Lixin Zhang

In the process of boiling heat transfer, the microlayer is not only a crucial medium for enhancing heat transfer but also directly determines the heat flux distribution, dry zone expansion, and overall heat transfer efficiency through its morphological evolution and evaporation behavior. Building on this, this study employs the Lattice Boltzmann Method (LBM) with a single-component multiphase model to numerically simulate the evaporation process of microlayers on non-isothermal walls. The results show that, due to the uneven velocity distribution in the flow field, the microlayer exhibits significant contraction behavior during evaporation, particularly at the three-phase contact point, where velocity differences lead to fluid accumulation and the formation of a “cap-like” structure. The initial growth of the dry zone follows a linear trend, but its growth rate gradually decreases as the microlayer thickness increases, while near-wall density effects result in residual thickness within the dry zone. Additionally, the microlayer height first increases and then decreases over time, accompanied by a noticeable time lag. Heat flux analysis reveals that, during the formation of the dry spot, the lowest heat flux occurs at the three-phase contact point, followed by a sudden increase. A cold air ring forms above the dry zone, expanding and splitting as it moves with the dry spot. Higher temperatures promote microlayer evaporation, with the evaporation volume exhibiting nearly linear growth and the total fluid mass decreasing linearly.

An accurate and fast building load prediction model is critically important for guiding building energy system design, optimizing operational parameters, and balancing a power grid between energy supply and demand. A physics-based simulation tool is traditionally used to provide the building load demand; however, it is constrained by its complex model development process and requirement for engineering judgments. Machine learning algorithms (i.e., data-driven models) based on big data can bridge this gap. In this study, we used the massive energy data generated by a physics-based tool (EnergyPlus) to develop three data-driven models (i.e., LightGBM, random forest (RF), and long-short term memory (LSTM)) and compared their prediction performances. The physics-based models were developed using office prototype building models as baselines, and ranges were provided for selected key input parameters. Three different input feature dimensions (i.e., six-, nine-, and fifteen-input feature selections) were investigated, aiming to meet different demands for practical applications. We found that LightGBM significantly outperforms the RF and LSTM algorithms, not only with respect to prediction accuracy but also in regard to computation cost. The best prediction results show that the coefficient of variation of the root mean squared error (CVRMSE), squared correction coefficient (R²), and computation time are 5.25%, 0.9959, and 7.0 s for LightGBM, respectively, evidently better than the values for the algorithms based on RF (18.54%, 0.9482, and 44.6 s) and LSTM (22.06%, 0.9267, and 758.8 s). The findings demonstrate that a data-driven model is able to avoid the process of establishing a complicated physics-based model for predicting a building’s thermal load, with similar accuracy to that of a physics-based simulation tool.

A cooling tower is an important guarantee for the proper operation of a solar system. To ensure proper operation of the system and to maintain high-efficiency points, the cooling tower must operate year-round. However, freezing is a common problem that degrades the performance of cooling towers in winter. For example, the air inlet forms hanging ice, which clogs the air path, and the coil in closed cooling towers freezes and cracks, leading to water leakage in the internal circulation. This has become an intractable problem that affects the safety and performance of cooling systems in winter. To address this problem, three methods of freeze protection for cooling towers are studied: (a) the dry and wet mixing operation method—the method of selecting heat exchangers under dry operation at different environments and inlet water temperatures is presented. The numerical experiment shows that the dry and wet mixing operation method can effectively avoid ice hanging on the air inlet. (b) The engineering plastic capillary mats method—its freeze protection characteristics, thermal performance, and economics are studied, and the experiment result is that polyethylene (PE) can meet the demands of freeze protection. (c) The antifreeze fluid method—the cooling capacity of the closed cooling towers with different concentrations of glycol antifreeze fluid is numerically studied by analyzing the heat transfer coefficient ratio, the air volume ratio, the heat dissipation ratio, and the flow rate ratio. The addition of glycol will reduce the cooling capacity of the closed cooling tower.