Higher-Order Spatial Discretization for Turbulent Aerodynamic Computations
Bibliographic record
Abstract
A higher-order spatial discretization is presented for the solution of the thin-layer Navier ‐Stokes equations with application to two-dimensional turbulent aerodynamic e ows. The terms raised to a level of accuracy consistent with third-order global accuracy include the inviscid and viscous e uxes, the metrics of the generalized curvilinear coordinate transformation, the diffusive e uxes in the turbulence model, the numerical boundary schemes, and the numerical integration technique used to calculate forces and moments. Given the presence of grid and e ow singularities, third-order convergence behavior is not expected. The motivation is to reduce the numerical error on a given grid or to reduce the grid density required to achieve specie ed error levels. Results for several grid convergence studies show that this higher-order approach produces a substantial reduction in numerical error in the computation of single- and multielement aerodynamic e ows, both subsonic and transonic. Comparisons with a well-established second-order algorithm demonstrate that signie cant savings in computing expense, typically factors of three to four, can be achieved using the higher-order discretization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.006 | 0.002 |
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".