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Record W2029846432 · doi:10.1115/1.4025326

Implementation and Validation of a 1D Fluid Model for Collapsible Channels

2013· article· en· W2029846432 on OpenAlexafffund
Peter G. Anderson, Sidney Fels, Sheldon Green

Bibliographic record

VenueJournal of Biomechanical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInviscid flowDiscretizationFluid mechanicsSolverFluid dynamicsMechanicsFlow (mathematics)Closing (real estate)Fluid–structure interactionComputational fluid dynamicsGeometrySolid mechanicsMathematicsPhysicsMathematical analysisFinite element methodMathematical optimization

Abstract

fetched live from OpenAlex

A 1D fluid model is implemented for the purpose of fluid-structure interaction (FSI) simulations in complex and completely collapsible geometries, particularly targeting the case of obstructive sleep apnea (OSA). The fluid mechanics are solved separately from any solid mechanics, making possible the use of a highly complex and/or black-box solver for the solid mechanics. The fluid model is temporally discretized with a second-order scheme and spatially discretized with an asymmetrical fourth-order scheme that is robust in highly uneven geometries. A completely collapsing and reopening geometry is handled smoothly using a modified area function. The numerical implementation is tested with two driven-geometry cases: (1) an inviscid analytical solution and (2) a completely closing geometry with viscous flow. Three-dimensional fluid simulations in static geometries are performed to examine the assumptions of the 1D model, and with a well-defined pressure-recovery constant the 1D model agrees well with 3D models. The model is very fast computationally, is robust, and is recommended for OSA simulations where the bulk flow pressure is primarily of interest.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.553
Threshold uncertainty score0.360

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 head0.024
GPT teacher head0.273
Teacher spread0.249 · 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 designSimulation or modeling
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

Citations8
Published2013
Admission routes2
Has abstractyes

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