MétaCan
Menu
Back to cohort

Mathematical Modeling of Squeeze Casting of Magnesium Alloy AM50

2010· article· en· W2050270485 on OpenAlexafffund
Zhi Zhong Sun, Alfred Yu, Henry Hu, Han Li

Bibliographic record

VenueDefect and diffusion forum/Diffusion and defect data, solid state data. Part A, Defect and diffusion forum · 2010
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Windsor
KeywordsCastingMagnesium alloyMaterials scienceHeat transferControl volumeMechanicsAlloyCooling curveMetallurgyMagnesiumThermodynamicsDie castingConvectionPhysics

Abstract

fetched live from OpenAlex

In this study, a two-dimensional (2-D) mathematical model was developed to simulate forced and natural convection, heat transfer and solidification occurring during the squeeze casting process. The model was based on the control-volume finite difference approach and on the enthalpy method. The computation was performed to understand the effect of applied pressures on the solidification and cooling behavior of a cylindrical squeeze casting of magnesium alloy AM50A which could have extensive usage in automobiles. The model computed the melt flow fields, the temperature distributions, the cooling curves, the shape and position of the phase front, and total solidification time of the casting. The predicted results show that high applied pressures result in high heat transfer across the casting/die interface, and consequently increase solidification and cooling rates.

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)0.000
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.021
GPT teacher head0.251
Teacher spread0.230 · 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

Citations1
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueDefect and diffusion forum/Diffusion and defect data, solid state data. Part A, Defect and diffusion forumSame topicAluminum Alloy Microstructure PropertiesFrench-language works237,207