Reynolds-Averaged Navier-Stokes (RANS) simulation of a staged pressurized oxy-coal combustion (SPOC)
Bibliographic record
Abstract
Staged pressurized oxy-coal combustion (SPOC), which is a promising technology being developed at the Washington University at St. Louis (WUSTL) aiming to be used for low-cost, low-emission, high-efficiency power generation, is investigated numerically using the ANSYS Fluent commercial package. The modelling supports the WUSTL experimental endeavors in a 100 kW lab-scale reactor, with 90% of energy coming from the coal and 10% originating from methane-air combustion. To be specific about the burner design, carbon dioxide is injected alongside the coal, for its carriage. In addition, a small amount of methane is also injected alongside the coal to maintain a steady flame. Steady and unsteady Reynolds-Averaged Navier-Stokes (RANS) simulations are performed resulting in an asymmetric flame shape. We propose several potential causes of such a flame asymmetry including peculiarities of the coal injection and the onset of the shear-layer instability occurring because the densities and velocities of the mixing streams. As a result, an influence of the presence of coal on the flame symmetry is demonstrated. In addition, a benchmark at which the further increase of coal would result in flame asymmetry has been found.This work is performed in cooperation with the Washington University at St. Louis (WUSTL). It is sponsored by the US Department of Energy (DoE) through the US-China Clean Energy Research Center – Advanced Coal Technology Consortium (CERC-ACTC).
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".