MétaCan
Menu
Back to cohort
Record W1981214045 · doi:10.5539/cis.v5n5p88

Finite Element Method for Internal Wave Equation for Stratified Fluid

2012· article· en· W1981214045 on OpenAlexvenueno aff
Dawletbay Utebaev

Bibliographic record

VenueComputer and Information Science · 2012
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceInterpolation (computer graphics)Finite element methodRepresentation (politics)Variable (mathematics)Boundary (topology)DiscretizationApplied mathematicsBoundary value problemPartial differential equationMotion (physics)MathematicsMathematical analysisPhysics

Abstract

fetched live from OpenAlex

In many areas of natural science non-stationary problems on internal wave motion arise. For example, such problems appear in aerophysics, geophysics, oceanology, in theory of rotating fluid, and at design and construction of mining constructions (Gabov & Sveshnikov, 1990). It is a complicated mathematical problem to obtain accurate analytical solutions for such problems. In such cases a natural apparatus for study of internal wave motion processes is in numerical methods. In this work, the schemes of the finite element method with high accuracy in space and time for solution of a mixed boundary problem for internal wave equation for stratified fluid are proposed and studied. The schemes constructed have specific advantages compared to the other schemes: a scheme with high order of accuracy (more than two); besides the solution itself one finds along which that its derivative (velocity) with the same accuracy; at use of interpolation representation of the solution one can obtain, if needed, the solution and its derivative for an arbitrary instant; since the schemes are two-layer ones, one can use variable step without loss of accuracy; the scheme is conventionally stable and requires 4 times more arithmetical operations compared to ordinary ones, though this scheme makes it possible to choose larger time steps to get given accuracy. Besides that, evaluations of accuracy of the schemes for the problem under consideration are obtained. By means of dispersion analysis comparison to known schemes is carried out.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.029
GPT teacher head0.260
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueComputer and Information ScienceSame topicGeotechnical and Geomechanical EngineeringFrench-language works237,207