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Record W2064332345 · doi:10.1109/mesa.2008.4735689

A New Topology Optimization Method for Multi-physics Micro Domains

2008· article· en· W2064332345 on OpenAlex
Mehrnaz Motiee, Amir Khajepour, Raafat R. Mansour

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTopology optimizationDiscretizationActuatorFinite element methodTopology (electrical circuits)Scheme (mathematics)Computer scienceConstraint (computer-aided design)Frame (networking)Domain (mathematical analysis)Microelectromechanical systemsMathematical optimizationMechanical engineeringEngineeringStructural engineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

In this paper, a novel topology optimization scheme is developed for a systematic design of electro-thermo-mechanical devices employing discrete domain with beam elements. To address the shortcomings of previous related work, frame ground structure is used for discretization and real-coded genetic algorithm is employed to solve the optimization problem. The failure criterion is addressed by imposing a buckling constraint. The proposed optimization scheme is implemented and verified with an example of electro-thermo mechanical actuator. The performance of the optimized configuration is further studied with finite element model in ANSYS. The results of this paper confirm that the presented method is a powerful tool to design MEMS structures with on-chip actuation.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.693

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 head0.022
GPT teacher head0.274
Teacher spread0.252 · 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

Quick stats

Citations2
Published2008
Admission routes1
Has abstractyes

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