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ADVECTION AND DIFFUSION SIMULATIONS USING LAGRANGIAN BLOCKS

2012· article· en· W1965245453 on OpenAlexaff
Vincent H. Chu, Wihel Altai

Bibliographic record

VenueComputational Thermal Sciences An International Journal · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsAdvectionComputationBlock (permutation group theory)DiffusionMoment (physics)LagrangianApplied mathematicsConvection–diffusion equationComputer simulationMechanicsMathematicsStatistical physicsPhysicsClassical mechanicsAlgorithmGeometryThermodynamics

Abstract

fetched live from OpenAlex

The Lagrangian block advection and diffusion is developed as an alternative numerical procedure to the solution of the transport equation. The blocks are the computational elements, which are defined by the zero, first, and second moments of the blocks. The centers of mass of the blocks move with the advection velocity. The second moment of the blocks increases at a rate proportional to the diffusivity. The accuracy of the simulations by the Lagrangian block method is assessed by comparing the block simulation with an exact solution of the advection-and-diffusion equation. Unlike most numerical methods, the error associated with the Lagrangian block method is small and is not cumulative even when a very coarse block size is employed for the computation. False numerical diffusion error is totally avoidable when Lagrangian blocks are used to do the computation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.306
Teacher spread0.274 · 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 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
Published2012
Admission routes1
Has abstractyes

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