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Record W2343004409 · doi:10.1002/aic.15288

Numerical modeling of the cooling cycle and associated thermal stresses in a melt explosive charge

2016· article· en· W2343004409 on OpenAlexaff
Warren Sanhye, Charles Dubois, Isabelle Laroche, Pierre Pelletier

Bibliographic record

VenueAIChE Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsGeneral Dynamics (Canada)Polytechnique Montréal
Fundersnot available
KeywordsMultiphysicsExplosive materialMaterials scienceMechanicsMechanical engineeringMomentum (technical analysis)CastingFoundryThermalShrinkageHeat transferEngineeringComposite materialStructural engineeringFinite element methodThermodynamicsChemistryPhysics

Abstract

fetched live from OpenAlex

A comprehensive simulation tool is developed to describe and optimize the cooling cycle in the melt‐casting of Composition B. It comprises a multiphysics approach tackling heat, mass, and momentum transfers involved in the casting process. The highly nonlinear solidification step and development of thermal stresses are included. A V & V (Verification and Validation) approach was adopted whereby the model was verified against a benchmark problem and tested with a simple cylindrical geometry. Then, the approach was applied to a 105 mm caliber artillery shell and simulation results were in close agreement with experimental measurements. The model is equipped with a CZM function to account for adhesion between the solidified cast and the mold. During cooling, separation is possible and the size and location of gaps, depending on shrinkage and adhesion, are successfully emulated. The importance of controlled solidification is pointed out, especially regarding steep temperature gradients within the shell. © 2016 American Institute of Chemical Engineers AIChE J , 62: 3797–3811, 2016

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 score0.314
Threshold uncertainty score0.126

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 head0.010
GPT teacher head0.196
Teacher spread0.186 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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