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Record W1846106224 · doi:10.1115/1.4031908

A Mechanism-Based Approach From Low Cycle Fatigue to Thermomechanical Fatigue Life Prediction

2015· article· en· W1846106224 on OpenAlexafffund
Xijia Wu, Zhong Zhang

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2015
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsNational Research Council Canada
FundersNational Research Council Canada
KeywordsMaterials scienceCoalescence (physics)CreepEmbrittlementPlasticityIsothermal processNucleationClassification of discontinuitiesMechanism (biology)Low-cycle fatigueConstitutive equationMetallurgyStructural engineeringMechanicsComposite materialThermodynamicsFinite element methodEngineering

Abstract

fetched live from OpenAlex

Deformation and damage accumulation occur by fundamental dislocation and diffusion mechanisms. An integrated creep–fatigue theory (ICFT) has been developed, based on the physical strain decomposition rule that recognizes the role of each deformation mechanism, and thus relate damage accumulation to its underlying physical mechanism(s). The ICFT formulates the overall damage accumulation as a holistic damage process consisting of nucleation and propagation of surface/subsurface cracks in coalescence with internally distributed damage/discontinuities. These guiding principles run through both isothermal low cycle fatigue (LCF) and thermomechanical fatigue (TMF) under general conditions. This paper presents a methodology using mechanism-based constitutive equations to describe the cyclic stress–strain curve and the nonlinear damage accumulation equation incorporating (i) rate-independent plasticity-induced fatigue, (ii) intergranular embrittlement (IE), (iii) creep, and (iv) oxidation to predict LCF and TMF lives of ductile cast iron (DCI). The complication of the mechanisms and their interactions in this material provide a good demonstration case for the model, which is in good agreement with the experimental observations.

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 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: Empirical · Consensus signal: none
Teacher disagreement score0.515
Threshold uncertainty score0.668

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 head0.016
GPT teacher head0.215
Teacher spread0.199 · 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 teacher head, 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

Citations14
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Engineering for Gas Turbines and PowerSame topicHigh Temperature Alloys and CreepFrench-language works237,207