Modelling interplanetary CMEs using magnetohydrodynamic simulations
Bibliographic record
Abstract
Abstract. The dynamics of Interplanetary Coronal Mass Ejections (ICMEs) are discussed from the viewpoint of numerical modelling. Hydrodynamic models are shown to give a good zero-order picture of the plasma properties of ICMEs, but they cannot model the important magnetic field effects. Results from MHD simulations are shown for a number of cases of interest. It is demonstrated that the strong interaction of the ICME with the solar wind leads to the ICME and solar wind velocities being close to each other at 1 AU, despite their having very different speeds near the Sun. It is also pointed out that this interaction leads to a distortion of the ICME geometry, making cylindrical symmetry a dubious assumption for the CME field at 1 AU. In the presence of a significant solar wind magnetic field, the magnetic fields of the ICME and solar wind can reconnect with each other, leading to an ICME that has solar wind-like field lines. This effect is especially important when an ICME with the right sense of rotation propagates down the heliospheric current sheet. It is also noted that a lack of knowledge of the coronal magnetic field makes such simulations of little use in space weather forecasts that require knowledge of the ICME magnetic field strength.Key words. Interplanetary physics (interplanetary magnetic fields) Solar physics, astrophysics, and astronomy (flares and mass ejections) Space plasma physics (numerical simulation studies)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".