.16% to 96.12% when the dye solution temperature was increased from 278 K to 308 K. However, the initial dye decolorization percentage was decreased with the further increase in dye solution temperature from 318 K to 358 K. The 2D simulation model was introduced to consider the influence of temperature and the electric field generated by corona discharge plasma in air and water. Results also showed a great match between the experimental and the simulation results. The reaction rates of dye degradation were analyzed using the Arrhenius equation. Furthermore, pseudo-zero-, pseudo-first-, and pseudo-second-order models were used to determine the reaction kinetics. The best fit for the experimental data would follow the pseudo-first-order model. Finally, electrical energy per order, energy yield, and experimental degradation data were calculated to investigate the cost analysis.

The optimization of outdoor thermal comfort has become the keystone to guarantee the healthy and comfortable use of outdoor spaces. This study aims to optimize the outdoor thermal comfort through vegetation parameterization in a boulevard located in Guelma city, Algeria during summertime. However, two main parameters were investigated, species and tree layout, through a numerical simulation. We first collected microclimate data of a sunny summer day. Second, we used real microclimate data in different simulations using the Envi-met atmospheric model. The findings reveal that Ficus Nitida is the most significant species to intercept solar radiation and provide shade over the day in Souidani Boudjemaa Boulevard, with a maximum reduction of Ta = 0.3 °C and UTCI = 2.6 °C at 13:00 p.m. Tree layout is a determining parameter in the creation of shaded paths, based on the quality of the shadows cast by the trees, namely, their size. Thereby, planting the washingtonia palm trees along the center of the boulevard is the best option to maximize the shaded area within the boulevard, with maximum reduction of Ta = 1.8 °C and UTCI = 3.5 °C at 16:00 p.m.

Modification of a cell’s architecture can enhance the performance parameters. This paper reports on the numerical modeling of a thin-film organic solar cell (OSC) featuring distributed Bragg reflector (DBR) pairs. The utilization of DBR pairs via the proposed method was found to be beneficial in terms of increasing the performance parameters. The extracted results showed that using DBR pairs helps capture the reflected light back into the active region by improving the photovoltaic parameters as compared to the structure without DBR pairs. Moreover, implementing three DBR pairs resulted in the best enhancement gain of 1.076% in power conversion efficiency. The measured results under a global AM of 1.5G were as follows: open circuit voltage (Voc) = 0.839 V; short circuit current density (Jsc) = 10.98 mA/cm2; fill factor (FF) = 78.39%; efficiency (η) = 11.02%. In addition, a thermal stability analysis of the proposed design was performed and we observed that high temperature resulted in a decrease in η from 11.02 to 10.70%. Our demonstrated design may provide a pathway for the practical application of OSCs.