Influence of the swirling jet on pulverized coal gasification performance based on CFD simulation
Bibliographic record
Abstract
Abstract In order to study the influence of the swirling jet of the gasification agent on coal gasification performance, the gasification process of Ningdong pulverized coal inside a GSP gasifier (2000 t/d) was simulated under hot conditions, and the results were compared with actual industrial parameters. A three‐dimensional steady multi‐phase turbulent flow model was established by ANSYS Fluent. The coupling effect of turbulent chemistry was considered by Species Transport model. The realizable k‐ϵ model was used for the gas phase flow. The P‐1 model was implemented for radiation heat transfer. The heterogeneous reactions were irreversible surface reactions and the gas phase reactions were to reach equilibrium. The numerical results showed detailed information of flow field under direct jet and swirling jet. It indicated the swirling jet had a significant influence on coal gasification performance by analyzing the characteristics of flame, species, and gas‐solid distribution. In other words, the swirling jet promotes the reaction efficiency between coal particles and gas phase, increases the residence time of pulverized coal, and temperature and species concentration distribution are more well‐distributed. Furthermore, this work can better predict coal gasification behaviour under different conditions, so as to provide the theoretic support for subsequent sulphur removal and optimal design of the gasifier.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".