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Record W2087307461 · doi:10.1115/esda2012-82437

Modelling Local Dynamic Pressure Within Inflatable Sports Balls

2012· article· en· W2087307461 on OpenAlexaff
Henry Hanson, Andy Harland, Christopher Holmes, Dan Price, Tim Lucas

Bibliographic record

VenueVolume 1: Advanced Computational Mechanics; Advanced Simulation-Based Engineering Sciences; Virtual and Augmented Reality; Applied Solid Mechanics and Material Processing; Dynamical Systems and Control · 2012
Typearticle
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsEulerian pathBall (mathematics)Finite element methodFootballInflatableCabin pressurizationMechanicsInternal pressureComputer scienceSimulationMarine engineeringMechanical engineeringLagrangianMathematicsEngineeringStructural engineeringApplied mathematicsPhysicsMathematical analysisGeographyThermodynamics

Abstract

fetched live from OpenAlex

This study used a coupled Eulerian Lagrangian (CEL) approach to model the air within a football (soccer ball) during two types of impacts. Conventional modelling techniques (and those used in all previous football finite element models known by the author) utilize a uniform pressure method incapable of accounting for spatial pressure variation. Internal pressures and deformations for the CEL and uniform pressure models were within a few percent of each other, indicating good agreement between pressurization techniques. By necessity, the air was defined with different methods in each model and this may have contributed to a discrepancy in maximum internal pressure. Using the CEL model, the pressure wave generated at impact was observed to travel from one side of the ball to the other at the speed of sound. Though the CEL model helped illustrate the impact scenario, there were no clear distinctions that gave it an advantage over the uniform pressure method for simple impact analysis.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.907
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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.

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

Citations1
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueVolume 1: Advanced Computational Mechanics; Advanced Simulation-Based Engineering Sciences; Virtual and Augmented Reality; Applied Solid Mechanics and Material Processing; Dynamical Systems and ControlSame topicSports Dynamics and BiomechanicsFrench-language works237,207