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Continuously Differentiable Stick-Slip Friction Model with Applications to Cable Simulation Using Nonlinear Finite Elements

2020· article· en· W3090668358 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2020 IEEE Conference on Control Technology and Applications (CCTA) · 2020
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsOdeNonlinear systemPulleySolverComputationLagrange multiplierControl theory (sociology)Slip (aerodynamics)Computer scienceCoulomb frictionEngineeringApplied mathematicsMathematicsMechanical engineeringMathematical optimizationPhysicsAlgorithm

Abstract

fetched live from OpenAlex

This paper presents a continuously differentiable friction model based on the Quinn regularization of the Coulomb model in order to improve numerical performance for simulating dynamic systems using implicit ODE solvers. The implementation of the friction model for simulations of cable-pulley and cable-winch contact is demonstrated using the nonlinear Absolute Nodal Coordinate Formulation. Frictional contact between the cable and a dynamic surface is implemented using a Lagrange multiplier formulation. Examples of simple a capstan and a motorized pulley system are provided to demonstrate the stick-slip behavior of the model and the performance improvement over the original Quinn model, respectively. Using the ODE solver ode15s, the computation time was reduced by factors of 4.5 to 18.8 depending on the model parameters. The proposed model can be used to model and verify the behavior of dynamics systems in control applications.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.238
Teacher spread0.220 · 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