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Record W3037336999 · doi:10.48336/gwrj-5p46

Dynamic risk analysis of offshore facilities in harsh environments

2020· dissertation· en· W3037336999 on OpenAlexaffabout
Jinjie Fu

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2020
Typedissertation
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAkaike information criterionBivariate analysisCopula (linguistics)EngineeringWind speedReliability engineeringOffshore wind powerSubmarine pipelineMarine engineeringEnvironmental scienceStatisticsMeteorologyEconometricsMathematicsWind powerGeotechnical engineeringGeography

Abstract

fetched live from OpenAlex

With the frequent occurrence of extreme weather conditions, the safe operation of offshore facilities has been seriously challenged. Previous research attempted to simulate hydrodynamic performance or structure dynamic analysis to single environmental load. The present thesis proposes two methodologies to assess the operational risk quantitatively with combined wind and wave loads in a harsh environment considering the dependence structure between the real-time environmental parameters. The first developed model calculates the environmental loads using wind load response modeling, the Morison model and the ultimate limit states method. Then physical reliability models and joint probability functions derive the probabilities of failure at the level of structural components corresponding to combined loads. BN integrates the root probabilities according to the unit configuration to calculate the failure probability of the Semi-submersible Mobile Unit (SMU). The model is examined with a case study of the Ocean Ranger capsizing accident on Feb 15, 1982. The model uses the prevailing weather conditions and calculates a very high probability of failure 0.7812, which proves the robustness and effectiveness of the proposed model. The second proposed model is the copula-based bivariate operational failure assessment function, which assesses the dependencies among the real-time environmental parameters. Dependence function is described by the parameter δ from the wave data and concurrent meteorological observation data which are obtained from the Department of Fisheries and Oceans Canada (DFO). Then, the true model is selected with the help of Akaike’s information criterion (AIC) differences and Akaike weight. Comparing the results from the proposed approach and the traditional approach, it is shown that operational failure probabilities considering dependence are noteworthy higher and deserve attention. In other words, the traditional approach underestimates the operational risk of offshore facilities, especially in harsh environments.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.235
Teacher spread0.221 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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