The effects of different solar radiation intensities, heat flow density of indoor heat sources, outdoor wind speed, and the relative location of indoor heat sources on the natural ventilation performance of solar chimneys are investigated through three-dimensional numerical simulations. The mechanism of the mutual coupling of the solar chimney effect with the outdoor wind and indoor heat source heat plume is explored. The results of the study show that when the structural parameters of the solar chimney are the same, the heat flow density on the surface of the indoor heat source, the outdoor wind speed and the solar radiation intensity all have a gaining effect on the ventilation performance of the solar chimney and the effects of the three on the ventilation of the solar chimney promote each other, when the solar radiation intensity is 200 W/m², the outdoor wind speed is 1.0 m/s, and the indoor heat source heat flow density increases from 0 to 1 500 W/m², the solar chimney ventilation volume increases from 0.393 m³/s to 0.519 m³/s, the maximum value of the increase is 32.1%. In the other two cases, the maximum increase in solar chimney ventilation is 176.7% and 33.1%, respectively. Under the same conditions, solar chimney ventilation is optimal when the heat source is in the middle of the room. The presence of outdoor wind, however, affects the optimum design parameters of the solar chimney. Compared to the case where no outdoor wind is taken into account, the optimum inlet width of 0.2–0.3 m for the solar chimney no longer applies with outdoor wind, with the optimum value rising to 0.5 m.

In this work, the superhydrophobic surfaces with micro-nano composite structure was successfully prepared by one-step electrodeposition based on Ca-myristic acid complex onto Cu substrate. The performance of delaying frost growth on micro-nanostructure superhydrophobic copper surfaces was explored, and the application of superhydrophobic materials in organic rankine cycle (ORC) was simulated. The experimental results confirmed that the superhydrophobic surfaces increased the nucleation barrier of the condensation droplets, enhanced the heat transfer resistance between the condensation droplets and the cold surface, and effectively restrained frost growth. The simulation study of superhydrophobic materials in organic rankine cycle (SH-ORC) system and ORC system was carried out with Matlab software. It was proved that the net power output and exergic efficiency of SH-ORC system were significantly increased compared with that of ORC system. When the heat source temperature was 180ºC, the net output power of SH-ORC was 15.07% higher than that of ORC, and the exergy efficiency was greater than 14%. The simulation results showed that the most suitable heat source temperature for SH-ORC was 180ºC. Therefore, the superhydrophobic copper surfaces can be potentially used to minimize frost formation in harsh environment.

The organic flash cycle (OFC) is a novel power cycle with small exergy loss in the endothermic process. However, the low-pressure throttle valve in the cycle has a large throttling loss. Aiming to reduce the cycle exergy loss and improve the system performance, this study constructs a new configuration named the organic flash Rankine cycle (OFRC). Using the R600/R245fa mixture as the circulating working fluid and 200 °C geothermal water as the heat source, the effects of the change in working fluid composition on the thermal properties of the OFRC were studied based on the first and second laws of thermodynamics. Then, the economic performance of the proposed OFRC was investigated and then compared with that of the conventional OFC. The results show that the OFRC system has a significant improvement in thermal performance and economy compared with the OFC system. When the mole composition of the R600/R245fa mixture is 0.5/0.5, the net output work, thermal efficiency, and exergy efficiency of the OFRC system can reach a maximum at 146.39 kW, 21.51%, and 80.94%, respectively, which are 98.2 kW, 14.43%, and 54.3% higher than those of the OFC system. The dual heaters in the OFRC system can effectively reduce loss in the endothermic process. When the R600 mole composition is 0.5 in the OFRC system, the exergy loss of the heater is only 7.42%, and the power generation cost (0.3267 $·kW⁻¹·h⁻¹ only) is lower than that in the OFC system.

The low thermal conductivities of phase-change materials (PCMs) remain a major obstacle to their efficient application in thermal energy storage. Nano‑enhanced phase-change materials (NEPCMs) present a promising alternative; therefore, accurate prediction of their thermal conductivities is of great importance. In this work, six machine learning (ML) models were developed to predict the effective thermal conductivities of NEPCMs: multilayer perceptron neural network (MLPNN), radial basis function neural network, least squares support vector machine, decision tree, k‑nearest neighbors, and dendrite morphological neural network models. A dataset comprising 324 data samples with six input parameters, the temperature, nanoparticle concentration, nanoparticle size, nanoparticle thermal conductivity, PCM thermal conductivity, and PCM phase state, was employed. Based on statistical and graphical approaches, the performances of the ML models and five classical empirical correlations were evaluated and compared. Physical trend analysis was conducted to further verify the ML model performances. Among the models compared in this study, the MLPNN model achieved the best overall performance on the compiled dataset (coefficient of determination ( R ² ) = 0.9924, root mean square error = 0.0476, and mean absolute percentage error = 3.10%), performs better than the other ML models and classical empirical correlations under the conditions examined. The MLPNN successfully captured the expected thermal conductivity variations with respect to each input parameter and performed adequately across most input ranges, with only slight increases in the error for high concentrations, extreme temperatures, and specific PCM types. This work provides a systematic benchmark for the selection of appropriate ML models and demonstrates the superiority of data‑driven approaches in predicting the thermal conductivities of NEPCMs.