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Design, Analysis and Preliminary Validation of a 3-DOF Rotational Inertia Generator

2020· article· en· W3129240771 on OpenAlexaff
Jean-Felix Tremblay-Bugeaud, Thierry Laliberté, Clément Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFlywheelInertiaTorqueGyroscopeMoment of inertiaGenerator (circuit theory)Control theory (sociology)MechatronicsAngular velocityComputer scienceRotational speedEngineeringControl engineeringPhysicsMechanical engineeringAerospace engineeringControl (management)Classical mechanics

Abstract

fetched live from OpenAlex

This paper investigates the design of a three-degree-of-freedom rotational inertia generator using the gyroscopic effect to provide ungrounded torque feedback. It uses a rotating mass in order to influence the torques needed to move the device, creating a perceived inertia. The dynamic model and the control law of the device are derived, along with those of a comparable concept using three flywheels instead of a gyroscope. Both models are then validated through simulations. Further simulations are conducted to establish motor torque and velocity requirements, and the gyroscopic concept is identified as having the less demanding requirements. The mechatronic design of a prototype of an inertia generator is presented, along with modifications to the dynamic model. Preliminary experimental validations are conducted. As the prototype faces instability issues when using the flywheels at high velocities, they are conducted using 0 RPM initial velocities. The results confirm that it is possible to both reduce and increase the rendered inertia even with current limitations. Finally, improvements for a second version of the prototype are discussed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.517
Threshold uncertainty score0.257

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 head0.016
GPT teacher head0.200
Teacher spread0.184 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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