A seedling plant factory requires precise environmental control to ensure uniform growth within a limited cultivation period. A porous panel exhaust system was installed to maintain a stable and uniform internal environment. To provide optimal temperature, humidity, and airflow, it is necessary to interpret the internal aerodynamics. However, field monitoring has limitations in analyzing the invisible flow patterns. To overcome this limitation, CFD simulations can be utilized to understand internal environmental conditions and uniformity. The objective of this paper is to develop and validate a CFD model of a seedling plant factory with a porous panel for improving the uniformity of the internal environment. Multiple data loggers were evenly installed at various locations inside the seedling plant factory, and 24 h field monitoring was conducted. The average temperature and humidity during the 16 h light period and 8 h dark period were maintained within 1% of the set values, while the regional temperature deviation had an average of 1.65 °C and a maximum of 2.63 °C. The regional humidity deviation had an average of 14.1% and a maximum of 23.8%. The CFD model was designed to analyze the internal environmental uniformity after validation by comparing it with the field monitoring data. The Realizable k-ε turbulence model, which exhibited an error of 4.0% in comparison with the field data, was selected through a validation test among four different turbulence models with the same configuration of the seedling plant factory. The CFD simulation results were interpreted quantitatively and qualitatively, focusing on the airflow, temperature, and humidity distributions caused by the air conditioner and humidifier. Variations in the average temperature of up to 0.5 degrees and velocity differences of 0.28 m/s were observed depending on the location of the cultivation shelves. The locations and causes of stagnant regions resulting from the airflow patterns were identified through the simulations.

To address the challenges of climate change and food security, the establishment of smart farm complexes is necessary. While there have been numerous studies on the productivity and environmental control of individual greenhouses, research on greenhouse complexes is considerably limited. Conducting environmental studies during the design phase of these complexes poses financial constraints and practical limitations in terms of on-site experiments. To identify potential issues that may arise when developing large-scale greenhouse complexes, it is possible to utilize modeling techniques using computational fluid dynamics (CFD) to assess environmental concerns and location issues before constructing the facilities. Consequently, simulating large-scale CFD models that incorporate multiple greenhouses and atmospheric conditions simultaneously presents significant numerical challenges. The objective of this study was to design and verify the 3D CFD model for a large-scale Venlo greenhouse, where acquiring field data before construction is not feasible for designing a greenhouse complex. The verification of the CFD models was conducted using the improved grid independence test (GIT) and wall Y⁺ approaches. The findings revealed that a grid resolution of 0.8 m and a first-layer height of 0.04 m were suitable for developing large-scale greenhouse models, resulting in a low Root Mean Square Error (RMSE) of 3.9% and a high coefficient of determination (R²) of 0.968. This process led to a significant reduction of 38% in the number of grid cells. Subsequently, the aerodynamic characteristics and regional ventilation efficiency were analyzed in a 3D greenhouse model for developing a new large-scale greenhouse complex.

Diseases in pig houses not only hinder the growth and productivity of pigs but also result in significant economic losses for farmers due to high mortality rates. Although viral infections, including PRRS and PCV-2, are the primary causes, the likelihood of disease onset is closely linked to the pigs’ immune status, which is often compromised by environmental stressors. This study aimed to investigate the relationship between environmental conditions and pig mortality through detailed field monitoring in a commercial pig house with 600 growing pigs. The facility, which experienced a surge in mortality after a ventilation system change, was analyzed for various environmental parameters, including ammonia concentration (range: 7.0–10.7 ppm), dust levels (PM10: 106 µg/m³, PM2.5: 45 µg/m³), ventilation rates (0.49 AER, 67% of design capacity), air temperature (mean: 22.3 °C, range: 18.1–28.7 °C), and relative humidity (mean: 67.4%, range: 55.3–83.2%). Pig mortality and its spatial distribution were recorded, while viral infections were identified using RT-PCR, detecting pathogens such as PRRS, PCV-2, Mycoplasma hyopneumoniae, and Salmonella. Our findings revealed that although dust and ammonia concentrations remained within permissible limits, mortality was significantly correlated with thermal instability. Chronic respiratory diseases were observed in regions where ventilation was concentrated, resulting in daily temperature variations as high as 6.64 °C. The combination of improper ventilation and frequent temperature fluctuations weakened the pigs’ immunity, facilitating the onset of disease. This research underscores the critical role of maintaining stable microclimatic conditions in reducing mortality and highlights the need for advanced automated environmental control systems in smart livestock barns. The insights gained from this study provide a foundational framework for developing precision ventilation and thermal management strategies to enhance productivity and animal welfare.

In livestock houses, particulate matter (PM) is a critical factor not only for disease and odor spread but also for the work environment. In particular, workers are exposed to high concentrations of organic particulate matter and harmful gases while performing their tasks, and, as they age, they become more vulnerable to respiratory diseases. This study analyzed the PM concentrations in commercial broiler houses, focusing on the differences in ventilation rates according to the season and the type of work, categorized into a static work period (SWP) and dynamic work period (DWP). In the regional monitoring using gravimetric methods, the average PM concentrations were found to be within acceptable limits, with TSP at 1042 µg/m³, PM-10 at 718 µg/m³, and PM-2.5 at 137 µg/m³. These values did not exceed the recommended exposure limits for inhalable dust at 2400 µg/m³ and respirable dust at 160 µg/m³ in chicken farmers. However, in the personal monitoring using a real-time aerosol spectrometer, it was revealed that the aerosol concentrations during DWP exceeded the standards by up to 214%. Specifically, during DWP, the concentrations were 1.74 times higher for TSP, 1.40 times higher for PM-10, and 1.22 times higher for PM-2.5 compared to SWP. It was observed that during the movement of workers, the physical generation of particles around 10 µm, such as feed and bedding, occurred due to the movement of chickens, which influenced the aerosol concentration.

The seedling plant factory requires precise environmental control to ensure uniform growth within a short time cultivation period. To provide optimal temperature, humidity, and airflow, it is necessary to interpret the internal aerodynamics. However, the analysis based on field experiments has limitations in interpreting the invisible flow patterns. To overcome this limitation, CFD simulations were employed. The objective of this study was to develop and validate a CFD model of the seedling plant factory with the porous panel for improving the internal environment and to identify the fluid dynamics issues using the validated model. Based on the field monitoring data obtained by 90 data loggers, the average temperature and humidity during the 16-hour light period and 8-hour dark period were maintained within 1% of the set values. However, regional differences occurred, which led to the design of a CFD model incorporating the porous panel to simulate these variations. The Realizable k-ε turbulence model, which exhibited an error of 4.0% in comparison with the field data, was selected through validation test among four different turbulence models with the same configuration of the seedling plant factory. The CFD simulation results were interpreted quantitatively and qualitatively, focusing on the airflow, temperature, and humidity distributions caused by the air conditioner and humidifier. Variations in average temperature of up to 0.5 degrees and velocity differences of 0.28 m/s were observed depending on the location of the cultivation shelves. The locations and causes of stagnant regions resulting from the airflow patterns were identified through the simulations.