Estimating an Enclosure Temperature During Solid Propellant Fires
Bibliographic record
Abstract
In any industrial energetic facility, it is useful to predict the effect of an accidental fire. Solid propellants are designed to quickly transform into high temperature gases. It is therefore necessary to predict the pressure evolution during a confined fire. Some methods are semi-empirical and thus do not depend on many thermodynamic variables. Other methods are numerical and require that many variables be defined. In any safety application, choosing the right method will depend on the risks involved, possible consequences and means available. Lack of precise knowledge of these variables is often what discourages facilities and equipment designers from modelling the behaviour of their systems. One such variable is the temperature in an enclosure during a fire for which the pressure generation is the main concern. For events involving low charge densities, using the flame temperature is an over-approximation. Assuming instantaneous perfect mixing of the enclosure air and combustion gases results in an under-approximation. In this paper, these approximations will first be considered compared to an event sequence. Three temperature models will be reviewed. Firstly, a simple model will consider the geometric mean of the previously described extreme values. Secondly, the radiative heating of the enclosure air due to a localized fire source will be studied. Finally, a finite volume multidimensional numerical model which includes turbulence effects will be presented. These three methods are seen to be of increasing complexity and will be compared to the event timescale. This timescale will help indicate if the level of complexity is warranted.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".