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Record W7018529688

Development of a Cell-Centred Finite Difference Numerical Methodology on Triangulated Domains

2012· dissertation· en· W7018529688 on OpenAlexaff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2012
Typedissertation
Languageen
FieldEngineering
TopicAdvanced Numerical Methods in Computational Mathematics
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsFinite difference methodFinite element methodFinite differencePartial differential equationCartesian coordinate systemBoundary value problemNumerical analysisComputational mechanicsFluid mechanicsFocus (optics)
DOInot available

Abstract

fetched live from OpenAlex

A cell-centred finite difference (CCFD) method for unstructured mesh topology is proposed and applied to model partial differential equations (PDEs) governing fluid flow and solid mechanics phenomena. The numerical method implements a finite difference approximation at cell centroids by taking differencing points along orthogonal Cartesian axes localized within each cell. The predominant advantage of this method is that it can be applied to arbitrary mesh topologies, including structured, unstructured and hybrid meshes. Either a direct or iterative approach is used to solve the system of equations developed by the proposed method. The numerical method is designed to solve a variety of physical phenomena governed by PDEs, such as electrostatic potential in electromagnetic fields, stress and strain in structural mechanics and wave phenomena in physics. The focus of the thesis research is to investigate the application of this methodology in heat transfer and fluid mechanics problems. This new finite difference methodology is applied to typical "benchmark" problems in such fields, covering the representative in different types of PDEs with initial and boundary conditions. Solutions obtained are compared to exact solutions if available from analytical methods or to the results from other reliable numerical simulations.

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 score0.429
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.080
GPT teacher head0.293
Teacher spread0.214 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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