Global optimization of the design of intensified shell and tube heat exchanger using tube inserts
Bibliographic record
Abstract
Abstract This paper investigates global optimization of the detailed design of intensified shell and tube heat exchangers using two tube inserts: twisted tape and coiled wire. Three objectives, including heat exchanger area, total annualized cost, and environmental impact, are respectively minimized. All the design variables are considered as the ones that have discrete values based on their physical nature or manufacturing standards. We present, for the first time, a tailored global optimization approach for the design of intensified shell and tube heat exchanger: Set trimming procedure. We compare the computational performance of our set trimming procedure, exhaustive enumeration, and commercial solvers. The proposed three objectives have certain competitive relationships and multi‐objective optimization is performed to analyze the conflicts among them. Two literature examples are tested for illustration purposes. The solution results indicate that the intensified heat exchanger designed using tube inserts compares well to the regular heat exchanger using plain tube. Compared with commercial solvers, set trimming procedure runs fast and can converge to global optimum without initial value. The pareto‐optimal solutions of multi‐objective optimization provide holistic trade‐offs between different objectives, hence helping the process designer make proper decisions for the design of intensified shell and tube heat exchangers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".