Direct Simulation Monte Carlo Methods for Hypersonic Flows with Automatic Mesh Optimization
Bibliographic record
Abstract
High-Altitude Low-Orbit 3D (HALO3D) is a comprehensive multidisciplinary software system being developed by the current authors to simulate flowfields around hypersonic aircraft whose flightpath spans low (continuum) to high (rarefied) altitudes. This paper presents a methodology for coupling HALO3D’s particle-based rarefied flow module, HALO3D–Direct Simulation Monte Carlo (HALO3D-DSMC), with a solution-driven edge-based automatic mesh optimization algorithm, OptiGrid. The paper studies the choice of optimization scalars and constraints for DSMC solvers, an aspect believed to be currently lacking in the literature. Three optimization constraints are used: minimum and maximum edge lengths and a target number of nodes/cells. Mesh optimization is conducted for Bird’s leading-edge case and flows over two- and three-dimensional cylinder geometries for freestream Knudsen numbers ranging from 0.01 to 0.047. An adaptation scalar set combining flow variables such as density, velocity components, modal temperatures, pressure, and Mach number produces an unstructured collisional-sampling mesh that greatly improves the quality of the solution without necessarily increasing mesh size. The solutions represented by the optimal meshes are smooth and free of irregularities, with salient flow features being captured well. In addition, the coupled system can simulate complex geometries and multiscale flow features with arbitrarily generated initial grids.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".