Bibliographic record
Abstract
Drying is important in many food processing applications, and particularly so in the dry fruits industry. This work is focused on developing computational models for simulating the drying of almonds in a tray dryer. It is important to quantitatively understand heat and mass transfer within and around a single almond particle as well as the particle‐particle interactions and their implications for dryer design. In this work, we have developed a systematic CFD modelling framework for modelling almond drying in a tray dryer. A single tray filled with almonds (∼2 kg) were dried at three set temperatures viz., 55, 65, and 75 °C. Air relative humidity at the inlet and outlet locations, and the weight of almonds were measured during drying for each experiment. An additional set of experiments were conducted in which almonds were filled only in the half section of the tray, keeping the other half empty. The same amount of almonds were used, to have multiple layers of almonds in the tray, and the set temperature for the experiment was 75 °C. Flow, heat, and mass transfer in the tray dryer were simulated using commercial CFD software Ansys Fluent. The validated computational model was used to simulate various cases including larger and more trays. The developed approach and models will be useful to select the appropriate dryer configuration and optimize its design. The developed models will also be useful to identify suitable operation conditions for the drying of almonds as well as other food products.
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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) | 0.001 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".