A New Subgrid Model for the Heat and Mass Transfer Between a Hot Gas and Char Particles in Dense-Bed Reactors
Bibliographic record
Abstract
This work is devoted to the development and verification of a new intrinsic-based subgrid model for moving char particles gasifying in a hot flue gas or syngas environments consisting of CO2/H2O/CO species. The distinguishing feature of our model relative to the submodels published in the literature is that it takes into account the thermal and chemical nonequilibrium between the particle's surface and its center. Thus, our model is able to predict temperature and species gradients inside the particles. The main focus of the new submodel is to demonstrate the crucial role of intrinsic-based heterogeneous reactions in the adequate prediction of carbon conversion rates for char particles gasification in fixed-bed and fluidized-bed gasifiers. The new model is verified against steady-state, particle-resolved computational fluid dynamics (CFD)-based, three-dimensional simulations carried out for different volume fractions of solid phase in a control volume (CV). Acceptable agreement has been demonstrated. Finally, to demonstrate our new model's predictions, we carried out several unsteady simulations for different ambient temperatures and Reynolds numbers. The importance of simultaneous change of char porosity and particles size during gasification has been demonstrated for different regimes indicated by the Damköhler numbers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".