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Record W2765786444 · doi:10.1115/detc2017-67468

Element Based Force Analysis of a Single Input-Multiple Output Linkage System

2017· article· en· W2765786444 on OpenAlexaff
Cong Sun, Fengfeng Xi, Amin Moosavian, Daniel J. Inman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLinkage (software)Control theory (sociology)ActuatorMorphingLoop (graph theory)Computer scienceTorquePhysicsMathematicsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Presented in this paper is a method for force analysis of a single-input multiple-output (SIMO) linkage array that is designed for curve morphing applications, such as morphing airfoils. Different from the existing force methods, this method is developed to determine the force of a single actuator at the front that is needed to resist the forces on each loop for the entire multiloop linkage system. The proposed method is based on a full force model of a single loop four-bar linkage. When this model is applied to a multiloop system, two force sources are considered for each loop, namely the external point force on the coupler and the internal transition torque from the proceeding loop. As a result, a recursive method is proposed to compute the force from the last loop through intermediate loops to the first loop. The force vector of the first loop represents the required force of the single actuator needed to counteract the forces experienced by all the coupler forces. A number of simulations are performed and compared with FEM results to prove its effectiveness.

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 score0.774
Threshold uncertainty score0.398

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 head0.013
GPT teacher head0.206
Teacher spread0.193 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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