Magnetohydrodynamics Solver for a Two-Phase Free Surface Flow Developed in OpenFOAM
Bibliographic record
Abstract
A magnetohydrodynamics solver (“mhdCompressibleInterFoam”) has been developed for a compressible two-phase flow with a free surface by extending “compressibleInterFoam” solver within OpenFOAM suite. The primary goal is to develop a tool to simulate compression of magnetic fields in vacuum and simplified magnetized plasma targets by imploding rotating liquid metal liners in the context of a Magnetized Target Fusion (MTF) concept in pursuit by General Fusion Inc. At present, the solver is limited to axisymmetric problems and the magnetic field evolution is solved in terms of toroidal field component and poloidal flux functions. The solver has been validated and verified using a number of test cases for which analytical or other numerical solutions are provided. Those tests cases include: (i) compression of toroidal and poloidal magnetic fields in vacuum and cylindrical geometry, (ii) axisymmetric annular Hartmann flow, and (iii) compression of magnetized target initialized with a Grad–Shafranov equilibrium state in a cylindrical geometry. A methodology to incorporate conductive solid regions into simulation has also been developed. Capability of the code is demonstrated by simulating a complex case of compressing a magnetized target, which is injected during implosion of a rotating liquid metal liner with an initially soaked poloidal magnetic field. An application of the solver to simulate compression of a magnetized target in a geometry and parameters relevant to the Fusion Demonstration Plant (FDP) being developed by General Fusion Inc. is also demonstrated.
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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 | 0.001 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".