Development of a Thermographic Imaging Technique for Simultaneous Interfacial Temperature and Velocity Measurements
Bibliographic record
Abstract
An experimental technique, hereby referred to as 'thermographic particle velocimetry' (TPV) and capable of recovering twodimensional (2-D) surface temperature and velocity measurements at the interface of multiphase flows is presented.The proposed technique employs a single infrared (IR) imager and highly reflective, silver-coated particles, which when suspended near or at the interface, can be distinguished from the surrounding fluid due to their different emissivity.The development of TPV builds upon our previous IR imaging studies of heated liquid-film flows; yet, the same measurement principle can be applied for the recovery of 2-D temperature-and velocity-field information at the interface of any flow with a significant density gradient between two fluid phases.The image processing steps used to recover the temperature and velocity distributions from raw IR frames are demonstrated by application of TPV in a heated and stirred flow in an open container, and include the decomposition of each raw frame into separate thermal and particle frames, the application of perspective distortion corrections and spatial calibration, and the implementation of standard particle image velocimetry algorithms.Validation experiments dedicated to the measurement of interfacial temperature and velocity were also conducted, with deviations between the results generated from TPV and those from accompanying conventional techniques not exceeding the errors associated with the latter.Finally, the capabilities of the proposed technique are demonstrated by conducting temperature and velocity measurements at the gas-liquid interface of a wavy film flow downstream of a localised heater.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".