Numerical evaluation of a one‐dimensional two‐fluid model applied to gas–solid cold‐flows in fluidised beds
Bibliographic record
Abstract
Abstract Computational demanding two‐ and three‐dimensional two‐fluid models are frequently adopted simulating gas–solid flows in fluidised beds. Reduced computational cost is favourable for efficient numerical studies of technologies such as the novel chemical looping combustion (CLC), chemical looping reforming (CLR), and sorption‐enhanced steam methane reforming (SE‐SMR) processes. In this study, we elucidate the potential of a one‐dimensional two‐fluid model to describe gas–solid cold‐flows in fluidised beds. The validity of the numerical simulation results of the bubbling beds and risers have been compared to experimental data in the literature. Moreover, sensitivity analyses on drag closure laws and operation condition have been performed and a number of model solution techniques and algorithms are studied. In addition, simulation results of the one‐dimensional model are compared to results of a two‐dimensional model. For particular sets of operating conditions and flow characteristics, the one‐dimensional model compares fairly well to the simulation results of the two‐dimensional model and to experimental data. Under other operating conditions, large quantitative deviations can be observed. However, the one‐dimensional model is assumed to be sufficently accurate for particular reactor process optimisation and design evaluations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".