Investigation of Turbulent Flow Behavior in a Heated Boundary Layer
Bibliographic record
Abstract
Abstract The boundary layers are known to play key roles in many engineering systems. The hydrodynamic boundary layer found in these systems is often turbulent in nature and heat transfer is involved which further increases flow complexity due to the influence of buoyancy. One of the constituent layers of the turbulent boundary layer, the inner layer, has been established as home to key dynamical turbulent phenomena which can be influenced by the buoyant force. In the mixed convection flow regime, flow inertia and buoyant force are on the same order of magnitude. In this regime, buoyant thermals rising from the wall interact with the inertia-driven turbulent flow field resulting in highly complex three-dimensional flow dynamics. Past research studies conducted in this flow regime have been mostly computational in nature with little experimental work. The current knowledge on the impact of the relative contributions by the buoyant force and flow inertia on turbulent phenomena in the mixed convection flow regime is very limited. This study reports on an investigation into the turbulent flow phenomena present in mixed convection turbulent boundary layer flow over a heated smooth horizontal flat plate. Experiments were performed in a closed loop wind tunnel where the turbulent boundary layer was heated from below. The multi-plane particle image velocimetry (PIV) technique was used to capture two-dimensional velocity fields over two planes with respect to the flow direction. Experiments were conducted over a range of Richardson numbers (Ri) between 0.0 and 2.0 to control the relative contribution of the buoyant force with respect to flow inertia. The measured velocity fields are used to describe the influence of buoyancy on the three-dimensional turbulent boundary layer flow.
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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 | 0.000 |
| 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".