Temperature calculations in fire exposed structures with the use of adiabatic surface temperatures
Bibliographic record
Abstract
Fire safety engineering is offering more and more advanced methods including software for fire dynamic calculations. This thesis presents a method for using the results obtained by fire modelling using the code FDS (Fire Dynamic Simulator) as input to FEM calculations for predicting temperature in fire exposed structures. Prof. Wickström of SP - Swedish National Testing and Research Institute has introduced the concept of adiabatic surface temperature (AST) for calculating heat transfer to fire exposed structures. The AST is defined as the temperature of a surface which cannot absorb any energy. It may be calculated at any surface in the fire simulation software used in this thesis. The aim in this work is to see if the AST at the surface of a beam in one simulation gives satisfactory temperature result when used in a temperature calculation of beams with different dimensions. The work is divided into four parts. The first part is to validate the AST as a means for calculations of temperature in fire exposed structures. Using the AST in the calculations presented an almost perfect agreement with the temperature obtained directly from the CFD code. The second part is a comparison made to investigate whether AST calculated by FDS could be used in a FEM simulation to predict heat transfer to a fire exposed structure. As the third part some different simulations are run to compare the FEM temperature calculations of beams with AST from different simulations. The result showed that AST obtained in one fire scenario can be used for different beam sections. It is advised to use the technique with care since certain circumstances such as choice of convection coefficients and flame height can influence the results. The final part is to make an experimentally verifiable set up. This is made to be able to compare simulated results with tests to be made at SP and at ULC (Underwriters’ Laboratories of Canada).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".