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Record W7132679273

Physical model studies of innovative oil containment boom designs

2021· article· en· W7132679273 on OpenAlexvenueno aff
S. Baker, A. Cornett, S. Potter, K. McKinney, H. Babaei, V. Pilechi

Bibliographic record

VenueNPARC · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsBoomContainment (computer programming)Oil boomOil spillScale (ratio)Computational fluid dynamics
DOInot available

Abstract

fetched live from OpenAlex

The objective of this research was to develop, assess and validate alternative boom designs that would allow containment and collection of oil spills in water at higher speeds than currently possible using conventional boom technologies. A comprehensive series of two- and three-dimensional CFD simulations and large-scale physical model experiments were conducted investigating the oil containment performance characteristics of several innovative boom concepts (and multiple variations thereof) at high speeds. Both the simulations and experiments considered varying quantities of low, medium, and high-viscosity oils. This paper discusses the physical scale model experiments in detail. The boom concepts investigated in this research included conventional designs with a single skirt and innovative designs incorporating ramps or permeable screen barriers to manage the oil slick at higher speeds. The extensive laboratory testing and computational modelling revealed that innovative boom designs are able to safely and effectively contain oil at speeds up to 3 knots or more. Effective high-speed booms will make it possible to contain and recover oil spilled into fast-flowing rivers, estuaries and coastal waters, and accelerate oil recovery operations in open water 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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.336

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 head0.044
GPT teacher head0.294
Teacher spread0.250 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

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