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Record W3005922235 · doi:10.1139/cjce-2019-0433

A rational bubble screen design approach for mitigation of underwater explosion near waterborne infrastructure

2020· article· en· W3005922235 on OpenAlexaffvenue
L. Sebastian Bryson, Paul R. Smith, Kamyar C. Mahboub

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsBubbleUnderwaterUnderwater explosionShock waveShock (circulatory)Flux (metallurgy)Materials scienceStructural engineeringEnvironmental scienceMarine engineeringMechanicsEngineeringGeologyPhysics

Abstract

fetched live from OpenAlex

Underwater explosions pose a significant threat to waterborne infrastructure. The potential for damage to nearby infrastructure is dependent on parameters such as the peak incident pressure, energy flux density, radial standoff distance, and dynamic material strength and structural response. Bubble screens have been shown to be an effective means to attenuate specific shock wave characteristics such as the peak incident pressure and energy flux density to safe levels. This paper presents a comprehensive, rational design approach for protecting waterborne infrastructure using bubble screens. The paper presents an improved methodology to predict peak incident pressure and energy flux density produced by shock waves, which includes depth effects. In addition, the damage potential to various structural materials is established based on the equivalent dynamic loads and dynamic material strength. The results are compiled to form a step-by-step procedure for the design and implementation of a bubble screen system.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score0.303

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 head0.017
GPT teacher head0.188
Teacher spread0.171 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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