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Record W81778461

A Smoothed Particle Hydrodynamics Approach to Simulation of Articular Cartilage

2014· article· en· W81778461 on OpenAlexaff
Philip Boyer, Chris Joslin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsSmoothed-particle hydrodynamicsFinite element methodArticular cartilageMechanicsIndentationStress (linguistics)Materials scienceStiffnessStructural engineeringPhysicsComposite materialOsteoarthritisEngineering
DOInot available

Abstract

fetched live from OpenAlex

A practical preoperative simulation of femoral acetabular impingement (FAI) requires a faster method than is currently available to simulate the articular cartilage within the hip joint. Articular cartilage has been extensively studied with finite element methods (FEM), and several models have previously been developed to simulate its complex material behaviour. In the present study, the fibril-reinforced poroviscoelastic model was extended to function in a smoothed particle hydrodynamics (SPH) simulation. Four indentation tests and one unconfined compression test were designed and validated with previously published experimental reaction force results. 3D models were visualized to identify areas of peak stress and component stresses during simulations. Strong correlation in reaction forces (r=0.98~0.99) was found between simulations and published results. 3D models displayed stress distributions indicating correct component behaviour. The fibril-reinforced poroviscoelastic models were found to perform with greater accuracy than the hookean models while requiring a mean frame rate drop of only 29% (standard deviation 0.62%). Simulations functioned at rates exceeding 100 frames / second.

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 score0.528
Threshold uncertainty score0.266

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 head0.203
Teacher spread0.193 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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