Mathematical Modelling of Smoke-Filling Dynamics in Compartment Fires: A Two-Zone Approach
Bibliographic record
Abstract
A novel two-zone model has been developed to predict the smoke layer height in compartments under pre-flashover fire conditions.This model is premised on the resolution of a mass balance in the upper layer of the compartment, employing the MQH correlation to approximate upper layer temperature.Furthermore, Zukoski's plume entrainment model is utilized to derive the mass flow rate of gases.This model aims to enhance existing methodologies for estimating smoke layer height, such as the widely recognized Zukoski's model, by accommodating vertical openings in the compartment walls and necessitating the resolution of a single differential equation.To validate its efficacy, the smoke layer heights generated by this model under varying fire scenarios were contrasted with those produced by a more intricate two-zone model (which resolves multiple differential equations) and with experimental data documented in the literature.Across all tested scenarios, the proposed model demonstrated considerable concordance with both aforementioned benchmarks.Notably, even in compartments with diverse useful areas and types of openings (window/door), the results procured using the proposed model displayed significant accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".