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Record W4384927430 · doi:10.24425/amm.2021.135871

Reducing Melt Surface Turbulence by Employing Surge and Filter in a Conventional Non-Pressurizing Gating System: Simulation and Experiment

2021· article· en· W4384927430 on OpenAlexaff
Amir Baghani, Ali Kheirabi, Ahmad Bahmani, Hamid Khalilpour

Bibliographic record

VenueArchives of Metallurgy and Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceTurbulenceSurgeFilter (signal processing)GatingMechanicsIndustrial chemistryMaterials processingSurface (topology)Nuclear engineeringProcess engineeringMeteorologyEngineeringElectrical engineeringPhysicsBiochemical engineeringBiophysics

Abstract

fetched live from OpenAlex

Tensile strength of aluminum castings has been improved by employing surge and filter in a conventional non-pressurizing gating system. For this purpose, three non-pressurizing bottom-gating systems were designed where the first design was a simple design with no filter and no surge, in the second design filter and in the third one surge was added to the end of runner. Tensile strength, Weibull module, scanning electron microscopy, chemical analysis, and melt pattern during the mold filling were thoroughly analyzed to compare these three designs. It was observed that employing filter and surge in the gating system reduces flow kinetic energy and consequently avoid surface turbulence and air entrainment, which leads to castings with fewer defects and higher reliabilities. Finally, it found that appropriate use of surge in the running system can be as effective as employing a filter in reducing melt front velocity.

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 score0.699
Threshold uncertainty score0.501

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 head0.009
GPT teacher head0.223
Teacher spread0.213 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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