Design of Fixed-Length Gradual Expansion Supersonic Nozzles for Use in Wind Tunnels
Bibliographic record
Abstract
A robust and repeatable method of designing fixed length, gradual expansion planar supersonic nozzles for use in transient wind tunnels is developed. The process designs nozzles using an ellipse for the non-simple expansion region, optimizing the major and minor radii for a fixed length. Six Mach setting nozzles from M=1.5-4.0 in ΔM=0.5 increments are designed with a fixed expansion length of 0.7 m and a test area of 0.20×0.23 m2 (8×9 in2) and length of 0.3 m (12 in). ANSYS Fluent is used to simulate the axial boundary layer progression for the Mach 2.5 nozzle using the 4-Equation Transitional SST turbulence model with a RANS solver. The boundary layer thickness at the nozzle exit is predicted to be 7.7% of the test section height. The boundary layer points are exported, and the method of reflection thickness is used to correct the nozzle contour in python. The corrected Mach 2.5 nozzle is fabricated using additive manufacturing after simulating with finite element analysis to ensure structural integrity. The nozzle is validated experimentally by measuring the Mach number through static pressure ports positioned axially along the nozzle contour, and a 10◦ wedge to induce oblique shock waves. Condensed water vapor in the test section caused by high relative humidity resulted in a lower than design Mach number for some tests. Two test runs were performed at lower humidity, reaching the expected test Mach number, validating the nozzle design.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".