Thermodiffusion in a Binary Fluid Mixture Subject to External Vibrations: Effect of Variable Physical Properties
Bibliographic record
Abstract
A microgravity environment is essential for studying the phenomenon of thermodiffusion in order to suppress the microscopic flows in the mixture. It is, however, noted that the residual micro accelerations (g-jitters) in the space laboratories is produced by several sources such as crew activities, mechanical systems, thrusters firing, spacecraft docking, etc. Such external forces lead to significant flows which can induce convection that may affect the accuracy of the experiment. Consequently, an appropriate interpretation of the space experimental results relies on theoretical and numerical studies of the g-jitter effect on the temperature and the concentration fields. In this paper, we have modeled the thermodiffusion experiment subjected to different levels of vibration when the steady gravity is assumed zero. A rectangular cavity that is subjected to a thermal gradient is filled with a binary mixture (water and isopropanol) and put under the influence of different levels of vibrations. The thermal gradient is applied perpendicular to the vibration. All physical properties including density, mass diffusion and thermodiffusion coefficients are assumed variable as function of temperature and concentration using PC-SAFT equation of state. It is found that using variable physical properties including density and diffusion coefficients make the results more realistic in comparison with the constant model especially in cases with higher Rayleigh vibrations.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".