Influence of Vibration on Diffusion in Liquid Mixtures On-Board the International Space Station
Bibliographic record
Abstract
Experiments on-board the International Space Station experience a convective flow due to the oscillatory g-jitters induced by several sources such as crew activities, mechanical systems, thrusters firing, spacecraft docking, etc. Although g-jitter seems to have a major impact on diffusion-related experiments in Space, very few experimental studies have addressed this topic. This study examined the effect of oscillatory g-jitters on transport processes (fluid flow, heat transfer and mass transfer). Cubic rigid cells filled with water and isopropanol at different concentrations were subjected to thermal gradients and forced vibrations. The cells were exposed to different levels of vibration in terms of frequency and amplitude, which were applied perpendicular to the temperature gradient. The full transient Navier Stokes equations coupled with the mass and heat transfer formulas were solved numerically using the control volume technique. The physical properties of the fluid mixture such as the density were determined using two different models. The effect of different levels of vibration on the flow was analysed and the results were compared in a benchmark study with other scientific groups. The effect of the diffusion coefficients variation and other physical properties on the temperature and concentration distribution was compared to those results obtained with constant diffusion coefficients. Results show that use of variable physical properties in the modelling produces different flow patterns and component concentration. By examining different flow patterns, it was found that the effect of using variable coefficients is much more significant in the cases with high Rayleigh vibration that result in strong flow when compared with numerical analysis using constant variables. The numerical analysis was also performed for the actual experiment on board the International Space Station. The same trend was seen for both the numerical and experimental results. However, the separation of components was higher in the experiment in comparison with the numerical analysis. This was discussed in detail for various scenarios in terms of the applied frequency and amplitude. Recommendations are made according to the findings from this study for the improvement of accuracy in the numerical and experimental analyses of future diffusion experiments in Space.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".