Greenhouse technology has advanced over the past few decades in terms of environmental control (e.g., indoor temperature, relative humidity, and CO₂ concentration). Ventilation is an effective way to adjust the indoor climate. Natural ventilation has gained significant research attention recently because of its low energy requirement. To evaluate the ventilation effectiveness, the ventilation rate is often used. This review summarizes the published review papers related to greenhouse ventilation. Ventilation models are reported under different conditions, including wind-induced, buoyancy-induced, and combined effects-induced ventilation in greenhouses. The influencing factors are described, such as the wind and buoyancy strength and distribution, greenhouse geometry, and vent arrangement. Various methods assessing natural ventilation in greenhouses are introduced, consisting of tracer gas techniques, the pressure difference method, the energy balance method, the emptying fluid-filling box method, and numerical simulation. The values of the key coefficients deduced and used in the literature are listed. This paper reports what has been done in the world and where we can start to develop dynamic ventilation models for solar and tunnel-type greenhouses in China. Further valuable investigations are discussed. The pressure distribution function in greenhouses with horizontal openings, a model for cross-ventilation induced by combined wind and buoyancy force, and an analytical plant-considered ventilation model with higher applicability are described. To ensure the accuracy of the ventilation models, other environmental variables, especially geography-dependent ones, can be added. More criteria are suggested to evaluate the ventilation performance rather than the ventilation rate to provide a comprehensive assessment.

The ventilation rate of a greenhouse is one of the major factors to consider when assessing its ventilation performance. Compared with plain areas, high-altitude areas have lower air pressure, thinner air, and stronger solar radiation, which in turn affect the magnitude of the local greenhouse ventilation rate. This paper is based on the use of online monitoring and computational fluid dynamics (CFD) techniques for modeling and model validation. The average relative error (ARE), mean absolute error (MAE), root-mean-square error (RMSE), and determination coefficient (R²) of the temperature were 4.88%, 1.396 °C, 1.428 °C, and 0.9982, respectively. The ARE, MAE, RMSE, and R² of the velocity were 9.525%, 0.035 m/s, 0.049 m/s, and 0.9869, respectively. Then, the distributions of the wind pressure, Reynolds number (Re), thermal pressure, air density, air speed, and temperature in greenhouses in high-altitude and plain areas were researched to obtain the relevant factors affecting the ventilation rates of greenhouses in high-altitude areas. In addition, correlation analyses were conducted for five variables affecting the ventilation rate: the inlet velocity, the temperature difference between the inside and outside of the greenhouse, the air density difference between the inside and outside of the greenhouse, total indoor radiation, and the internal heat source of the crop, and the coefficients of their correlations with the greenhouse ventilation rate were 1.0, −0.83, −0.72, −0.72, and 0.68, respectively. A natural ventilation rate model for plateau areas was developed, with the ARE, RMSE, and R² between the sample values and fitted values determined to be 4.55%, 0.543 m³/s, and 0.9997, respectively. The model was validated by predicting the greenhouse ventilation rate in winter (3 January 2022), and the ARE, RMSE, and R² of the sample values and predicted values were 9.726%, 8.435 m³/s, and 0.9901, respectively. This study provides a theoretical basis for further research on greenhouse ventilation characteristics in high-altitude areas.

The natural ventilation model plays a crucial role in greenhouse environmental control. It has been extensively studied by previous researchers, but it is limited to low-altitude areas. This study established a numerical model of single-span plastic greenhouses in high-altitude areas. The model was validated using measured data, showing a good agreement between the measured and simulated values. By setting boundary conditions based on on-site monitoring data, ventilation rates were extracted under different conditions for numerical simulations. Through nonlinear fitting, an empirical formula for natural ventilation rates, with a determination coefficient (R²) of 0.9724, was derived. The formula was validated through an energy balance analysis of indoor air. Different ventilation opening sizes were simulated to derive an empirical formula for natural ventilation rates based on opening size. Building on this, the relationship between plant height and ventilation rate was analyzed. As the dominant factors of natural ventilation change with environmental fluctuations, this study also proposed the threshold wind speed for wind pressure ventilation, thermal pressure ventilation, and coupled ventilation, filling the knowledge gap in relevant ventilation rate calculations. This is the first time that a natural ventilation model of single-span plastic greenhouses in high-altitude areas has been proposed, providing the basis in terms of modeling for the further development of local facility agriculture.

: With the rapid development of modern agriculture facilities, agricultural fans have been widely used due to their low pressure and large airflow characteristics. However, existing agricultural fans have large flow losses and low energy efficiencies. To increase the airflow and energy efficiency of these fans, optimization designs based on skewed and swept blades were carried out. First, a “DDZ” agricultural fan (a leaf model agricultural fan commonly used in China) was chosen as the archetype fan. Its performance curves and flow field distribution were obtained by performance testing and numerical simulation. Second, the stack lines of the skewed blade and swept blade were designed based on the original blade, 3 skewed blade parameters (skewed angle a, x direction control parameter kx, and y direction control parameter ky), and 3 swept blade design parameters (swept angle ß, z direction control parameter kz, and r direction control parameter kr). Finally, the optimal skewed blade design parameters (a = 16.8°, kx = 1.65, and ky = 0.5) and optimal swept blade design parameters (ß = 10.6°, kz = -0.33, and kr =0.6) were obtained using numerical simulations and orthogonal testing, which is a response surface method. The numerical simulation results showed that the airflow and energy efficiency ratios of the optimal skewed blade fan were increased by 4.3% and 20.5%, and those of the optimal swept blade fan were increased by 4.5% and 15.4%, respectively, in comparison with those of the original fan. The flow fields showed that the optimal skewed blade mainly reduced the radial flow at the blade root and the leakage flow. The optimal swept blade mainly reduced the leakage flow by changing the distribution of the static pressure on the blade surfaces. Keywords: Agricultural fan, Skewed-Swept blade, Numerical simulation, Optimization.

The milk yield of dairy cows in a non-stressed state in the cold region in China is lower during cold seasons. In this study, the correlations between indoor environmental factors and milk production were analyzed. Temperature, relative humidity, and light intensity were found to be the main factors affecting milk yield. The warning values of these factors for lower milk production were 5 °C, 60%, and 300 lx, respectively. A neural network model predicting milk yield based on environmental factors was established, and the optimal model parameters were determined, resulting in a high accuracy of R² = 0.802. This model was used to investigate the optimal measure for improving the indoor environment, which helps to increase milk production and economic benefits, including LED lights, heating radiators, and dehumidifiers. In conclusion, each type of device led to the growth of milk yield, reaching 2.341, 1.706, and 1.893 kg cow⁻¹ Day⁻¹. The combination of heating radiator and LED light resulted in the highest increased net benefit of 16.802 CNY cow⁻¹ Day⁻¹. This is the first time that a neural network model was successfully built to predict milk yield based on climatic features which was also applied to economic analysis of indoor environment improvement for dairy barns in extreme cold regions.