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Record W2807920665 · doi:10.1615/jpormedia.v21.i5.20

A POROUS MEDIA MODEL OF ALVEOLAR DUCT FLOW IN THE HUMAN LUNG

2018· article· en· W2807920665 on OpenAlex

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

VenueJournal of Porous Media · 2018
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsPorous mediumDuct (anatomy)MechanicsComputational fluid dynamicsPermeability (electromagnetism)Computer sciencePorosityPhysicsGeologyChemistryMedicineGeotechnical engineeringAnatomy

Abstract

fetched live from OpenAlex

Prediction of air flow in the human lung is of great interest for many physiological applications. Recent advances in modeling such flows using computational fluid dynamics have included the development of porous media-based approaches that consider the small-scale airways and alveoli as a porous domain. This article presents a derivation of the governing equations relevant to flow in an alveolated duct based on the theory of volume-averaging as well as their closure. It is shown that the momentum closure problem reduces to that of a steady-state problem which is solved over a representative unit cell of an alveolated duct to predict its permeability. The modeling approach is validated against permeability predictions coming from transient simulations of flow in an expanding and contracting duct. Finally, analytical expressions for the velocity and pressure in an alveolated duct are derived and presented.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.434

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 communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.239
Teacher spread0.221 · 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