Local Heat Transfer Measurements on a Curved Microsurface Using Liquid Crystal Thermography
Bibliographic record
Abstract
In this paper, local heat transfer measurements in circular mini- and microchannels are presented through the use of unencapsulated thermochromic liquid crystals for local wall temperature measurement Microchannel heat transfer is fundamental to the design of a number of novel technologies in development for thermal control. Measurements are carried out in three different stainless steel tubes with nominal inner diameters of 1.07, 0.51, and 0.25 mm. The working fluids are distilled water and FC-72. A unique localized calibration of the thermochromic liquid crystal material is employed to minimize the effects of lighting nonuniformity and the effect of variable coating thickness. Local heat transfer and frictional pressure drop measurements are presented for the laminar, transitional, and turbulent flow regimes. The results are compared with correlations for both macro- and microchannels in the turbulent regime and show that, considering experimental uncertainty, both correlations are adequate for the experimental range investigated. Overall, the methods developed in this work demonstrate that the unencapsulated forms of thermochromic liquid crystals are a viable approach for temperature measurement in microheat transfer experiments.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".