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Record W2154884452 · doi:10.1139/tcsme-2000-0024

DESIGN OPTIMIZATION OF A COMPLEX MECHANICAL SYSTEM USING ADAPTIVE RESPONSE SURFACE METHOD

2000· article· en· W2154884452 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2000
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Multi-Objective Optimization Algorithms
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsResponse surface methodologyComputer scienceMulti-objective optimizationEngineering optimizationSystems designScheme (mathematics)Optimization problemConstraint (computer-aided design)Complex systemMathematical optimizationControl engineeringEngineeringMechanical engineeringMathematics

Abstract

fetched live from OpenAlex

Today’s increasingly competitive industrial environment demands shorter product development lead-times, lower costs, and higher quality products. These requirements produce more complex design problems that are characterized by multiple design objectives as well as complex design objective and constraint relations. The optimization of these computationally intensive design problems leads to new technical challenges. This work applies a new global optimization search scheme, the Adaptive Response Surface Method (ARSM), to the optimal design of a complex mechanical system — the radiator stack PEM fuel cell system. The design optimum manifests a significant increase in system performance and decrease in cost. The proposed method can also be applied to the solution of other complex design optimization problems.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.160
Threshold uncertainty score0.695

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 science0.0010.000
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 head0.036
GPT teacher head0.260
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