Natural Convection Solar Tunnel Dryer for Maize: Design Process, CFD Simulation, Drying, Exergy and Economic Performance
Bibliographic record
Abstract
This study explores the design, CFD simulation, experimental validation, exergy, and economic performance of a natural convection solar tunnel dryer for drying maize ears. The dryer, designed to address postharvest losses and promote sustainable drying practices, has a capacity of 114.2 kg and a total area of 5.0 m². It reduced the maize moisture content from 23.4% to 12.5% (wet basis) in 5 days, compared to 12 days under open sun drying. Experimental performance closely aligned with CFD simulations performed using SOLIDWORKS 2023, which predicted airflow and heat transfer. The dryer effectively heated the air from an average of 25°C at the inlet to 55°C at the collector’s outlet end. The central and top sections exhibited the highest temperatures due to direct solar radiation, while slight cooling occurred near the outlet as heat was absorbed by the drying material and lost through convection and radiation. The chimney, designed as a vertical solar collector, enhanced airflow by increasing buoyancy pressure. Exergy analysis identified losses due to irreversibility, suggesting chimney design modifications to improve airflow and exergy utilization. Economically, the dryer offered a payback period of 3.7 years, demonstrating its value in preventing postharvest losses and enhancing food safety. This integrated approach highlights the feasibility and sustainability of natural convection solar dryers as effective solutions for postharvest maize drying, particularly in regions with similar climatic conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".