Local Heat Transfer Measurements in Micro Geometries Using Liquid Crystal Thermography
Bibliographic record
Abstract
A technique is described on the use of un-encapsulated thermochromic liquid crystals (TLC’s) to measure the local heat transfer coefficient in microchannel geometries. Microchannel heat transfer is at the heart of the microchannel heat sink, a recent technology aimed at managing the stringent thermal requirements of today’s high-end electronics. The microencapsulated form of liquid crystals are well established for use in surface temperature mapping. Limited studies however are available on the use of the un-encapsulated form. This form is advantageous as it offers the potential for high spatial resolution which is necessary for micro geometries. The evaluation of this method and its associated difficulties is therefore the motivation for the experimental facility developed and described in the present work. Measurements are made in a closed loop facility combined with a microscopic imaging system and automated data acquisition. Results are presented for a circular tube made of stainless steel with an inner diameter of 1.0668mm. A localized TLC calibration is used to account for non-uniformities in the coating and variation of lighting conditions. Results for single-phase, thermally developing, laminar and turbulent flows using distilled water are presented. The results show that the correlations for conventional size channels are adequate for predicting the heat transfer characteristics of a nominally sized 1 mm channel.© 2005 ASME
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".