The Curious Case Of A NACA 0012 Airfoil: Are We Learning Something New?
Bibliographic record
Abstract
Numerical methods and "history-contextualized" high-performance computing have been the cornerstone of computational fluid dynamics (CFD) for the past 70 years, allowing solving of more and more complex models governing flow physics.In CFD, we are at a juncture where it has become possible to use so-called high-fidelity methods to simulate accurate complex flow models, allegedly with high accuracy.The primary method for building and quantifying confidence in modeling and simulation is to verify and validate computational tools.The focus of this paper is on a widely-used test case used for the verification of compressible flow solvers.We report the results for the compressible flow past a 2D NACA 0012 airfoil at Reynolds, Mach, and Prandtl numbers of 𝑅𝑒 = 5,000, 𝑀𝑎 = 0.5, 𝑃𝑟 = 0.72, at zero angle of attack.This test case is reported to be (laminar) steady in the literature.Is that the case?Is this test case teaching us something new about the large class of numerical discretizations used in this work?
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".