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Real model for micro swimmer and the study of the relationship between the swimming speed, pitch angle, and rotation rate for the flagellum

2023· article· en· W4388827610 on OpenAlexaff
Zhezheng Ren

Bibliographic record

VenueJournal of Physics Conference Series · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRotation (mathematics)Rotational speedMechanicsRADIUSFlagellumPhysicsPitch angleRange (aeronautics)MATLABProtein filamentPoint (geometry)SimulationOpticsClassical mechanicsGeometryMaterials scienceMathematicsComputer scienceEngineeringAerospace engineeringChemistry

Abstract

fetched live from OpenAlex

Abstract This study focuses on the fluid mechanics of a microswimmer and explores the relationship between speed, pitch angle, and rotation rate for the flagellar during bacterial swimming. Based on the simulation using MATLAB, it is concluded that when the pitch angle of the flagellar helix is in the range of 0 to 90 degrees, the value of swimming speed increases firstly and decreases. When the angle reaches 46.83 degrees, the speed reaches the maximum point. The radius of the body of the microswimmer is determined by the Buckingham Pi theory. After calculating by using the equations in the related paper and measuring by the real model, we derive that the relationship between swimming speed and the rotation rate for the flagellar filament should be proportional at the low rotation rate so that it can be obtained to optimize the artificial micro swimming device with higher swimming efficiency.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.296
Teacher spread0.226 · 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 designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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