An Implicit Adaptive Non-Linear Frequency Domain Method (pNLFD) for Viscous Periodic Steady State Flows on Deformable Grids
Bibliographic record
Abstract
In the present study, an implicit and adaptive Nonlinear Frequency Domain method (pNLFD) has been implemented to the Navier-Stokes equations on deformable grids. Although the computational time for periodic flows is drastically reduced by using the NLFD approach over classical time marching schemes, implementing the pNLFD concept leads to an even faster numerical algorithm. Besides that, the need for a large amount of memory, which is the main disadvantage of the NLFD method, is resolved in the present pNLFD approach. Moreover, the concept of dynamic or moving/deformable grid, which is a need in many problems dealing with periodic flows, is extended to the pNLFD method. Finally, in order to accelerate the convergence, the nonlinear LU-SGS technique which is an implicit time marching method, is implemented. In the LU-SGS technique the cells are treated locally, hence its implementation is quite suitable for the pNLFD method, where different cells have different number of modes and therefore has to be treated individually. Results are presented for 2D stationary, oscillating and pitching cylinders 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".