On the use of the active infrared technique to infer heat and gas transfer velocities at the air‐water free surface
Bibliographic record
Abstract
A comparison study of the experimental and theoretical transfer velocities of heat ands gas transfer at a wavy air‐water interface is undertaken using an active infrared technique and two gas tracers. Applying the surface renewal model formalism [ Danckwerts, 1951 ], we find that the experimentally evaluated heat transfer velocity is roughly a factor of 2 higher than the transfer velocity of a gas with a low solubility in water when both are referenced to Sc = 600. Potential origins of such a discrepancy are investigated and we propose the use of the random eddy model [ Harriott, 1962 ] to explain our results. The model is an extension of surface renewal to include the eddy approach distance as a new parameter. Numerical simulations of the random eddy model have been performed using a timescale evaluated from the Active Controlled Flux Technique (ACFT) and the characteristics of heat as well as the two gases used in the experiments (He and SF6). The simulation results show that the transfer velocities of two species, referenced to the same Schmidt number, are different and that their ratio depends on the average value of the approach distance and its distribution. The model as implemented in the present work also predicts changes in the Schmidt number exponent when the hydrodynamics conditions are varied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".