MODELING OF TEMPERATURE PROFILES UNDER CONTINUOUS TUBE‐FLOW MICROWAVE AND STEAM HEATING CONDITIONS
Bibliographic record
Abstract
ABSTRACT Mathematical models were developed based on perfectly mixed flow (PMF), piston flow with heat diffusion (PFHD) and laminar flow (LF) approaches to predict liquid temperature history under continuous tube‐flow microwave and steam heating conditions. Two helical glass coils placed inside domestic microwave ovens (one coil in each of the two 700 W capacity ovens) or in a steam cabinet were used for heating and a spiral condenser at the exit was used for cooling. Transient and steady state mean temperatures of the fluid were experimentally measured at the exit and were compared with predictions from the mathematical models for both systems. The residence time, velocity distribution as well as temperature profiles, along the radius and the length of the tubes, were computed using the models. The PFHD and the LF models better described temperature profiles during the initial transient period, while the PMF model shovsed a better agreement with experimental data during steady staie conditions. The occurrence of secondary turbulence in the helical coil (associated with high Dean numbers) was believed to be responsible for reducing the radial temperature gradients and achieving close to “perfectly mixed piston flow” situation. A relatively larger temperature gradient across the radius was observed under microwave heating conditions than under steam heating conditions. The time‐temperature effects were integrated to predict the lethality at selected temperatures and flow rates for both continuous‐flow thermal processing.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".