Effects of dimensional size and surface roughness on service performance for a micro Laval nozzle
Bibliographic record
Abstract
Abstract Nozzles with large and small dimensions are widely used in various industries. The main objective of this research is to investigate the effects of dimensional size and surface roughness on the service performance of a micro Laval nozzle. The variation of nozzle service performance from the conventional macro to micro scale is presented in this paper. This shows that the dimensional nozzle size has a serious effect on the nozzle gas flow friction. With the decrease of nozzle size, the velocity performance and thrust performance deteriorate. The micro nozzle performance has less sensitivity to the variation of surface roughness than the large scale nozzle does. Surface quality improvement and burr prevention technologies are proposed to reduce the friction effect on the micro nozzle performance. A novel process is then developed to control and depress the burr generation during micro nozzle machining. The polymethyl-methacrylate as a coating material is coated on the rough machined surface before finish machining. Finally, the micro nozzle with a throat diameter of 1 mm is machined successfully. Thrust test results show that the implement and application of this machining process benefit the service performance improvement of the micro nozzle.
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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".