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Record W7117982079 · doi:10.5539/jmsr.v14n2p22

Analytical Formulation for Larson–Miller Constant of Steel

2025· article· W7117982079 on OpenAlexvenueno aff
Manabu TAMURA

Bibliographic record

VenueJournal of Materials Science Research · 2025
Typearticle
Language
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsnot available
Fundersnot available
KeywordsGravitational singularityCreepExponential functionConstant (computer programming)Standard deviationEntropy (arrow of time)Martensite

Abstract

fetched live from OpenAlex

The Larson–Miller constant (C) of martensitic steel, which is used as the Larson–Miller parameter, is considerably larger than the typically used value of 20 for many types of heat-resistant steels. To provide better understanding regarding this fact, an analytical formulation for the Larson–Miller constant is developed using a model based on interactions between moveable dislocations and elastic singularities in a system. To verify the proposed equation, eight types of Grade 91, 92, and 122 steels are used, whose maximum rupture life exceeds 1E5 h. Creep data are classified into 257 groups by temperature, stress, and strain or time. C values are obtained by applying multiple-regression analyses to time parameters that obey the exponential law, assuming a thermally activated process. C and Ccal are calculated for each data group based on an exponential law and a proposed equation, respectively. The statistical values for C and Ccal are as follows: ̄C=32.41, Cmin=7.87, Cmax=64.88, and (Ccal⁄C)=99.3%. Although (∆C)̄=(Ccal-C) ̅=0.02 is extremely low, the standard deviation of ∆C is large, i.e., 1.27. Results confirmed that the proposed equation can estimate wide-ranging C values, although the equation is expected to be improved. A major component of C for C>15 is an increase in the entropy change caused by elastic interactions between moveable dislocations and elastic singularities in a system.

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)0.016
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.055
GPT teacher head0.397
Teacher spread0.342 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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