Finite Volume Simulation of Gaseous Microflows Using Modified Boundary Conditions
Bibliographic record
Abstract
The rapid progress in fabricating and utilizing micro-electromechanical systems during the last decade has not been matched by the corresponding advances in our understanding from the unconventional physics involved in manufacturing and operation of micro devices. To avoid the complexity encountered in modeling of nonlinear Boltzmann equations, the Navier-Stokes equations can be solved considering the slip flow regime concepts. The modeling can be achieved via employing suitable slip velocity boundary conditions at the solid walls. The modified first-order slip models can, in some cases, extend the range of applicability of the Navier-Stokes solvers to around and beyond Kn=0.1, where the accuracy of firstorder slip models starts deteriorating. To extend the capabilities of an already developed macro-scale Navier-Stokes solver to microflow analysis, we benefit from using a modified first-order slip boundary condition. The basic numerical approach is a finite-volume method, which incorporates the major advantages of finiteelement method as well. Eventually, we present an extensive comparison between the first and higher-order slip boundary models of the regular Navier-Stokes solver for hard sphere gases. The achieved results indicate that the use of high-order slip models in solid boundaries can vigorously increase the capabilities of the large scale Navier-Stokes solvers to predict micro flows with large Knudsen magnitudes confidently.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".