Numerical simulations towards optimizing the burner design for efficient staged pressurized oxy-coal combustion.
Bibliographic record
Abstract
Staged pressurized oxy-coal combustion (SPOC) – a technology developed at Washington University at St. Louis (WUSTL) – is nowadays a promising tool for low-cost/emissions, high-efficiency power generation. The present computational simulations, performed at West Virginia University (WVU) by means of the ANSYS Fluent package, compliment the experimental research for better understanding of the impacts of turbulence, fluid flow, flame/particle dynamics, and heat transfer on oxy-coal combustion. Here, CO2 is injected alongside the coal, for its carriage, while a small amount of CH4 is also injected alongside the coal to maintain a steady flame. Out of 100 kW in a lab-scale reactor, 10 kW originates from CH4-air burning and 90 kW comes from coal combustion. Steady and unsteady RANS simulations are performed resulting in an asymmetric flame shape. Three causes of the flame asymmetry are hypothesized: (i) coal injection, (ii) onset of the shear-layer instability due to various stream velocities and densities in a shear layer, where mixing occurs, and (iii) presence of the vortex shedding due to the flow past a bluff body, which is used to stabilize the flame. As a result, an impact of the presence of coal on the flame symmetry is demonstrated, and a benchmark at which further increase of coal would result in flame asymmetry is found. The present simulations also shows occurrence of the vortex shedding when the flow passes over a disk in the reactor used to stabilize the flame, constituting a potential reason for the periodic flame oscillations experienced in the result.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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".