Design of Piping Systems for Accidental Explosion and Fire Events
Bibliographic record
Abstract
Abstract Piping systems constitute the most critical portion of process plants. Proper blast and fire design of critical piping systems improve safety and resiliency. Design of piping and pipe supports are typically governed by operating and abnormal load conditions depending on the design basis. Well established analysis and design methodologies as per the applicable ASME codes ensure performance of piping systems against load cases such as internal pressure, thermal expansion, self-weight, wind, seismic and vibration. Pipe stress analysis using code based linear elastic analysis methods allow design for these types of conventional load cases in a practical way. However, beyond design basis load cases from hydrocarbon accidents including explosion and fires can pose additional challenges. Limitations of conventional design tools against demands due to extreme events require use of more advanced techniques. This study presents a practical approach for assessment and design of piping systems for hydrocarbon accident events. Performance based failure criteria for piping systems has been shown to reduce the conservatism compared to allowable stress design for extreme events. Examples from major projects and case studies are also presented to demonstrate the technical approach. Consideration of a holistic approach accounting for interaction of piping and its support structure plays a key role in improving the design process.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".