Simulations of Slender Hypersonic Geometries with Blunt Leading Edges Using rhoCentralFoam
Bibliographic record
Abstract
Hypersonic flows have extensive applications in aerospace and present numerous areas of intrigue for fundamental fluid and thermodynamics research. Due to the high costs and practical difficulties of examining these flows experimentally, numerical simulations are a key tool. Effective simulation of hypersonic blunt flows for the prediction of heat transfer, skin friction, and other salient fluid dynamic phenomena remains a challenge. This is particularly true for three-dimensional problems, as the computational cost can be very large, even for simple shapes. Therefore, it is of interest to highlight and explore reliable, extensible, and efficient simulation methodologies, covering the process, including meshing, preconditioning, solution, and postprocessing. In the present paper, simulations have been performed of multiple benchmark cases for hypersonic flow to investigate and validate effective simulation methodologies in the OpenFOAM framework. Simulations are compared to experimental results. Key points of discussion include initialization, meshing strategies, numerical schemes, and their impact on results. The current paper shows that the compressible flow solvers within OpenFOAM are clearly extendable to complex three-dimensional geometries, but require careful attention to mesh topology.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".