Modeling on rapid ablation/sublimation from the surface of solid particles injected into thermal plasmas
Bibliographic record
Abstract
Summay form only give. Thermal plasmas are often used in materials processing technologies, e.g., plasma spray, powder spheroidization, etc. The raw materials are normally injected into the plasma in the form of powders, which are then accelerated, heated, melted and evaporated. Evaporation/sublimation affects the interaction between the materials and thermal plasmas. Furthermore, evaporation/sublimation modifies the gas flow and temperature fields around the surface of the particles. To understand these complex phenomena, it is of great importance to model the interactions including phase transition, between solid, gas and plasmas in a self- consistent manner. In this work, we have developed a computational model to study the evaporation/ablation of graphite particles immersed in a thermal plasma. Carbon particle sublimation injected into thermal plasmas was simulated using the CIP-CUP method, which is a unified solver for incompressible and compressible flows including different phases of matter. In addition, the volume of fluid (VOF) function was adopted to track the boundary between solid and gas. The gas flow and temperature fields were obtained during the sublimation.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".