Application of computational fluid dynamics (CFD) for optimal temperature sensor placement in a greenhouse equipped with a pad-fan cooling (PFC) system
The greenhouse microclimate is influenced by several external parameters, including ambient temperature, relative humidity, solar radiation intensity, wind speed, and the type of cultivated crops. Attaining optimal environmental control within greenhouse necessitates the precise placement of sensors...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525003429 |
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| Summary: | The greenhouse microclimate is influenced by several external parameters, including ambient temperature, relative humidity, solar radiation intensity, wind speed, and the type of cultivated crops. Attaining optimal environmental control within greenhouse necessitates the precise placement of sensors to monitor these key parameters. However, sensor placement is frequently guided by the empirical knowledge and experience of greenhouse owners and designers rather than systematic methodologies. The primary objective of this study is to identify the optimal location for temperature sensor placement using Computational Fluid Dynamics (CFD) analysis. Air temperature data was utilized in this study, a key factor in plant growth, often regarded as one of the most significant. A greenhouse with dimensions of 52.5 m × 21 m × 7.75 m modeled in SolidWorks and subsequently imported into ANSYS Fluent for CFD simulations. A mesh independence analysis determined that a computational grid comprising 324,414 cells provided an appropriate balance between computational efficiency and solution accuracy. CFD analysis was conducted under two conditions: with and without an active cooling system. Temperature and airflow velocity data were collected at 15 discrete points positioned at a height of 1.5 m above the greenhouse floor for all simulated scenarios. A comparison between experimental measurements and CFD results demonstrated good agreement, with the mean absolute percentage error (MAPE) remaining below 5 % in all cases. In all simulated conditions, the maximum and minimum temperatures were recorded at the greenhouse roof and floor, respectively, with a maximum temperature difference exceeding 10 °C. The findings indicated that the temperature gradient was significantly greater when the cooling system was deactivated. The optimal sensor installation position was determined using the entropy-based method and K-means clustering. Data from the mean absolute temperature difference indicates that if only one temperature sensor is to be used in the greenhouse, the entropy method suggests position 4, near the pad, as the optimal installation location, whereas the K-means method recommends position 8, at the center of the greenhouse. The optimal sensor placements were established by combining standardized temperature and air velocity data (Z-index), with nodes 4 and 5 identified as ideal locations for the entropy and K-means methods, respectively. |
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| ISSN: | 2772-3755 |