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A 3D discrete-element model for simulating liquid feeding during dendritic solidification of steel

2019· article· en· W2947087518 on OpenAlexaff
Yi Feng, Miha Založnik, Brian G. Thomas, A.B. Phillion

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldMaterials Science
TopicSolidification and crystal growth phenomena
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDendrite (mathematics)MechanicsMaterials scienceRepresentative elementary volumeFlow (mathematics)Fluid dynamicsEquiaxed crystalsFinite element methodHagen–Poiseuille equationDiscrete element methodThermodynamicsPhysicsGeometryMathematicsComposite material

Abstract

fetched live from OpenAlex

Abstract A 3D meso-scale discrete-element model has been developed to simulate fluid flow during dendritic solidification of steel. The model domain is a representative volume element consisting of a set of equiaxed dendritic grain envelopes along with extra-dendritic liquid channels, where the final grain shape is given by a Voronoi tessellation. Solidification of each grain is simulated via a volume average approach. The output of the solidification simulation at a given solid fraction is used as the input mesh for the fluid flow simulation. A single domain Darcy-Brinkman model is used to calculate the pressure field within the liquid channels, with Poiseuille flow assumed to occur in the extra-dendritic region, and Darcy flow assumed to occur within the dendrite envelope. Mass conservation over each element is then used to derive a flow equation that is solved via the finite element method. The results of this new model are first compared with a previously-developed granular model [1] where fluid flow only occurs between the grains, and then compared with different forms of the Carman-Kozeny equation. It is shown that the intra-dendritic liquid fluid flow plays a major role in the semi-solid pressure field, and thus needs to be included when investigating hot tearing susceptibility in engineering alloys undergoing dendritic solidification.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.246
Teacher spread0.224 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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