Design and Failure Mechanism Analysis of Fire Resistance Test for Fire-Resistant Oil Booms
Bibliographic record
Abstract
Fire-resistant oil boom is a special equipment used for emergency response to marine oil spill accidents. Its core function is to intercept floating oil while resisting high-temperature flames, preventing the spread of fire and oil pollution, and ensuring marine environmental safety. To verify the fire resistance performance of fire-resistant oil booms in actual fire environments, it is necessary to conduct continuous fire resistance tests to evaluate their structural integrity, thermal insulation efficiency, and flame retardancy. Based on standard fire resistance test requirements, this study designed and built a land-based water tank simulation test environment. The test site layout, flame loading method, temperature monitoring point arrangement, and data collection method were elaborated in detail, and the deformation, ablation, and failure processes of the oil boom under high-temperature combustion were systematically recorded. The test results show that the fire-resistant oil boom can still maintain key performance indicators in line with specification requirements under long-term flame action, verifying its fire resistance reliability. This study realizes the fire resistance test of fire-resistant oil booms under land-based water tank conditions for the first time, solving the problems of high cost and uncontrollability of marine tests. Meanwhile, through multi-parameter data monitoring, it provides data support for material optimization and failure mechanism analysis of fire-resistant oil booms, which has important reference value for improving marine oil spill fire emergency equipment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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".