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Record W2983218196 · doi:10.1115/gt2019-91499

Implementation of Adaptive Gaussian Quadrature for Improved Accuracy of Boundary Element Methods Applied to Three Dimensional Geometries

2019· article· en· W2983218196 on OpenAlexaff
Michael J. Davies, Joseph Saverin

Bibliographic record

VenueVolume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy · 2019
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGaussian quadratureQuadrilateralBoundary element methodLaplace's equationMathematical analysisGaussianEllipsoidDipoleConstant (computer programming)Bilinear interpolationQuadrature (astronomy)Finite element methodIntegral equationBoundary value problemGeometryMathematicsPhysicsComputer scienceNyström methodOptics

Abstract

fetched live from OpenAlex

Abstract An adaptive Gaussian quadrature method for characterizing flow over three dimensional bodies via a boundary element method using isoparametric quadrilateral elements with non-constant source and dipole strengths has been developed and tested. This method is compared to state-of-the-art methods: flat elements with constant strengths, flat elements with bilinear strengths, and twisted elements with constant dipole strengths. As such, an overview of current boundary element methods is provided. The method developed here for twisted elements with non-constant source and dipole strengths is advantageous in that it both better approximates the actual geometry of the surface and the distribution of the dipole and source strengths. The majority of current methods are lacking at least one of these attributes. The developed method has been validated by comparison to two known analytical solutions: a non-lifting ellipsoid and a Kármán-Trefftz airfoil. The flexible and robust procedure presented here results in improved accuracy of the solution to the Laplace equation around three dimensional bodies.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.571
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.293
Teacher spread0.282 · 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 designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

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