Experimental thermal time estimation for Mach‐Zehnder interferometer to study coupled heat and mass transfer
Bibliographic record
Abstract
In the present investigation, a theoretical approach and experimental models are discussed for estimating the experimental thermal time of an optical digital interferometry experiment. The investigation deals with coupling between heat and mass transfer phenomena. Mach‐Zehnder Interferometry has been found to be an accurate and precise experimental tool for visualizing the thermodiffusion phenomenon inside a parallelepiped cavity, when a thermal gradient is applied to its two opposite sides. Processing the results of this experiment requires distinguishing the two experimental phases: 1) refractive index changes due to change of temperature and 2) refractive index changes due to the separation of the components. We can separate these two phases according to the thermal time of the liquid mixture. In previous studies L2/χ (L = distance between hot and cold sides, χ = thermal diffusivity of the mixture) was used as the thermal time of the liquid. However, due to a high separation rate at the beginning of the thermodiffusion process, an accurate estimation of the thermal time can significantly affect the final result of the interferometry measurements. Here, a theoretical approach and an experimental method were developed to estimate the proper thermal time for this experiment. We also discuss the importance of precisely estimating the thermal time on the experimental results. This study shows that assuming thermal time equals L2/χ may cause noticeable underestimation when measuring the Soret coefficients and maximum separation values.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".