Ellipsoidal statistical Bhatnagar–Gross–Krook model with velocity-dependent collision frequency
Bibliographic record
Abstract
In this paper, an ellipsoidal statistical (ES) Bhatnagar–Gross–Krook (BGK)-type kinetic model with velocity-dependent collision frequency is proposed and further numerically tested for one-dimensional shock waves and planar Couette flow at steady state for hard sphere molecules. In this new kinetic model, a physically meaningful expression for the velocity-dependent collision frequency derived from the Boltzmann equation is used, while the important properties for a kinetic model are retained at the same time. This kinetic model can be simplified to the classical ES-BGK model and the BGK model with velocity-dependent collision frequency for suitable choices of parameters. The H theorem for this new kinetic model has so far been proven only for small Knudsen numbers. The numerical method used here for kinetic models is based on Mieussens’s discrete velocity model [L. Mieussens, J. Comput. Phys. 162, 429 (2000)]. Computational results from the kinetic models (including the BGK model, the ES-BGK model, the BGK model with velocity-dependent collision frequency, and this new kinetic model) are compared to results obtained from the direct simulation Monte Carlo (DSMC) method. It is found that results obtained from this new kinetic model lie in between results from the ES-BGK model and results from the BGK model with velocity-dependent collision frequency. For one-dimensional shock waves, results from this new kinetic model fit best with results from the DSMC, while for planar Couette flow, the classical ES-BGK model is suggested.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".