THE ROLE OF THE VELOCITY DISTRIBUTION IN THE DSMC PRESSURE BOUNDARY CONDITION FOR GAS MIXTURES
Bibliographic record
Abstract
A prescribed pressure is the most common flow boundary condition used in flow simulations. In the Direct Simulation Monte Carlo (DSMC) method, boundary pressure is controlled by the number flux of the simulating molecules entering the domain. In the conventional DSMC algorithm, this number flux is calculated iteratively using sampled values of velocity and number density by means of an expression derived from the Maxwell distribution function. It is known that this procedure does not work well for low speed flows which are of interest in most micro-flow applications and the statistical scatter of the DSMC results is generally stated to be the main reason. However, the Maxwell distribution used in the pressure boundary treatment is valid for equilibrium conditions, and therefore, current implementations of the DSMC pressure boundary treatment are limited to boundaries with sufficiently small rarefaction effects. This is not the case for some practical problems in which highly rarefied flows through the boundaries lead to considerable nonequilibrium effects. In this study, an expression for the species number flux is derived using the Chapman–Enskog velocity distribution to improve the pressure boundary condition. The resulting algorithm is then used for modeling a micro-channel binary gas mixture flow with prescribed pressure boundary conditions.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".