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Record W2126511524 · doi:10.1109/robot.1992.220245

Two numerical issues in simulating constrained dynamics

2003· article· en· W2126511524 on OpenAlexaff
R.E. Ellis, S.L. Ricker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsQueen's University
Fundersnot available
KeywordsSingularityConstraint (computer-aided design)Ordinary differential equationKinematicsClosure (psychology)Differential equationTrajectoryApplied mathematicsEquations of motionNumerical analysisMotion (physics)Computer scienceMathematicsComputationMathematical analysisClassical mechanicsAlgorithmPhysicsLawArtificial intelligenceGeometry

Abstract

fetched live from OpenAlex

A common approach to formulating the dynamics of closed-chain mechanisms requires finding the forces of constraint at the loop closures. However, there are indications that this approach leads to ill-conditioned systems that must be inverted and to numerically unstable differential equations of motion. The authors derive a sufficient condition for ill conditioning of augmented dynamical systems, namely, that the mechanism's trajectory passes through, or very near, a kinematic singularity. In singular regions the equations of motion are also numerically stiff, and frequently require special numerical methods for computer solution. The authors propose a novel method of calculating closed-chain dynamics, based on the systematic elimination of variables that are both redundant and that may adversely affect the computations. This approach produces numerically stable solutions of the differential equations of motion, and the equations are apparently much less stiff than the equations produced by the traditional force-closure approach.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.803
Threshold uncertainty score0.361

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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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

Citations7
Published2003
Admission routes1
Has abstractyes

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