Designing a Comparative Interferometric Method for Measuring the Thermal Conductivity of Transparent Fluids
Bibliographic record
Abstract
In this paper, a comparative interferometric method is designed to measure the relative thermal conductivity of transparent fluids compared to deionized water by examining temperature fields in both fluids, separated by a thin conductive barrier.The flow and temperature fields in the experimental model were numerically simulated using Ansys Fluent 2023 R1.To minimize natural convection effects, the model was heated from the top and cooled from the bottom.The impact of natural convection within the cavities was investigated by simulating the model with and without considering the natural convection effects.Moreover, various sources of potential experimental errors, such as heat loss to the ambient and imperfect levelling (±0.5 degrees) were examined.The simulated interference fringes (lines of constant beam-averaged temperature) were reconstructed in the numerical model for each case.Subsequently, the simulated fringes were analysed to obtain the temperature gradient in both fluids and the relative thermal conductivity of the test fluid.It was shown that the impact of natural convection on the results is negligible and can be disregarded.Furthermore, all the mentioned error sources lead to less than a 0.2% error in the measured relative thermal conductivity.A sample infinite fringe interferogram from the experimental model is presented for deionized water as both the test and reference fluids.This new comparative optical method will be ultimately used to measure the relative thermal conductivity of nanofluids in support of a program of optical convective heat transfer research.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".