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Record W4386816380 · doi:10.23977/jemm.2023.080308

Discussion on the Application of Fluid Mechanics in Sailing

2023· article· en· W4386816380 on OpenAlex
Yujie Liu

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2023
Typearticle
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsnot available
Fundersnot available
KeywordsFluid mechanicsLift (data mining)EngineeringComputational fluid dynamicsField (mathematics)Marine engineeringAerodynamicsMechanical engineeringComputer scienceAerospace engineeringMechanicsMathematicsPhysics

Abstract

fetched live from OpenAlex

As an ancient and charming navigation tool, the design and performance of sailboats have always been the focus of attention in the field of navigation technology. This article aims to explore the application of fluid dynamics in the design and performance optimization of sailboats. This article first introduces the basic concepts and principles of fluid mechanics, and then explores the applications of fluid mechanics in the resistance, lift, and stability of sailboats. By analyzing the design and optimization of different types of sailboats, the important role of fluid mechanics in improving sailboat speed, reducing resistance, optimizing sail shape, and ship stability has been revealed. This article emphasizes the value and potential of fluid mechanics in the field of sailing through case analysis, providing useful reference for future sailing design and technical research.

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)0.001
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 score0.197
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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