Computational fluid dynamics study of optimized hypersonic leading edge geometries
Bibliographic record
Abstract
An aerothermal optimization study of two-dimensional hypersonic leading edge geometries has been performed. The accuracy of a simplified model and a reduced order numerical model was assessed through comparison to simulations of the compressible Navier-Stokes equations performed in OpenFOAM. Specifically, the estimated surface pressure, and laminar convective heating distributions have been compared. The simplified model was found to have compromised accuracy in regions of high and low surface curvature. The reduced order numerical model was found to give accurate results with significantly reduced computational cost compared to complete Navier-Stokes simulations. Optimizations were then performed using the simplified analysis technique, and the reduced order numerical model. The performance of the optimized hypersonic leading edge geometries was analyzed using OpenFOAM. The results show that both methods achieve a similar geometric result. However, the quality of the optimization is improved by using the reduced order numerical model. An analysis was performed in the design space immediately surrounding the optimized geometry to assess the impact of small geometric changes on aerothermal performance. The results show that even small changes in leading edge geometry can have a significant influence on aerothermal 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".