Canary Greenhouse CFD Nocturnal Climate Simulation
Bibliographic record
Abstract
The aim of this paper is to predict in details the distributed nocturnal climate inside a one hectare Moroccan canary type tomato-greenhouse equipped with continuous roof and sidewalls ventilation openings with fine insect screens, by means of 3D CFD (Computational Fluid Dynamics) simulations by using a commercial Software package CFD2000 based on the finite volumes method to solve the mass, momentum and energy conservation equations. The turbulent transfers were described by a k-ε model. Likewise, the dynamic influences of insect screens and tomato crop on airflow movement were modeled by means of the concept of porous medium with the Boussinesq assumption. Atmospheric radiations contribution was included in the model by customising the plastic roof cover temperature deducted from its energy balance. Also, the CFD code was customized in order to simulate in each element of the crop cover the sensible and latent heat exchanges between the greenhouse air and tomato crop. Simulations were carried out with a wind prevailing direction perpendicular to the roof openings (west-east direction). Simulations were later validated with respect to temperature and specific humidity field measurements inside the experimental greenhouse. Also, the model was verified respect to global sensible and latent heat transfers. Results show that, generally, greenhouse nocturnal climate distribution is homogeneous along the studies greenhouse area. The insect proof significantly reduced inside airflow wind speed. But there is no significant effect on the inside air temperature and specific humidity respect to outside.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".