Optimal geometry and flow arrangement for minimizing the cost of shell‐and‐tube condensers
Bibliographic record
Abstract
This paper presents a model for estimating the total cost of shell-and-tube heat exchangers (HEs) with condensation in tubes or in the shell, as well as a designing strategy for minimizing this cost. The optimization process is based on a genetic algorithm. The global cost includes the energy cost (i.e. pumping power) and the initial purchase cost of the exchanger. The choice of the best exchanger is based on its annualized total cost. Eleven design variables are optimized. Ten are associated with the HE geometry: tube pitch, tube layout patterns, baffle spacing at the center, baffle spacing at the inlet and outlet, baffle cut, tube-to-baffle diametrical clearance, shell-to-baffle diametrical clearance, tube bundle outer diameter, shell diameter, and tube outer diameter. The last design variable indicates whether the condensing fluid should flow in the tubes or in the shell. Two case studies are presented and the results obtained show that the procedure can rapidly identify the best design for a given heat transfer process between two fluids, one of which is condensing. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".