Pressure drop optimisation in design of multi‐stream plate‐fin heat exchangers, considering variable physical properties
Bibliographic record
Abstract
Abstract A modified method for the design of multi‐stream plate‐fin heat exchangers that considers variable physical properties is proposed in this paper. The new method, based on Pinch Technology, exploits the dependency of physical properties (heat capacity, viscosity, density and thermal conductivity) on temperature variations. A set of temperature correction factors based on variable physical properties is derived for the hot and cold streams of a multi‐stream heat exchanger. This allows calculation of effective stream pressure drops, which can lead to a valid trade‐off between operating and capital cost in the targeting stage. Accordingly, composite curves are constructed; based on the enthalpy intervals, the multi‐stream heat exchanger is subdivided into a number of block sections. A plate‐fin heat exchanger is then designed for each section by maximising the allowable effective pressure drops. Next, using a Genetic Algorithm, the method is completed in order to optimise the pressure drop of streams. Therefore, fin types for each individual stream are considered as optimising variables. By taking the variable physical properties of each stream into account and using the best fin selection, one can achieve accurate results in the design stage.
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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".