Development of Foot Design for Gasketed Plate Heat Exchangers
Bibliographic record
Abstract
In the process of making the industry more energy efficient, the gasketed plate heat exchanger has an important role as it effectively transfers heat between fluids. This master thesis aims to investigate and develop a new supportive foot for Alfa Laval’s gasketed plate heat exchangers. The goal was to present a design that can sustain severe external loads induced by for example the connecting piping system or earthquakes. Beyond sustaining the severe loads, the foot was also required to maintain a high serviceability and a low cost. The project covered in this report follows a development process where eleven different concepts were generated and scored. The most important factors in the developed concepts were cost, manufacturing, and serviceability. Two concepts emerged from the scoring and was further developed to test their feasibility. From the in-depth development one concept remained and was considered the best design. The design was then modelled in a FEM program together with an existing design to verify the stresses in the used bolts in the foot. In the verification of the design, it was concluded that the new design reduced the usage in the attached bolts in the foot by up to 67%, compared to existing designs. It was also found that preloading the bolts made the model less tolerant to the severe loads. Furthermore, adding a higher frictional coefficient to the model decreased the usage in the studied bolts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".