Analysis of De-Laval nozzle designs employed for plasma figuring of surfaces
Bibliographic record
Abstract
Plasma figuring is a dwell time fabrication process that uses a locally delivered chemical reaction through means of an inductively coupled plasma (ICP) torch to correct surface figure errors. This paper presents two investigations for a high temperature jet (5000 K) that is used in the context of the plasma figuring process. Firstly, an investigation focuses on the aerodynamic properties of this jet that streamed through the plasma torch De-Laval nozzle and impinged optical surfaces. Secondly, the work highlights quantitatively the effects of changing the distance between the processed surface and nozzle outlet. In both investigations, results of numerical models and experiments were correlated. The authors’ modelling approach is based on computational fluid dynamics (CFD). The model is specifically created for this harsh environment. Designated areas of interests in the model domain are the nozzle convergent-divergent and the impinged substrate regions. Strong correlations are highlighted between the gas flow velocity near the surface and material removal footprint profiles. In conclusion, the CFD model supports the optimization of an ICP torch design to fulfil the demand for the correction of ultra-precision surfaces.
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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 | 0.001 | 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 | 0.001 | 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".