CFD APPROACH FOR MODELING HIGH AND LOW COMBUSTION IN A NATURAL DRAFT RESIDENTIAL WOOD LOG STOVE
Bibliographic record
Abstract
In this work, a comprehensive CFD model was developed for pyrolysis to provide significant emission reductions.In wood stoves with natural draft, the airflow is driven by buoyancy to overcome the resistance to flow within the stove.This combustion model is tested to reproduce at best the physical and chemical phenomena taking place in a natural draft wood log stove for low power (18 kW) residential heating.The RANS approach was used to solve the aerothermodynami c equations.A body fitted hexahedron mesh was adopted and the k-ε turbulence model was used to ensure closure of the Navier-Stokes equations.The chemical reaction for combustion was modeled using species transport and rate of species production formulated as the EDDY DISSIPATION CONCEPT.The key parameters for validation are based on temperature inside the combustion chamber at the center plane and the [CO]/[CO2] ratio for emissions at the exit plane.Therefore, the developed modelling approach can be used for engineering analysis and optimization of existing stoves and for relatively-quick evaluations of new stove designs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".