MétaCan
Menu
Back to cohort
Record W2164452475 · doi:10.1177/0954410011408941

Estimation models for the preliminary design of electromechanical actuators

2011· article· en· W2164452475 on OpenAlexaff
Marc Budinger, Jonathan Liscouët

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsActuatorSizingAirframeAerospaceComponent (thermodynamics)Reliability (semiconductor)EngineeringControl engineeringScalingComputer scienceScaling lawPower (physics)Control theory (sociology)Aerospace engineeringControl (management)Mathematics

Abstract

fetched live from OpenAlex

This article presents estimation models for the model-based preliminary design of electromechanical actuators. Models are developed to generate all the parameters required by a multi-objective design, from a limited number of input parameters. This is achieved using scaling laws in order to take advantage of their capability to reflect the physical constraints driving the actuator’s component sizing. The proposed approach is illustrated with the major components that are involved in aerospace electromechanical actuator design. The established scaling laws provide the designer with parameters needed for integration into the airframe, power sizing, thermal balance, dynamics, and reliability. The resulting estimation models are validated with industrial data.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.091
GPT teacher head0.271
Teacher spread0.180 · 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 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

Citations61
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace EngineeringSame topicProbabilistic and Robust Engineering DesignFrench-language works237,207