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Record W3189303430 · doi:10.4043/31328-ms

Structural Digital Twin of FPSO for Monitoring the Hull and Topsides Based on Inspection Data and Load Measurement

2021· article· en· W3189303430 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

VenueOffshore Technology Conference · 2021
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsIntecsea (Canada)
Fundersnot available
KeywordsHullFinite element methodSoftware deploymentAsset managementAsset (computer security)FidelityCondition monitoringComputer scienceEngineeringMarine engineeringReliability engineeringSystems engineeringStructural engineeringSoftware engineeringComputer securityTelecommunicationsElectrical engineering

Abstract

fetched live from OpenAlex

Abstract A high-fidelity FPSO Structural Digital Twin (SDT) based on Reduced Basis Finite Element Analysis (RB-FEA) coupled with inspection data and physical sensor measurements (advisory hull monitoring system) is presented to demonstrate a complete FPSO "digital thread" that combines operational data feeds, detailed structural analysis based on as-is asset condition, and automated structural integrity reporting. This lays the groundwork for a philosophical shift for asset lifecycle management by enabling the use of "as-measured" conditions in lieu of assumed "design-conditions" for a more accurate, and robust understanding of asset health. We demonstrate the deployment of this methodology for the Bonga FPSO and discuss the value that it brings during day-to-day operations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.066
GPT teacher head0.294
Teacher spread0.228 · 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