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Record W2626232695

How to extract structural matrices (mass,stiffness...) from Ansys and import them into Matlab. Obtention of elementary cyclic symmetric matrices from an elementary sector in Ansys.

2018· preprint· en· W2626232695 on OpenAlexaff
Alain Batailly

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2018
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Measurement and Metrology Techniques
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSection (typography)StiffnessMATLABStructural engineeringInterpretation (philosophy)Binary numberMathematicsComputer scienceEngineeringArithmeticProgramming language
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this note is to explain how to extract stiffness and mass matrices from Ansys. This can be useful for specific calculations with other codes such as Matlab. Section one focuses on structures featuring cyclic symmetric where the stiffness and mass matrices of only one elementary sector allow to obtain the global matrices. Section two presents the Ansys commands to obtain the binary files (.emat and .full files) containing the matrices. Section three is devoted to the interpretation of these files.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.115
Threshold uncertainty score0.386

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1150.068

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 head0.240
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreSoftware

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
Published2018
Admission routes1
Has abstractyes

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