Hydrodynamic analysis of gas‐liquid‐liquid‐solid reactors using the XDEM numerical approach
Bibliographic record
Abstract
Abstract Multiphase reactors are abundantly used in many industries. Among them, few reactors deal with four phases called gas‐liquid‐liquid‐solid systems, which receive less attention due to their complex situation. Numerical study of such complex systems is not easy and requires large computational effort. In this study, a discrete‐continuous numerical model known as the eXtended discrete element method (XDEM) is proposed to investigate the hydrodynamic behaviour of fluid phases passing through the packed bed of solid particles. This model is applied to the dripping zone of a blast furnace. In this zone, two distinct liquid phases, namely liquid iron and slag, flow through a pile of coke particles while exchanging momentum. In this work, besides the solid‐fluid and gas‐liquid interactions, the liquid‐liquid interactions are also studied and the phases’ mutual effects are discussed. In addition, a sensitivity study on the slag viscosity is performed, which shows the importance of liquid phase properties on the system behaviour. The results evaluation shows that the liquid iron accelerates the downward flow of the slag and the slag decelerates the downward flow of the liquid iron phase due to the resistance force caused by their relative velocity.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".