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Record W2989677960 · doi:10.1115/omae2019-96409

Seismic Design Challenges of High Pressure Riser Systems on Gravity Based Structures

2019· article· en· W2989677960 on OpenAlexaboutno aff
Mahesh Sonawane, Rohit Vaidya, Ronak Kadakia, Hunter Haeberle, Phil Ward

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsConductorEngineeringGeologyMarine engineeringStructural engineering

Abstract

fetched live from OpenAlex

Abstract This paper presents two case studies of the seismic analysis of high pressure riser and conductor systems used on shallow water fixed platforms (approximately 120m water depth) offshore Newfoundland and Labrador, Canada. The case studies presented consider the Hebron and White Rose extension Husky gravity-based concrete platforms. A methodology is presented to assess the dynamic and resonant response of each conductor and riser system to seismic loading using a fully integrated conductor and platform interaction model. Seismic loading is an application of an earthquake-generated agitation to a structure. This occurs at contact surfaces either with the ground, or with adjacent structures, or with gravity waves. Nonlinear time history analyses of the riser system subjected to various ground motion records are performed to simulate the seismic load. Design considerations that drive the HP riser design are discussed. The paper addresses the intricacies of the gravity base concrete platform-riser system interaction, initial configuration and dynamic seismic response. The riser and conductor system response is used to determine HP riser connector and centralization requirements. The learnings taken from the detailed modeling method are presented along with the advantages of this methodology.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.197
Teacher spread0.185 · 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 designTheoretical or conceptual
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
Published2019
Admission routes1
Has abstractyes

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