Application of Gaussian Moment Closure to Microscale Flows with Moving Embedded Boundaries
Bibliographic record
Abstract
The application of the Gaussian moment closure to continuum and microscale flows with embedded, and possibly moving, boundaries is considered. The Gaussian moment closure is briefly reviewed, as is an extension that allows for the treatment of flow of diatomic gases. A parallel upwind, finite volume scheme with adaptive mesh refinement using a Roe-type numerical flux function is described for solving the hyperbolic system of partial differential equations arising from this closure on multiblock meshes with embedded and possibly moving boundaries. The purely hyperbolic nature of moment equations makes them particularly insensitive to discretizations involving grids with irregularities. Typical of adaptive mesh-refinement, embedded-boundary, and Cartesian cut-cell treatments, mesh irregularities are difficult to deal with when second derivatives are required by the physical model. Such is the case for the Navier–Stokes equations. Numerical solutions to mathematical descriptions involving second derivatives show significantly degraded solution quality as compared to solutions of first-order quasi-linear moment equations. Solid-wall boundary conditions are implemented via a Knudsen-layer approximation. Comparisons are made between numerical solutions of the Gaussian model on both body-fitted meshes and meshes with embedded boundaries, as well as to experimental and approximate analytic results for a variety of flow problems. The benefits and potential of the proposed approach for unsteady microscale flow applications having complex geometries are clearly demonstrated.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".