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Record W2304291435 · doi:10.14288/1.0080736

Effect of parabolization on viscous resistance of displacement vessels

2010· article· en· W2304291435 on OpenAlexaboutno aff
Voytek Klaptocz

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typearticle
Languageen
FieldEngineering
TopicTribology and Wear Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsResistance (ecology)Displacement (psychology)PsychologyPsychoanalysis

Abstract

fetched live from OpenAlex

The addition of parabolic side bulbs at the ship's mid body is aimed at reducing wavemaking resistance. This concept was first successfully tested on a coaster tanker and then extended to the UBC Series Model 3, a typical Canadian West Coast fishing vessel. A series of systematic tow tank experiments revealed that while parabolization decreases the total resistance (due to a drop in wave-making resistance) the form factor suffers an increase. This thesis focuses on numerical predictions of the influence of side bulbs on the viscous resistance characteristics of a displacement vessel. An integral boundary method solver and a 2D RANS solver were chosen as tools to predict the effect of parabolization on viscous drag for the UBC Series Model 3 hulls and the UBC Series Model 4. The concept of parabolization was then extended to an NPL Trimaran hull. A 3D RANS code was used to compare the calculated values of skin friction and boundary layer thickness to those calculated by the integral boundary layer solver. The RANS code was also used to numerically predict the effect of parabolization on viscous pressure drag for the NPL hull. In total, three different bulbs were studied numerically in addition to the parent NPL hull. The numerical results were compared to experimental data obtained from calm water resistance predictions obtained from tow tank testing. Further effort to decrease the impact of parabolization on form factor was made by applying moving surface boundary layer control to the UBC Series Model 4.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.957

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 head0.002
GPT teacher head0.166
Teacher spread0.163 · 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 designObservational
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
Published2010
Admission routes1
Has abstractyes

Explore more

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