Buoyancy-driven modulation of Görtler instability and its effects on boundary-layer transition under non-adiabatic conditions
Bibliographic record
Abstract
We investigate the laminar-to-turbulent transition in concave-wall boundary layers subject to Görtler instability under adiabatic, heated, and cooled wall conditions. Using large-eddy simulations in the Oberbeck–Boussinesq framework with a temperature difference of ΔT=±60 K, one finds that non-adiabatic conditions cause a slight shift in transition onset and significantly affect primary and secondary instabilities. Wall heating enhances primary Görtler vortices, increasing skin friction. Conversely, cooling reduces vortex intensity. Energized velocity profiles are observed under heated walls, and suppressed profiles under cooled walls. In the adiabatic case, transition follows the classical evolution of Görtler vortices, while non-adiabatic conditions, such as heating or cooling, introduce buoyancy effects even in the laminar region, leading to the early amplification of subharmonic modes. These modes interact non-linearly with primary vortices, modifying the energy cascade and the breakdown process. The study also identifies a new marker for the breakdown of varicose-dominated turbulence and highlights the influence of buoyancy on the onset of sinuous and varicose instabilities. Despite the difference in thermal conditions, both heating and cooling result in similar turbulence kinetic energy levels for these secondary instabilities.
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.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".