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Record W4376278056 · doi:10.1002/prs.12474

Modeling and risk analysis of large‐scale crude oil pool fire on an offshore facility

2023· article· en· W4376278056 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProcess Safety Progress · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsMemorial University of Newfoundland
FundersNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of China
KeywordsMarine engineeringEnvironmental scienceSubmarine pipelineScale (ratio)EngineeringPetroleum engineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Large‐scale pool fires on offshore platforms can have disastrous consequences. Wind load is an essential component of the marine environment; hence, wind‐shielding facilities are installed on the platforms as needed. A computational fluid dynamics technique is used in this study to model large‐scale pool fires. The effects of wind load and windshields on large‐scale pool fires are investigated. Furthermore, the impact of the large‐scale pool fire on personnel is evaluated based on thermal radiation dose. It has been discovered that the growing horizontal momentum of the wind load might result in a significant distance of fatal injury. The windshields have double‐edged effects, including that: (1) they can reduce the injury distance by preventing the spread of fire products; and (2) they could block the release of heat and smoke. It is advised that while installing windshields on offshore platforms, different fire scenarios need to be considered. This research can be used to provide technical support for the development of the emergency evacuation strategy and layout design of offshore platforms.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.007
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.378
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it