Experimental and numerical evaluation of the performances of type‐ <scp>C</scp> and three‐segment demisters used in cooling towers
Bibliographic record
Abstract
Abstract Demisters have become a key vapour‐liquid separation device for eliminating mist and preventing any escape of liquid droplets from cooling towers. In this paper, the overall performances of two types of demisters, namely type‐C and three‐segment demisters, are systematically investigated by using both experimental and numerical methods. The validation results show that the numerical results agree well with experimental data. On this basis, the underlying influences of key operating parameters, such as the circulating pressure of the pump, spray water flow rate, inlet gas velocity, and droplet size on the overall performance, are revealed. The results show that the overall separation efficiency is more sensitive to the gas velocity and increasing the gas velocity leads to two distinct declining stages of the overall separation efficiency for both demisters. The first stage appears due to the competition between the drag force and the inertial/centrifugal forces, while the next stage arises mainly due to the droplet re‐entrainment. The geometric design of the type‐C demister is more conducive to the separation of the fine droplets due to the presence of the hook plate, even at a relatively low gas velocity. Besides, the type‐C demister has more potential to reduce the power consumption while achieving a higher profit for a given grade separation efficiency.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".