Bibliographic record
Abstract
The use of single flush-mounted thin-films for thermal sensing of wall shear stress fluctuations in turbulent flows has seen a decline, in spite of their non-intrusiveness, and the availability of microfabrication technologies to create very small films. The limitations of such single-element sensors are quite severe—their spatial resolution is not determined by their size alone, but modified by substrate heat conduction which creates variations in the effective sensor size (heat exchange area), dependent on strength and timescale of the fluctuations. Here a two-element design is investigated—with the hot-film sensor element surrounded by an electrically isolated guard heater film maintained at the same temperature as the sensor, but controlled by a separate anemometer circuit. Numerical studies are used to examine such guard heater designs over a range of shear stress values. The results show that if the sensor film center-location is biased towards the downstream end (75% and 65% of guard-heater length for water and air, respectively), with an appropriately-sized guard heater, 95% of the total heat generated in the sensing film can be transferred directly to the fluid, for strong turbulent fluctuations (Peclet number Pe > 8000) when the working fluid is water.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".