Bibliographic record
Abstract
Vented Suppressive shield (VSS) containers have traditionally been used for the storage of hazardous materials, especially explosives.The role of VSS containers is to attenuate the blast pressure and impulse outside the container and to eliminate primary fragment hazard associated with accidental explosions.Most VSS containers are typically designed from experience and observations of previous container testing programs or lessons learned from previous accidents.Another method that is used to analyze and design VSS structures is using computational fluid dynamic (CFD) software packages such as AUTODYN that are expensive, has long computational time as well as requires special expertise to get the best use of it.The aim of this study is to develop a reliable design methodology that may be used to design the elements of the VSS containers without the need of using CFD software packages.The study begins with defining the vent area ratio for the studied VSS sections.AUTODYN was used to calculate the pressure outside several VSS containers with different cases.The obtained data were utilized to develop a set of equations to predict the pressure and impulse outside the container.The pressure values obtained from the equations showed a good correlation with the results obtained from previous experimental results.The second part of the thesis was studying the pressure profile on the elements of the VSS containers.The pressure profiles on the side wall of different VSS sections were studied using 2D AUTODYN models.Some modifications were made to the Friedlander's waveform equation in order to take into account the effect of internal explosion.The single layer plate was chosen as a control configuration because of the simplicity of its section and geometric coefficients were introduced in order to take into account the effect of different VSS geometric sections.The pressure profiles obtained from the developed equations showed a good correlation with those obtained from AUTODYN.Finally, a single degree of freedom (SDOF) model was developed to study the structural response of the VSS elements due to the applied blast loading.The SDOF model was able to predict the structural behaviour of the steel VSS elements.The results were compared with those obtained from AUTODYN software and a good correlation was found between the two responses.Key = -1 (Plastic in compression) Key = 2 (Large deformation occur to the element; membrane action takes place) Key = 3 (Failure)
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".