Numerical Calculation of a Millmetre-sized Laval Nozzle and Optimization of the Length of Divergent Section Based on CFD
Bibliographic record
Abstract
Micro laval nozzle has been widely used in such areas as micro propulsion system,supersonic air-jet pulverization,laser cutting,etc. The gas flow characteristics for micro nozzles with different section shapes and different divergent section lengths were analyzed with the aid of computational fluid dynamics( CFD) simulation technology,which helped to determine the rules of nozzle type selection and the optimum divergent section length. The simulation results indicated that the exit velocity of two-dimensional axisymmetric nozzle is larger than that of rectangular section one,which shows that the two-dimensional axisymmetric type is recommended when the nozzle is in millimeter level. The flow fields of the two-dimensional axisymmetric nozzles with different divergent section lengths were investigated through the comparison of their exit velocities,thrust forces and efficiencies,which presents that the optimal divergent length is 3 mm in this research. The proposed simulation method can be applied to the selection of other nozzle parameters and the optimization of length determination,which can help to reduce the difficulty of manufacturability of micro-nozzle with excellent performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".