Numerical Simulation on the Anti-fouling Property of Helical Blade Rotors in Circular Tube
Bibliographic record
Abstract
Inferior heat transfer efficiency and severe fouling precipitation have been the troublesome problems for shell-and-tube heat exchangers.As a typical type of tube insert, the helical blade rotors (HBRs) can achieve heat transfer augmentation and fouling mitigation in heat exchanger.In this work, the numerical simulation regarding to the anti-fouling property of HBRs in circular tube was conducted.The numerical method was verified accurate in comparison with the results in literature prior to the simulation.The results showed that in the process of the crystallization of the fouling the deposition rate decreased and the removal rate increased, and eventually they tended to be stable and reached a dynamic balance; the deposition rate, the removal rate, the net rate and the thermal resistance in tube with HBRs was lower than that in smooth tube, which means that inserting HBRs into the circular tube can effectively prevent the deposition of crystallization fouling; moreover, the mass fraction of CaCl 2 in tube with HBRs was lower than that in smooth tube, and in tube with HBRs it was not just lower near the tube wall but also near the HBRs, which illustrate that the HBRs can enhance the mass transfer of Ca 2+ in circular tube, and the mass transfer of Ca 2+ towards the HBRs is enhanced, while the mass transfer towards the tube wall is weakened.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".