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Record W2327192159 · doi:10.1149/05701.1379ecst

Identification of Ni-YSZ Anode Creep Property Using PSO for Multiscale Simulation

2013· article· en· W2327192159 on OpenAlexaff
S. Watanabe, Fumitada Iguchi, Kazuhisa Sato, Koji Yamamoto, Toshiyuki Hashida, Kenjiro Terada, ‪Tatsuya Kawada

Bibliographic record

VenueECS Transactions · 2013
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsCybernet Systems Corporation (Canada)
Fundersnot available
KeywordsCreepMaterials scienceAnodeParticle swarm optimizationYttria-stabilized zirconiaCermetStress (linguistics)Deformation (meteorology)Composite materialComputer scienceAlgorithmCubic zirconiaCeramicElectrodePhysics

Abstract

fetched live from OpenAlex

Our group has attempted to construct an SOFC multiscale model which can simulate inelastic deformation under operating conditions. In this study, we have focused on mechanical properties of Ni-YSZ anode and aimed to identify creep parameter which is a key factor of inelastic deformation. Measurements were performed at elevated temperatures under controlled oxygen partial pressures using in-situ mechanical testing equipment. Creep parameters of Ni-YSZ anode was optimized to have least difference between the result of numerical material test and experiment, using Particle Swarm Optimization (PSO) algorithm of the approximate optimization techniques based on behavior of self-organized system. As a result, we could confirm transit creep on Ni-YSZ cermet, and identify parameters of it. Simulated creep curves showed good fitting to experimental curves at applied stress up to 5MPa but, deviated at higher applied stress.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.311
Teacher spread0.275 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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Same venueECS TransactionsSame topicAdvancements in Solid Oxide Fuel CellsFrench-language works237,207