Bibliographic record
Abstract
Thermally modulated flow is very common in nature and can be spotted in the atmosphere, ocean, mantle, as well as in industrial processes. The study of thermally modulated flow is of interest to unravel intriguing physics and provide a platform for engineering applications such as flow control, mixing intensification, etc. A reference flow, driven by pressure gradient, has been considered in a three-dimensional channel, where thermal modulation was applied in the form of a sinusoidal heating profile in spanwise (perpendicular to the flow) direction. The heating profile is characterized by the heating intensity (Rap) and the heating wavenumber (). Stationary analysis of this configuration exhibits the formation of streaks at low Reynolds numbers. These streaks are themselves subject to instabilities and can be favorable for mixing intensification. The conditions leading to the onset of the instabilities have been determined using linear stability analysis. It was observed that sinusoidal heating at the lower wall leads to a new instability mode. This new mode is driven by the inviscid mechanism, and disturbance motion is mostly activated in the middle of the channel. The critical Reynolds number significantly decreases as the heating intensity increases, e. g., the critical Reynolds number can be reduced to Rec 260 for Rap = 1800. The critical conditions for the onset of this instability have been determined for the complete range of heating wavenumbers and 0.8 has been identified as optimum. It is further demonstrated that the temperature field only marginally affects the critical conditions.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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 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".