Implicit Navier-Stokes computations of unsteady flows using subiteration methods
Bibliographic record
Abstract
A time-accurate, implicit numerical method for the two-dimensional compressible thin-layer Navier-Stokes equations is described. Subiteration techniques are used to solve the non-linear problem arising at each time step. The subiteration methods have been incorporated in the well-known implicit Navier-Stokes flow solver ARM, developed at the NASA Ames Research Center. Two different subiteration techniques are used. They employ similar methodologies used to solve steady-state problems such as approximate factorization, diagonalization, and local time-stepping. One scheme uses a single time-step value while the other uses a dual time-stepping approach where one time-step governs the temporal accuracy of the code and the second time-step is chosen to accelerate convergence at each iteration. Two modifications are made to the subiteration schemes to improve the efficiency of the code. The combined second and fourth-difference dissipation, which requires the inversion of a set of scalar pentadiagonal equations, is altered to use a modified second-difference dissipation scheme in the implicit operator, leading to a scalar tridiagonal form. The second modification consists of freezing the Jacobian matrices during the subiterations. Turbulent flow about an airfoil under buffet conditions is also considered. (Abstract shortened by UMI.)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".