A Multi–Level Approach for Simulation of Storage and Respiration of Produce
Bibliographic record
Abstract
A produce gas respiration model and fruit-stack geometric digital generation approach is used with commercial CFD software (ANSYS CFXTM) to conduct shape-level simulations of the fluid flow, heat and respiration processes that occur during the storage of produce, with the ultimate purpose of providing detailed information that can be used to develop closure coefficients for volume-averaged simulations. A digital generation procedure is used to develop an accurate representation of the shapes of the different produce. The produce shapes are then implemented into a discrete element modelling tool to generate a randomly-distributed stack of produce in a generic container, which is then utilized as a representative elementary volume (REV) for simulations of airflow and respiration. Simulations are first conducted on single pieces of produce and compared to a recently published experimental data for tomatoes and avocadoes to generate coefficients for the respiration model required for the shape-level simulations on the REV. The results of the shape-level simulation are then processed to produce coefficients that can be used for volume-averaged (porous-continuum-level) calculations, which are much more practical for simulations of large areas of storage comprised of hundreds or thousands of boxes of different commodities. The results show that the multi-level approach is a viable means for developing the simulation parameters required to study refrigeration, ripening and storage/transport of produce.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".