High-density controlled environment agriculture (CEA-HD) air distribution optimization using computational fluid dynamics (CFD)
Bibliographic record
Abstract
In this paper, the indoor environment of a small-scale high-density controlled environment agriculture (CEA-HD) space was simulated using computational fluid dynamics.Spatial modelling of the indoor environment considering the influential phenomena (e.g.transpiration and photosynthesis) over the indoor temperature, relative humidity, carbon dioxide (CO2) concentration, and airflow velocity is still challenging.These indoor environment conditions were computed for a 3D model of a CEA-HD experimental space while simultaneously modelling crop airflow impingement, transpiration and photosynthesis.The crops being grown were represented in the model as porous media zones and their exchanges with the indoor air were modelled using user defined functions.The air distribution parameters and configuration were optimized using a simplified 2D model to overcome the steep computational time, and associated cost, of 3D simulation.The objective function of the optimization problem relied on a correlation analysis of the simulation output.The optimization of the 2D model yielded an airfoil configuration that reduced the mean airflow speed and relative humidity variations between the cultivation tiers while achieving higher mean velocities ( ≈ 1.9 m•s -1 ) at a lower inlet speed (8 m•s -1 ).The proposed modelling and optimization approach is a small step forward towards model-based design and operation of CEA-HD production spaces.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".