Numerical Investigation of the Capture Efficiency of a Domestic Range Hood
Bibliographic record
Abstract
Abstract Comfort criteria in building are increasing continuously. Multiple studies tend to use CFD approaches in order to predict and optimize the flow conditions accurately. One of the most relevant problematic is the extraction of the CO2 by kitchen hoods. In the present work, a new solver, based on a buoyancy Boussinesq solver and a turbulent passive scalar transport equation, is developed in OpenFoam to model the heated CO2 extraction by a rotating fan using the Multiple reference frame (MRF) approach. The new solver is first validated successfully against experimental results of a heated cavity and CO2 diffusion. An improvement of 30% in the capture efficiency is reached by increasing the extraction flow rate of the fan from 100 to 300 cfm. An extraction efficiency of 100% is observed for flow rates above 330 cfm. The novelty of this study is to present a new open-source solver able to model the rotation of the whole fan, CO2 transport and heat exchange simultaneously in steady state.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".