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Record W4210443370 · doi:10.3233/jid-210020

A Novel Implementation of Energy-Based Homogenization Method

2022· article· en· W4210443370 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

VenueJournal of Integrated Design and Process Science · 2022
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHomogenization (climate)DiscretizationComputer scienceFinite element methodSoftwareBoundary value problemComputational scienceAlgorithmStructural engineeringMathematicsEngineeringMathematical analysis

Abstract

fetched live from OpenAlex

This paper develops a novel implementation of energy-based homogenization method, which has rigorous mathematical foundation of the homogenization method, and also efficiently and accurately predict the mechanical performance of composite materials. The feature extraction, domain discretization and periodic boundary condition application are carried out automatically in this method. Besides, this model remains a fairly small scale and it could be directly embedded into structure optimization algorithms. The numerical calculation could be easily implemented with a commercial computer aided engineering (CAE) software and the integration algorithm was realized by the third-party language. This article explains the scheme of the CAE/CAD integration in homogenization method and the theoretical model of the energy-based homogenization for cellular solid element and shell element. Furthermore, two examples for cellular solid and stiffened plate, and its implementation in cellular material design are presented to illustrate the verification of the proposed method.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.900
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.283
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