Bibliographic record
Abstract
Magnetohydrodynamics (MHD) studies the dynamics of an electrically conducting fluid under the influence of a magnetic field.\nMany astrophysical phenomena are related to MHD,\nand computer simulations are used to model these dynamics.\nIn this thesis,\nwe conduct MHD simulations of non-radiative black hole accretion as well as fast magnetic reconnection.\nBy performing large scale three dimensional parallel MHD simulations on supercomputers and using a deformed-mesh algorithm,\nwe were able to conduct very high dynamical range simulations of black hole accretion of Sgr A* at the Galactic Center.\nWe find a generic set of solutions,\nand make specific predictions for currently feasible observations of rotation measure (RM).\nThe magnetized accretion flow is subsonic and lacks outward convection flux,\nmaking the accretion rate very small and having a density slope of around $-1$.\nThere is no tendency for the flows to become rotationally supported,\nand the slow time variability of the RM is a key quantitative signature of this accretion flow.\n\nWe also provide a constructive numerical example of fast magnetic reconnection in a three-dimensional periodic box.\nReconnection is initiated by a strong,\nlocalized perturbation to the field lines and the solution is intrinsically three-dimensional.\nApproximately $30\\%$ of the magnetic energy is released in an event which lasts about one Alfv\\'en time,\nbut only after a delay during which the field lines evolve into a critical configuration.\nIn the co-moving frame of the reconnection regions,\nreconnection occurs through an X-like point,\nanalogous to the Petschek reconnection.\nThe dynamics appear to be driven by global flows rather than local processes.\n\nIn addition to issues pertaining to physics,\nwe present results on the acceleration of MHD simulations using heterogeneous computing systems \\cite{shan2006heterogeneous}.\nWe have implemented the MHD code on a variety of heterogeneous and multi-core architectures (multi-core x86, Cell, Nvidia and ATI GPU) using different languages (FORTRAN, C, Cell, CUDA and OpenCL).\nInitial performance results for these systems are presented,\nand we conclude that substantial gains in performance over traditional systems are possible.\nIn particular,\nit is possible to extract a greater percentage of peak theoretical performance from some heterogeneous systems when compared to x86 architectures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".