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Record W2017718190 · doi:10.3139/217.1625

Numerical Study of Internal Bubble Cooling (IBC) in Film Blowing

2001· article· en· W2017718190 on OpenAlex
V. Sidiropoulos, J. Vlachopoulos

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Polymer Processing · 2001
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBubbleAirflowMechanicsTurbulenceMaterials scienceInletInternal flowComputer simulationInternal heatingStack (abstract data type)CompressibilityAir bubbleAir coolingFlow (mathematics)Heat fluxMechanical engineeringHeat transferPhysicsEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract Numerical simulation of turbulent airflow emerging from an Internal Bubble Cooling (IBC) stack and directly impinging on the internal surface of a blown film bubble has been carried out. The streamline pattern and heat flux are determined through a finite volume numerical technique using a version of k-∊ turbulence modeling. It is shown that balancing the airflow between the multiple slits of the stack is useful to increase the internal cooling rates, but may require more elaborate designs of the inlet pipe. As the air flows towards the internal bubble surface there is significant deceleration which diminishes the effectiveness of the heat removal mechanisms. Depending on the flow rates and geometrical configurations the airflow may induce compressibility effects at the exhaust pipe. The numerical results suggest that Internal Bubble Cooling (IBC) equipment introduces distinctive design and operation challenges.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.566

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 head0.247
Teacher spread0.238 · 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