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Record W2996029660 · doi:10.1139/cjp-2019-0211

Numerical study on structures formed by the deposition and solidification of a single molten droplet

2019· article· en· W2996029660 on OpenAlexvenueno aff
Wenbin Li, Ying Zhang, Yue Chen, Peisheng Li, Chunhong Ma

Bibliographic record

VenueCanadian Journal of Physics · 2019
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsnot available
FundersNatural Science Foundation of Jiangxi ProvinceNational Natural Science Foundation of China
KeywordsDimensionless quantityMechanicsSuperheatingWeber numberFlatteningPhysicsDeposition (geology)Deformation (meteorology)Work (physics)Rotational symmetryScale (ratio)ThermodynamicsReynolds numberTurbulenceMeteorology

Abstract

fetched live from OpenAlex

The formed structure is of importance in determining the surface quality of a component made by droplet-based 3D printing. In the present work, the molten droplet solicitation process was simulated under an axisymmetric system where the smallest length scale and time scale were fully resolved. Evolutions of sharp droplet interfaces were captured through the front tracking method. Parametric studies have been carried out to explore how the dynamic metrics, which include the Ohnesorge number (Oh) and Weber number (We), can affect the structure of depositing droplet. The effect of the superheat parameter on the cooling rate was also investigated in the final section. Numerical results show that the inertial resisting force is critical dynamics in the variation of horizontal dimensionless length at the early deposition process. Three levels of Oh numbers and stages of We numbers were classified according to the deformation behavior. Flattening degree under different Oh number and We number cases were both fitted well by the exponential function. This paper also reveals that the variation law of the cooling rate and solidification time is affected by the superheat parameter, resulting in a feasible and promising method to predict droplet deformation time through the fully resolved numerical simulations during the manufacturing process.

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 score0.672
Threshold uncertainty score0.169

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 head0.013
GPT teacher head0.205
Teacher spread0.192 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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