Analysis of heat transfer in a vessel with helical pipe coil and multistage impeller
Bibliographic record
Abstract
Abstract Results of heat transfer coefficient measurements in an agitated vessel heated or cooled by the liquid media flowing in a helical pipe coil are presented in this paper. The multistage impeller made of two pitched six‐blade impellers and adjustable clearance was used in a vessel with conical bottom. The transient method based on measuring the temperature dependency on time and solving the unsteady enthalpy balance was used to determine the heat transfer coefficients between the agitated liquid and the helical pipe coil. The results are summarized by the Nusselt number correlations, which describe the dependency on the impeller Reynolds number. The second part of this paper introduces a theoretical analysis of the flow in the liquid batch near the helical pipe coil. Based on the known pumping capacity of the multistage impeller, the characteristic velocity near the helical pipe coil can be evaluated. This characteristic velocity can be then used to determine the Nusselt number describing the heat transfer in a flow around a cylinder with the same cross‐section profile as the helical pipe coil. A clear correlation between this Nusselt number and the integral Nusselt number is presented in this paper and introduces an alternative approach for prediction of heat transfer characteristics on the basis of hydrodynamic parameters describing an agitated system.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".