Application of genetic algorithms in design and optimisation of multi‐stream plate–fin heat exchangers
Bibliographic record
Abstract
Abstract This paper introduces two new approaches in the thermo‐hydraulic design of multi‐stream heat exchangers (MSHEs). In both approaches, geometrical aspects of the MSHE (e.g. exchanger dimensions, fin type, etc.) are optimised with a genetic algorithm (GA) using the Total Annual Cost (TAC) as an objective function. The first approach is capable of utilising the maximum allowable stream pressure drops and can result in minimal surface area requirements. In the second approach, all of the pressure drop values are considered as design variables and are therefore subject to optimisation. These approaches have been applied to two case studies taken from literature, and the results are compared to those arising from a currently used design method. In the first case study, application of the new approaches resulted in a smaller TAC than the current approach by 5.77% and 31.86%, respectively, and improvement in the second case was estimated to be 5% and 21.46%, correspondingly. The effect of different fins on an MSHE's TAC is discussed through application of GAs to the current approach. It is shown that correct selection of fin types reduces the TAC of the two case studies by 21.75% and 8.7%, respectively. © 2012 Canadian Society for Chemical Engineering
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".