A parallel adaptive mesh refinement scheme for hypersonic flows with an equilibrium high-temperature equation of state
Bibliographic record
Abstract
An explicit parallel adaptive mesh refinement (AMR) scheme is proposed and developed for the solution of the partial differential equations governing two-dimensional hypersonic turbulent flows in conjunction with a high temperature equilibrium equation of state and the k-ω turbulence model. A finite-volume spatial discretization procedure is applied to the conservative form of the five governing equations: continuity, momentum and energy with two equations for the turbulence model, on structured body-fitted quadrilateral meshes. Limited piecewise-linear solution reconstruction with various approximate Riemann solvers modified to account for the high temperature equation of state is used in the numerical evaluation of the inviscid fluxes. The gradients for evaluating the viscous fluxes are calculated using centrally-weighted diamond path reconstruction. A block-based AMR scheme is used that allows for local anisotropic refinement of the grid and efficient parallel implementation via domain decomposition. The system of non-linear ordinary differential equations resulting from the finite volume discretization of steady-state boundary value problems is solved using explicit time marching methods with multigrid acceleration. Numerical results are presented and discussed for flows having Mach numbers in the range M<8. The results demonstrate the validity of the equilibrium high-temperature equation of state and the computational efficiency of the parallel explicit AMR schemes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".