Grid Optimization for the Full-Scale Test Facility to Evaluate the Fire Performance of Houses - Part 1: Basement Fire
Bibliographic record
Abstract
In the event of a house fire, the occupants may be harmed by the untenableconditions that may be develop during the fire. The time to untenable conditions can be estimated using experimental studies or numerical simulations. Experimental studies usually provide realistic information but are expensive and time consuming. Numerical simulations, using validated models, can therefore be used to overcome these drawbacks and may also be used to help in the design of experiments. As part of a research project to evaluate life safety in houses, the Fire Risk Management Program at IRC/NRC has carried out numerical simulations to study the fire performance of houses. The numerical simulations were conducted using the Fire Dynamics Simulator (FDS)1, a CFD model developed by the US. National Institute of Standards and Technology (NIST). As a first step, the effect of the CFD grid sizes on the simulation results of the house fire was investigated in order to determine an optimum grid size that could be adopted for future simulations. Several fire sizes were investigated and the optimum grid resolution was found. The chosen grid resolution was then used to determine the time when conditions would become untenable, based on existing criteria from the literature. This report presents the details of the grid resolution analysis study as well as an evaluation of life safety in houses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".