The effect of free-stream turbulence on turbulent boundary layers and convective heat transfer from flat plates
Bibliographic record
Abstract
The present work investigates the effect of free-stream turbulence (FST) on turbulent boundary layers and forced convective heat transfer from flat plates. High resolution, 2-D and 3-D, steady Reynolds-Averaged Navier-Stokes (RANS) simulations using Computational Fluid Dynamics (CFD) techniques were performed to analyze the influence of different free-stream conditions, such as turbulence intensity (TI), integral length scale (Lu) and free-stream velocity (Uo) on local and total skin friction and convective heat transfer coefficients (CHTC), as well as on turbulent boundary layer parameters (boundary layer thickness and momentum thickness). The present study shows that the Shear Stress Transport (SST) k-ω model with the low Reynolds number (Re) approach is the most suitable model for representing incident turbulent flow over isothermal flat plates, since it provides the correct skin friction and Nusselt number for turbulent boundary layers, along with the appropriate streamwise TI decay through the numerical domain. Using the results, a set of non-dimensional correlations for local and total skin friction, momentum thickness, local and total CHTC were developed. These are simple and useful tools for the prediction of skin friction and forced convective heat transfer from flat plates under FST, which can be helpful for many engineering applications such as photovoltaic systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".