Out-of-core Hydrodynamic Simulations of Cosmological Structure Formation
Bibliographic record
Abstract
Astrophysical and cosmological structure formation are challenging problems because they involve dynamical and hydrodynamical processes that can span a large range in scale, mass, and energy. Hydrodynamic and N-body simulations are powerful tools with which to solve the nonlinear physics, and their continuing development and application is the focus of this thesis. I present a new approach to Eulerian computational fluid dynamics that is designed to work at high Mach numbers encountered in astrophysical simulations. The Eulerian conservation equations are solved in an adaptive frame moving with the fluid where Mach numbers are minimized. The Moving Frame code separately tracks local and bulk flow components, allowing thermodynamic variables to be accurately calculated in both subsonic and supersonic fluid. An out-of-core hydrodynamic code has been developed for high resolution cosmological sim-ulations. Out-of-core computation refers to the technique of using disk space as virtual memory and transferring data in and out of main memory at high I/O bandwidth. The code is based on a two-level mesh scheme where short-range physics is solved on a high-resolution, localized mesh while long-range physics is captured on a lower resolution, global mesh. This thesis includes the first astrophysical application of Eulerian hydrodynamic simulations to
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".