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Record W1975912114 · doi:10.1081/sme-100000002

POLYNOMIAL SHAPE FUNCTIONS AND NUMERICAL METHODS FOR FLEXIBLE MULTIBODY DYNAMICS*

2001· article· en· W1975912114 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMechanics of Structures and Machines · 2001
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLegendre polynomialsChebyshev polynomialsDiscretizationMultibody systemMathematicsPolynomialNonlinear systemApplied mathematicsSolverMathematical analysisMathematical optimizationClassical mechanicsPhysics

Abstract

fetched live from OpenAlex

The use of Taylor polynomials as shape functions in a Rayleigh–Ritz discretization of flexible beams in dynamic multibody systems has been previously investigated [[1] Valembois, R. E., Fisette, P. and Samin, J. C. 1997. Comparison of Various Techniques for Modelling Flexible Beams in Multibody Dynamics. Nonlinear Dynam., 12: 367–397. [Crossref], [Web of Science ®] , [Google Scholar] [2] Saad, M., Piedboeuf, J.-C. and Akhrif, O. in press. Comparison of Different Shape Functions in Assumed-Mode Models of a Flexible Slewing Beam. Mechanism Mach. Theory, [Google Scholar]]. This approach is relatively simple, but it was found to cause some ill-conditioning problems in the numerical solution of the system equations. In this paper, two solutions to these problems are presented. First, better-behaved system equations can be obtained by using orthogonal Chebyshev or Legendre polynomials in place of Taylor polynomials. Secondly, a judicious choice of numerical solver can reduce or eliminate the ill-conditioning problems. Two examples are used to demonstrate these findings.

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 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 score0.975
Threshold uncertainty score0.579

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 head0.008
GPT teacher head0.270
Teacher spread0.262 · 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