Theoretical and numerical investigation of thermodiffusion (Soret effect) and thermosolutal convection
Bibliographic record
Abstract
Thermodiffusion is a mass diffusion phenomenon induced by a temperature gradient and has attracted increasing interests from theorists and experimentalists. In this work, the modelling of thermodiffusion in fluids is theoretically treated in the framework of non-equilibrium thermodynamics. The method developed in this work for thermodiffusion calculation have been incorporated into a computational fluid dynamics program to study the influence of the thermosolutal convection caused by residual gravity and g-jitter. The results have shown that the influence of microgravity environment would become stronger with an increase in the acceleration level, while at the same acceleration level, the effect would decrease at higher frequencies. For an isopropanol-water mixture under a typical microgravity condition for thermodiffusion measurement, the effect of residual gravity lower than 10--5G or the g-jitter components with an acceleration level of 10--4 G and frequencies higher than 0.0005 Hz may be negligible. The analysis in this work has indicated that under the expected microgravity environment on the International Space Station, the influence of g-jitter on the thermodiffusion measurement may be negligibly small for frequencies lower than 1 Hz. Based on four postulates in non-equilibrium thermodynamics, a non-equilibrium thermodynamic approach has been developed for thermodiffusion in associating fluid mixtures. Using the thermodynamic properties provided by the PC-SAFT Equation of State, this approach was used to evaluate the thermal diffusion factor alphaT in aqueous alkanol solutions, including methanol-water, ethanol-water and isopropanol-water mixtures. Using two adjustable parameters calculated from experimental data, the approach has shown a successful estimation of the sign change in the thermodiffusion factor for these mixtures, which was an unresolved problem in thermodiffusion research. In addition to the new model for thermodiffusion in associating mixtures, the current approaches for thermal diffusion estimation in multicomponent hydrocarbon mixtures have been evaluated by comparisons with recent experimental data. Three equations of state, including Peng-Robinson, volume translated Peng-Robinson and PC-SAFT were used in the calculation. The Firoozabadi model combined with vtPR or PC-SAFT was shown to be applicable to thermal diffusion estimation for hydrocarbon mixtures, while PC-SAFT combined with the Firoozabadi model would be a promising choice for mixtures other than hydrocarbons due to the wide applicability of PC-SAFT.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".