CFD Analysis of Different Aeration Ducts on Temperature and Airflow in Stored Maize
Bibliographic record
Abstract
The conditions of temperature and airflow distribution in the mass of stored grains are highly influenced by the configuration of the aeration ducts. However, silos are large structures, and the physical experiments on them become expensive and slow. Thus, this study aimed model and evaluate the temperature and airflow distribution in a maize mass, stored in a metal silo with different geometries of aeration ducts, using CFD (computational fluid dynamic). CFD was used to model and evaluate aeration ducts of square, ring, double bar and single bar shape. The proposed model was validated from experimental data. The airflow distribution and temperature in the grain mass were analyzed at different points. The ducts of a square and a ring shape showed better distributions of airflow in the grain mass, with averages of 0.00236 m s-1 and 0.00275 m s-1, respectively. The square shape aeration duct, presented better temperature values in the middle layer of the grain mass during aeration, with average 25.09 °C. CFD can be used in decision making for the best design of a silo, saving financial resources and time, as long as the parameters used in the simulation are reliable and represent the reality.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".