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Record W2005549124 · doi:10.1115/omae2013-10069

Real-Time Finite Element Analysis of a Remotely Operated Pipeline Repair System

2013· article· en· W2005549124 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsDynamic Systems Analysis (Canada)
Fundersnot available
KeywordsSubseaRemotely operated underwater vehicleMarine engineeringPipeline (software)Pipeline transportEngineeringRemotely operated vehicleSeabedProcess (computing)Computer scienceSimulationMechanical engineeringAutomotive engineeringRobotGeologyArtificial intelligenceMobile robot

Abstract

fetched live from OpenAlex

Remotely operated vehicle (ROV) pilots are frequently trained to operate in increasingly complex subsea environments using ROV simulators. These computer simulators de-risk important subsea operations by increasing ROV pilots’ skill levels in performing tasks under challenging environmental and operational constraints. ROV pilot-training simulation scenarios typically involve a variety of subsea equipment, such as trees, flow lines, pipeline end terminations (PLETs), etc. However, many critical ROV tasks, such as pipeline repair or riser installation, involve flexible structures. The following paper investigates a method for accurately simulating pipelines and flexibles within an ROV pilot-training simulator. The goal of the technology development is to enable engineers and marine operators to assess the risks associated with certain tasks, such as pipeline repair or flexible hook-up, in real-time using ROV simulation technology. In particular, the challenge that this paper will address is how to determine the stresses in a subsea pipeline using a lumped mass finite-element cable model within a multi-body simulation framework. Repair of subsea pipelines is a complex multi-step process typically carried out by ROVs. During pipeline repair, a pipeline repair system (PRS) is lowered to the seabed. The PRS must lift the pipeline off the seabed and the damaged section of pipeline must then be cut and removed, and a new section of pipeline put in place. During the lifting, cutting and installation phases it is important that the pipeline is not overstressed and the equipment used in the repair operation is not overloaded. In addition, there are a wide array of operational variables, procedures and decisions that must be evaluated. Towards this end, an ROV simulation facility capable of assessing stresses and operations in real-time was constructed using the finite element simulation software package ProteusDS in conjunction with GRI Simulations Inc.’s VROV simulator. The system was designed to evaluate the impact of different combinations of operating parameters and is intended to be useful for system design and analysis. The system would be of immense utility in rapid response to a real-world incident where the system may be called into action. The following paper reviews the simulation framework, the models employed, the results of model verification, and discusses the challenges of the project.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.188
Teacher spread0.182 · 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

Quick stats

Citations4
Published2013
Admission routes1
Has abstractyes

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