Pineapple Drying Analysis in Packing Plants Through Computational Fluid Dynamics
Bibliographic record
Abstract
The aim of this research is to determine the average fluid velocity values and the average total pressure at the outlet of the ventilation system conduit for the drying, by air impulsion, of pineapples in packing plants, in order to ensure the required humidity level of the crown for its proper packaging. The CAD model of the air conduction was obtained in Solidworks and Computational Fluid Dynamics (CFD) techniques were applied to simulate the performance of the ventilation system. Air was indicated as the type of fluid, and an internal analysis of the pipe with roughness Rz = 3.2 μm. As a computational domain, the pipeline was established, from the connection to the fan to the outlet diffuser. As boundary conditions were established; a W and Z Manufacturing regenerative fan at the inlet of the flow pipe 618,439 m3/h, and at the outlet of the pipe the value of atmospheric pressure. As a result of the simulation, an average velocity value at the diffuser outlet of 127.294 m/s was obtained, higher than the required 96.06 m/s. An average total pressure value of 111718.73 Pa was obtained in the conduit suitable for the proposed purposes. For the conveyor belt, it is recommended to use, in each row of pineapples, the simulated fan with a simple diffuser with a rectangular section of 1 x 20 cm, or a regenerative fan with a flow of 1248.77 m3/h and a diffuser with two-outlet bifurcation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".