Verification and Validation of High-Fidelity Open-Source Simulation Tools for Supersonic Aircraft Aerodynamic Analysis
Bibliographic record
Abstract
Abstract Small supersonic vehicle concepts used as research platforms to test new aerospace technologies, such as advanced propulsion systems or large sensor payloads, require major modifications to conventional, large-scale, manned, supersonic airframe design. High-fidelity numerical simulation of these concepts in academic settings often requires the use of in-house or available open-source tools instead of expensive commercial software or those with export-control restrictions. A verification and validation analysis of two widely-used open-source compressible-flow solvers, rhoCentralFoam (rCF) and su2, is performed for several flow problems relevant to the supersonic aerodynamics of small-scale, autonomous aircraft concepts. The one-dimensional shock tube problem, two-dimensional supersonic turbulent boundary layer, and three-dimensional delta wing are simulated with both solvers. The effects of flux scheme, flux limiters, and Courant–Friedrichs–Lewy (CFL) number on solution accuracy, stability, and solver speed are assessed. The solvers' limitations and their usefulness as supersonic aircraft design tools in a holistic sense are discussed.
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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 | 0.008 | 0.017 |
| 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 | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".