Algorithmic Advanced for the Adaptive Non-Linear Frequency Domain Method.
Bibliographic record
Abstract
An innovative implicit approach for the adaptive Nonlinear Frequency Domain method (adaptive NLFD) has been introduced for the Navier-Stokes equations on deformable grids. It has been shown that for a periodic ow problem, a huge reduction in the computational costs and a spectral temporal accuracy of the results could be achieved by solving the ow governing equations in the frequency instead of the time domain. This computational e ciency may be even further enhanced through an adaptive modal augmentation of the Fourier series representing the local ow solution. In the present study, to accelerate the convergence rate, an innovative modi ed nonlinear LU-SGS technique is proposed, where the modes are updated in a segregate fashion. The unique and important outcome of this implementation is that the computational e ciency of the solver does not decrease as the number of modes increases. Results are presented for the laminar vortex shedding behind a stationary cylinder, a stationary transonic airfoil, and a plunging airfoil and are compared with previous numerical results as well as experimental data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".