Bibliographic record
Abstract
This thesis is a product development project that was performed at Alfa Laval Tumba AB. In the past Alfa Laval have manufactured gas separators for engines where gas flows were cleaned from oil. A new business area for this kind of technology has shown to be the manufacturing industry where oil mist has to be separated from the vented air. Therefore a new and bigger design is needed to be able to handle the greater gas flows. A product like that would be a strong competitor on the market when the maintenance would be low in comparison to today’s equipment. During the project several concept suggestions were developed using customer interviews and research as input. The concepts were evaluated and a winner was selected and was developed further. This final concept was analysed considering primarily performance, production cost, solid mechanics and rotor dynamics. The result was named Skarven. Its cleaning technology is made out of a spinning disc stack that with centrifugal force separates the oil from the airflow. The disc stack is enclosed by a cylindrical shell and is powered by an electric motor. The small size and weight makes it possible to mount Skarven directly on the machine, which is preferable. The low manufacturing cost and the simple design makes it cheaper, lighter, smaller and easier to maintain than the competitive products. The conclusion of this thesis is that Alfa Laval’s gas separator can be readapted to greater gas flows and have a good chance to gain market shares in the oil mist eliminator business with this design.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.031 |
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".