Effect of non-uniform nanofluid concentration on interferometric heat transfer measurements
Bibliographic record
Abstract
The effect of non-uniform nanofluid concentration on the accuracy of interferometric heat transfer measurements has been investigated using a Mach-Zehnder interferometer. Because the refractive index is a function of concentration as well as temperature, concentration variations within the nanofluid can produce unwanted interference fringes, leading to temperature measurement errors. Measurement errors in the temperature gradient are demonstrated for conduction within a cavity heated from top to bottom, filled with an Al 2 O 3 -water nanofluid (0.16 wt%) produced using a standard two-step method. The results of the current measurements show that the temperature gradient can be overestimated by up to 100 % due to near-wall concentration gradients in an unstable nanofluid. The measurement problem is delineated, and several approaches to mitigate this source of measurement error are outlined. The technical trade-offs associated with designing interferometric heat transfer experiments to reduce the sensitivity to concentration differences are discussed. These trade-offs include selecting the nanofluid type and concentration, temperature differences, and the optical path length of the experimental model. It is shown that many interferometric studies in the literature were much more sensitive to concentration-induced errors than the current experiment. An isothermal stability test is recommended to detect nanofluid concentration gradients prior to temperature-based interferometry experiments. • Homogeneous nanofluids are critical for temperature-based interferometry. • Unstable nanofluids can cause large heat flux measurement errors. • A concentration sensitivity parameter is proposed. • Mitigation of concentration errors in interferometry is discussed.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".