Air Flow Model for Mixed-Mode and Indirect-Mode Natural Convection Solar Drying of Maize
Bibliographic record
Abstract
An air flow model for mixed-mode and indirect-mode natural convection solar drying of maize to help understand the factors that influence air flow in the dryer is presented. Temperatures at various sections of the dryer obtained from drying experiments were input to the air flow model to predict the respective thermal buoyancies. The air flow rate was determined by balancing the sum of the buoyancy pressures with the sum of the flow resistances in the various sections of the dryer. To validate the model, the predicted air flow was compared with measured air flow from experiments. For both the mixed-mode and indirect-mode, the biggest driver of the air flow is the thermal buoyancy created in the collector, while the grain bed is the dominant pressure drop. Thermal buoyancy on top of the grain bed is largely responsible for the variation in air flow, translating into low mass air flow during the early stages of drying when grain moisture is high, and higher air flow in the later stages when grain moisture is low. The heating of the grain bed by direct radiation in the mixed-mode translates into a slightly higher air flow rate than the indirect-mode. The implications are that a thinner grain bed results in shorter drying time as it has a higher air flow rate than a thicker one. To mitigate the low air flow at the early stages of drying, the collector length should be appropriately designed for a desired air flow.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".