Study Response of Turbulence in Transition of Unsteady Channel Flows
Bibliographic record
Abstract
Experimental study is carried out to study the response of turbulence in an unsteady turbulent flow subject to a rapid increase of flow rate.The study confirms the concept proposed by He and Seddighi [1] (J Fluid Mech 715:60-102, 2013) based on DNS results that transient channel flow undergoes a process of transition that resembles the laminar-turbulent bypass transition despite the initial flow is turbulent.Particle Image Velocimetry (PIV) is used to obtain the instantaneous velocity of the unsteady flows.The determination of the skin friction coefficient of the unsteady flows is obtained from hot-film anemometry.A predetermined flow variation is obtained through a pneumatically regulated valve.During the acceleration of the channel flow, the Reynolds number is increased rapidly from the initial Reynolds number to the final Reynolds number.In response to the rapid increase of flow rate, the skin friction coefficient increases sharply resulting from the creation of a thin boundary layer of high strain near to the wall.As the boundary layer thickness increases, the viscous force reduces, and skin friction coefficient decreases to a minimal point that marks the onset of transition.During the transitional period, new turbulence structures are generated that make the skin friction coefficient to increase again.The present study conforms with the findings of the previous DNS that the transient flow involves three phases, namely pre-transition, transition and fully turbulent.The three distinct phases are equivalent to the three regions of boundary layer bypass transition which are buffeted laminar flow, intermittent flow, and fully turbulent flow regions and the flow characteristics are similar to previous numerical results on channel transient flows.
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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 | 0.000 | 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".