Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research activities on inflow turbulence generation methods have been vigorous over the past quarter century, accompanying advances in eddy-resolving computations of spatially developing turbulent flows with direct numerical simulation, large-eddy simulation (LES), and hybrid Reynolds-averaged Navier-Stokes–LES. The weak recycling method, rooted in scaling arguments on the canonical incompressible boundary layer, has been applied to supersonic boundary layer, rough surface boundary layer, and microscale urban canopy LES coupled with mesoscale numerical weather forecasting. Synthetic methods, originating from analytical approximation to homogeneous isotropic turbulence, have branched out into several robust methods, including the synthetic random Fourier method, synthetic digital filtering method, synthetic coherent eddy method, and synthetic volume forcing method. This article reviews major progress in inflow turbulence generation methods with an emphasis on fundamental ideas, key milestones, representative applications, and critical issues. Directions for future research in the field are also highlighted.
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.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it