High Spatial Resolution Fluid Thermometry in Boundary Layers by Macroscopic Imaging of Individual Phosphor Tracer Particles
Bibliographic record
Abstract
We introduce a measurement concept using seeded thermographic phosphor particles, which achieves high spatial resolution and rejection of surface induced signals, and is thereby applicable to resolve two-dimensional temperature distribution in sub-millimiter thermal boundary layers. Unlike previous implementations of ratiometric phosphor thermometry in fluid flows, which is based on the division of two spectrally or temporally separated images of the luminescence from clusters of seeded particles, here we treat the individual phosphor particles as independent temperature detectors positioned at the discrete particle locations. 2D rotated Gaussian functions are fitted to each particle image as to integrate particle signals in the two frames for ratio-based thermometry and to position the particles with sub-pixel resolution (< 10μm). In addition, the fitting method allows to separate the luminescence signal of the imaged particles from interfering signals with a low spatial frequency, for example from surface reflection or re-scattering of luminescence light. After assessing the spatial resolution, and the robustness of the temperature measurements against high levels of re-scattered signals, near-wall measurements are demonstrated. The ability to finely resolve the temperature distribution within a 500 µm thin thermal boundary layer is validated against the laminar Prandtl-Blasius equation. As the thermometry counterpart and complement to Particle Tracking Velocimetry, this technique allows to probe the fine details of heat transfer in boundary layers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".