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Record W4396953111 · doi:10.3390/met14050584

Large-Scale Multi-Phase-Field Simulation of 2D Subgrain Growth

2024· article· en· W4396953111 on OpenAlexafffund
Ali Khajezade, Warren J. Poole, Michael Greenwood, Matthias Militzer

Bibliographic record

VenueMetals · 2024
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsNatural Resources CanadaUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du Canada
KeywordsRecrystallization (geology)Materials scienceGrain growthGrain sizeMicrostructurePhase (matter)Field (mathematics)Deformation (meteorology)MetallurgyComposite materialGeologyPhysicsMathematics

Abstract

fetched live from OpenAlex

The characteristics of subgrains in a deformed state after the high-temperature deformation of aluminum alloys control the subsequent recrystallization process and corresponding mechanical properties. In this study, systematic 2D phase-field simulations have been conducted to determine the role of deformed state parameters such as subgrain size and disorientation distributions on subgrain growth in an individual grain representing a single crystallographic orientation. The initial subgrain size and disorientation distributions have been varied by ±50%. To have a statistically relevant number of subgrains, large-scale simulations have been conducted using an in-house-developed phase-field code that takes advantage of distributed computing. The results of these simulations indicate that the growth of subgrains reaches a self-similar regime regardless of the initial subgrain structure. A narrower initial subgrain size distribution leads to faster growth rates, but it is the initial disorientation distribution that has a larger impact on the growth of subgrains. The results are discussed in terms of the evolution of the average diameter of subgrains and the average disorientation in the microstructure.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.425

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 head0.271
Teacher spread0.255 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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