CFD investigation of Plate Channels
Bibliographic record
Abstract
Alfa Laval has a wide product range for plate heat exchangers that can be applied in most industries. The constant development of new and old heat exchangers drive the need for new and efficient methods that can reliably be used in the development process. In this thesis, a new turbulence model within a computational fluid dynamics-program has been tested and evaluated for one of Alfa Lavals commercial plate heat exchangers to see if better results can be achieved compared to older models. This project has been divided into three main parts. Firstly, a mesh-study was conducted for a typical heat exchanger geometry in order to understand mesh-size in relation to interesting parameters. Secondly, since the turbulence-model has a set of parameters for tuning the flow, an initial parameter study was conducted. Testing combinations of the parameters on a smaller geometry. This was also combined with a high-resolved large eddy simulation (LES) to study the parameters effect on local phenomenons. Lastly, a global parameter-study was conducted for the commercial plate heat exchanger. The project showed that better accuracy can be achieved with certain sets of model-parameters depending on different plate-combinations. Future work is however needed as it could be interesting to further study local phenomenons using a LES-model on a plate heat exchanger that has experimental data, but most importantly test the GEKO-model for more heat exchangers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".