Characterization of Thermals in a Heated Turbulent Boundary Layer
Bibliographic record
Abstract
Abstract The hydrodynamic boundary layer encountered in many practical engineering systems is turbulent in nature and known to play a significant role in governing the induced friction drag and species transport. In turbulent boundary layer flows, heat transfer is often involved which increases flow complexity due to the influence of buoyancy. When the buoyant force is sufficiently large in magnitude, thermals carrying heated fluid are known to detach and rise from the wall. Literature review shows that in mixed convection, thermals have been primarily identified through qualitative flow visualizations and there is a scarcity of their quantitative assessment. Furthermore, the evolution of thermals in the boundary layer with respect to flow inertia and viscous shear is not well-understood. Hence, there is a need for a better understanding of the dynamics of thermals in mixed convection turbulent boundary layer flow. The objective of this study is to experimentally investigate the three-dimensional nature of thermals rising from a turbulent boundary layer flow over a heated smooth horizontal flat plate. Experiments were performed in a closed loop low-disturbance wind tunnel with a test section featuring a 1 m long heated bottom wall. The multi-plane particle image velocimetry (PIV) technique was used to capture images in multiple planes with respect to the turbulent boundary layer mean flow direction for three-dimensional characterization. The measurements were conducted at Richardson numbers (Ri) of 0.3, 1.0, and 2.0. Flow visualization images are used to describe the nature of thermals and the dynamical processes involved during their interaction with bulk boundary layer flow. An image processing algorithm to detect thermals is then detailed and applied to experimental images. The performance of the new algorithm is then assessed in its ability to detect thermals.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".