Modeling and analysis of heat transfer in a four-season greenhouse using ANSYS Fluent CFD software
Bibliographic record
Abstract
In multiseason greenhouses, heating and cooling systems account for a significant part of the total operating cost. To make greenhouses more efficient it is important to reduce their reliance on external sources of energy as well as to minimize the energy wasted when it is most available. To do this a ground to air heat transfer (GAHT) system is used to store excess heat available during the daytime into the ground where it can be recovered during the night when the temperature in the greenhouse decreases to sub optimum levels. In this work, the mixture multiphase model in ANSYS Fluent was used to model and analyze the flow and energy exchange of dry and humid air through a simple GAHT system during daytime and nighttime conditions. The analysis of the dry air at initially 35 °C showed that approximately 70 kJ of heat stored in the air was able to be transferred to the cold soil initially at 15 °C and that increasing the flow of the air caused the exchange in heat to occur faster. The analysis of air initially at 35 °C with 80% relative humidity was able to transfer between 400 and 450 kJ of energy to the ground initially at 15 °C. This exchange of heat occurred predominantly due to thermal contact between the air and the soil/GAHT system and through the latent heat of condensation resulting in approximately a factor of 6 increase in energy exchange compared to dry air. In the last part of this work, we simulated nighttime conditions where colder air initially at 5 °C with 50% relative humidity was forced to circulate in the GAHT system with the ground initially at 15 °C. The results showed that the ground was able to successful transfer energy to the air warming the latter to 16 °C. In this case the exchange of thermal energy was significantly less with heat being transferred from the ground to the air, from the latent heat of condensation to the ground, and finally from the thermal energy of the air back to the ground. The results of this work demonstrated great potential for using ANSYS Fluent to further study the design characteristics of GAHT systems in the hopes of increasing their efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".