An Implicit Adaptive Non-Linear Frequency Domain Method (pNLFD) for Viscous Periodic Steady State Flows on Deformable Grids
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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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 it