Heat and mass transfer across the vapor–liquid interface: A comparison of molecular dynamics and the Enskog–Vlasov kinetic model
Bibliographic record
Abstract
Due to the intricacies of the interface between vapor and liquid, evaporation and condensation processes are not fully understood. The small spatial extent of the interface renders experimental studies on this subject challenging so that computational investigations are indispensable. For two heat and mass transfer scenarios across a vapor–liquid interface, molecular dynamics simulation is compared with the direct simulation Monte Carlo solution of the Enskog–Vlasov kinetic equation . A heat flux from the vapor to the liquid in a closed system as well as classical evaporation into an open half-space are considered. In both scenarios, temperature and one-dimensional driving gradients are widely varied, sampling systems containing 5 ⋅ 1 0 5 molecules. Since the two simulation methods rest on different potential models for the molecular interactions , a meaningful transformation between the truncated and shifted Lennard-Jones fluid and the Sutherland fluid is proposed. Spatially resolved density, temperature and velocity profiles from these simulation methods are consistent, except for the interface width. Consequently, particle flux and downstream pressure match as well. The good agreement between the results reinforces the validity of these approaches. The study is accompanied by successful comparisons of these simulations to kinetic gas theory with respect to macroscopic property variations at the interface.
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) | 0.001 | 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".