Performance differences and design method improvement of Laval nozzles under superatmospheric and subatmospheric conditions
Bibliographic record
Abstract
To enhance the performance of subatmospheric supersonic wind tunnels, this study explores the design of Laval nozzle profiles using the method of characteristics. For Mach numbers ranging from 1.2 to 1.8, various nozzle designs are validated using numerical simulations, and the performance differences between superatmospheric and subatmospheric conditions are analyzed. An improved formula for boundary layer correction under subatmospheric conditions is developed using the least-squares method, and its reliability is numerically verified. All nozzle designs considered in this study perform outstandingly in superatmospheric conditions, with outlet Mach number errors of less than 0.2%. Under subatmospheric conditions, the outlet Mach number errors increase by a factor of ten, and the maximum total pressure loss coefficient approaches 5.57%, representing an increase in almost 30% over the superatmospheric case. The reduced Reynolds number of subatmospheric conditions causes areas of high total pressure loss to accumulate near the wall boundary layer, which induces an increase in the boundary layer displacement of 0.2–0.4 mm and reduces the effective expansion ratio by 0.24%–0.67%. The improved boundary layer correction formula has broad applicability for Mach 1.1–2.0 nozzles under subatmospheric conditions. In such scenarios, the improved nozzles reduce the total pressure loss by 22.18%–32.37% compared with unimproved nozzles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".